In [2]:
## How to predict cancer (cancer data) using a keras deep learning model
def Snippet_340():
print()
print(format('How to predict cancer (cancer data) using a keras deep learning model','*^88'))
import warnings
warnings.filterwarnings("ignore")
# load libraries
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.optimizers import SGD, RMSprop, Adam
from keras.regularizers import l2
import matplotlib.pyplot as plt
import time
start_time = time.time()
# set parameters
VALIDATION_SPLIT = 0.25
VERBOSE = 1
BATCH_SIZE = 32
# load Dataset
iris = datasets.load_breast_cancer()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
# Preprocess The X Data By Scaling
sc = StandardScaler(with_mean=True, with_std=True)
sc.fit(X_train)
# Apply the scaler to the X training data
X_train_std = sc.transform(X_train)
# Apply the SAME scaler to the X test data
X_test_std = sc.transform(X_test)
# ---------------------------------
# setup a deep learning model
# ---------------------------------
accuracy = []
for OPTIMIZER in [SGD(), RMSprop(), Adam()]:
for NB_EPOCH in [50,100,200]:
for N_Units_in_Multiple_Layers in [64, 128, 256]:
model = Sequential()
model.add(Dense(units = N_Units_in_Multiple_Layers, input_shape=(X_train.shape[1],),
kernel_regularizer=l2()))
model.add(Activation('relu'))
model.add(Dense(units = N_Units_in_Multiple_Layers, kernel_regularizer=l2()))
model.add(Activation('relu'))
model.add(Dense(units = 1))
model.add(Activation('sigmoid'))
model.summary()
model.compile(loss='binary_crossentropy', optimizer=OPTIMIZER, metrics=['accuracy'])
model.fit(X_train_std, y_train, batch_size=BATCH_SIZE, epochs=NB_EPOCH,
verbose=VERBOSE, validation_split=VALIDATION_SPLIT)
score = model.evaluate(X_test_std, y_test, verbose=VERBOSE)
print()
print('Optimizers: ', OPTIMIZER)
print('Epoch Sizes: ', NB_EPOCH)
print('Neurons or Units: ', N_Units_in_Multiple_Layers)
print(model.metrics_names); print(score)
print("Test score:", score[0])
print('Test accuracy:', score[1])
accuracy.append(score[1])
print()
print(accuracy)
y = accuracy; N = len(y); x = range(N); width = 1./1.5;
plt.ylim(0.8,1.0)
plt.bar(x,y,width); plt.show()
print()
print("Execution Time %s seconds: " % (time.time() - start_time))
Snippet_340()
*********How to predict cancer (cancer data) using a keras deep learning model********** Model: "sequential_28" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_82 (Dense) (None, 64) 1984 _________________________________________________________________ activation_82 (Activation) (None, 64) 0 _________________________________________________________________ dense_83 (Dense) (None, 64) 4160 _________________________________________________________________ activation_83 (Activation) (None, 64) 0 _________________________________________________________________ dense_84 (Dense) (None, 1) 65 _________________________________________________________________ activation_84 (Activation) (None, 1) 0 ================================================================= Total params: 6,209 Trainable params: 6,209 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/50 298/298 [==============================] - 0s 462us/step - loss: 1.6965 - accuracy: 0.6913 - val_loss: 1.6399 - val_accuracy: 0.8200 Epoch 2/50 298/298 [==============================] - 0s 48us/step - loss: 1.6111 - accuracy: 0.8188 - val_loss: 1.5689 - val_accuracy: 0.9000 Epoch 3/50 298/298 [==============================] - 0s 48us/step - loss: 1.5471 - accuracy: 0.8826 - val_loss: 1.5150 - val_accuracy: 0.9200 Epoch 4/50 298/298 [==============================] - 0s 46us/step - loss: 1.4969 - accuracy: 0.9027 - val_loss: 1.4720 - val_accuracy: 0.9200 Epoch 5/50 298/298 [==============================] - 0s 45us/step - loss: 1.4557 - accuracy: 0.9195 - val_loss: 1.4360 - val_accuracy: 0.9200 Epoch 6/50 298/298 [==============================] - 0s 51us/step - loss: 1.4201 - accuracy: 0.9195 - val_loss: 1.4063 - val_accuracy: 0.9200 Epoch 7/50 298/298 [==============================] - 0s 47us/step - loss: 1.3900 - accuracy: 0.9295 - val_loss: 1.3811 - val_accuracy: 0.9200 Epoch 8/50 298/298 [==============================] - 0s 47us/step - loss: 1.3635 - accuracy: 0.9396 - val_loss: 1.3593 - val_accuracy: 0.9200 Epoch 9/50 298/298 [==============================] - 0s 50us/step - loss: 1.3404 - accuracy: 0.9396 - val_loss: 1.3397 - val_accuracy: 0.9200 Epoch 10/50 298/298 [==============================] - 0s 49us/step - loss: 1.3195 - accuracy: 0.9396 - val_loss: 1.3225 - val_accuracy: 0.9200 Epoch 11/50 298/298 [==============================] - 0s 48us/step - loss: 1.3010 - accuracy: 0.9430 - val_loss: 1.3077 - val_accuracy: 0.9200 Epoch 12/50 298/298 [==============================] - 0s 48us/step - loss: 1.2848 - accuracy: 0.9430 - val_loss: 1.2941 - val_accuracy: 0.9200 Epoch 13/50 298/298 [==============================] - 0s 49us/step - loss: 1.2696 - accuracy: 0.9463 - val_loss: 1.2812 - val_accuracy: 0.9200 Epoch 14/50 298/298 [==============================] - 0s 48us/step - loss: 1.2551 - accuracy: 0.9463 - val_loss: 1.2699 - val_accuracy: 0.9200 Epoch 15/50 298/298 [==============================] - 0s 45us/step - loss: 1.2420 - accuracy: 0.9497 - val_loss: 1.2592 - val_accuracy: 0.9200 Epoch 16/50 298/298 [==============================] - 0s 45us/step - loss: 1.2296 - accuracy: 0.9497 - val_loss: 1.2491 - val_accuracy: 0.9200 Epoch 17/50 298/298 [==============================] - 0s 48us/step - loss: 1.2181 - accuracy: 0.9530 - val_loss: 1.2400 - val_accuracy: 0.9200 Epoch 18/50 298/298 [==============================] - 0s 48us/step - loss: 1.2073 - accuracy: 0.9530 - val_loss: 1.2318 - val_accuracy: 0.9200 Epoch 19/50 298/298 [==============================] - 0s 49us/step - loss: 1.1977 - accuracy: 0.9530 - val_loss: 1.2235 - val_accuracy: 0.9200 Epoch 20/50 298/298 [==============================] - 0s 42us/step - loss: 1.1879 - accuracy: 0.9564 - val_loss: 1.2155 - val_accuracy: 0.9200 Epoch 21/50 298/298 [==============================] - 0s 47us/step - loss: 1.1786 - accuracy: 0.9597 - val_loss: 1.2078 - val_accuracy: 0.9200 Epoch 22/50 298/298 [==============================] - 0s 49us/step - loss: 1.1696 - accuracy: 0.9564 - val_loss: 1.2011 - val_accuracy: 0.9200 Epoch 23/50 298/298 [==============================] - 0s 49us/step - loss: 1.1610 - accuracy: 0.9564 - val_loss: 1.1943 - val_accuracy: 0.9200 Epoch 24/50 298/298 [==============================] - 0s 49us/step - loss: 1.1529 - accuracy: 0.9597 - val_loss: 1.1873 - val_accuracy: 0.9200 Epoch 25/50 298/298 [==============================] - 0s 47us/step - loss: 1.1448 - accuracy: 0.9631 - val_loss: 1.1809 - val_accuracy: 0.9200 Epoch 26/50 298/298 [==============================] - 0s 47us/step - loss: 1.1371 - accuracy: 0.9631 - val_loss: 1.1745 - val_accuracy: 0.9200 Epoch 27/50 298/298 [==============================] - 0s 50us/step - loss: 1.1297 - accuracy: 0.9631 - val_loss: 1.1686 - val_accuracy: 0.9200 Epoch 28/50 298/298 [==============================] - 0s 47us/step - loss: 1.1226 - accuracy: 0.9664 - val_loss: 1.1626 - val_accuracy: 0.9200 Epoch 29/50 298/298 [==============================] - 0s 46us/step - loss: 1.1155 - accuracy: 0.9664 - val_loss: 1.1568 - val_accuracy: 0.9200 Epoch 30/50 298/298 [==============================] - 0s 43us/step - loss: 1.1087 - accuracy: 0.9664 - val_loss: 1.1513 - val_accuracy: 0.9200 Epoch 31/50 298/298 [==============================] - 0s 51us/step - loss: 1.1021 - accuracy: 0.9664 - val_loss: 1.1460 - val_accuracy: 0.9200 Epoch 32/50 298/298 [==============================] - 0s 51us/step - loss: 1.0958 - accuracy: 0.9664 - val_loss: 1.1407 - val_accuracy: 0.9200 Epoch 33/50 298/298 [==============================] - 0s 51us/step - loss: 1.0894 - accuracy: 0.9664 - val_loss: 1.1357 - val_accuracy: 0.9200 Epoch 34/50 298/298 [==============================] - 0s 47us/step - loss: 1.0833 - accuracy: 0.9698 - val_loss: 1.1306 - val_accuracy: 0.9200 Epoch 35/50 298/298 [==============================] - 0s 50us/step - loss: 1.0771 - accuracy: 0.9698 - val_loss: 1.1257 - val_accuracy: 0.9200 Epoch 36/50 298/298 [==============================] - 0s 47us/step - loss: 1.0712 - accuracy: 0.9698 - val_loss: 1.1208 - val_accuracy: 0.9200 Epoch 37/50 298/298 [==============================] - 0s 52us/step - loss: 1.0656 - accuracy: 0.9698 - val_loss: 1.1158 - val_accuracy: 0.9200 Epoch 38/50 298/298 [==============================] - 0s 46us/step - loss: 1.0598 - accuracy: 0.9698 - val_loss: 1.1115 - val_accuracy: 0.9200 Epoch 39/50 298/298 [==============================] - 0s 46us/step - loss: 1.0544 - accuracy: 0.9698 - val_loss: 1.1069 - val_accuracy: 0.9200 Epoch 40/50 298/298 [==============================] - 0s 46us/step - loss: 1.0490 - accuracy: 0.9698 - val_loss: 1.1023 - val_accuracy: 0.9200 Epoch 41/50 298/298 [==============================] - 0s 49us/step - loss: 1.0437 - accuracy: 0.9698 - val_loss: 1.0976 - val_accuracy: 0.9200 Epoch 42/50 298/298 [==============================] - 0s 46us/step - loss: 1.0386 - accuracy: 0.9698 - val_loss: 1.0933 - val_accuracy: 0.9200 Epoch 43/50 298/298 [==============================] - 0s 47us/step - loss: 1.0335 - accuracy: 0.9698 - val_loss: 1.0890 - val_accuracy: 0.9200 Epoch 44/50 298/298 [==============================] - 0s 45us/step - loss: 1.0284 - accuracy: 0.9698 - val_loss: 1.0843 - val_accuracy: 0.9200 Epoch 45/50 298/298 [==============================] - 0s 45us/step - loss: 1.0233 - accuracy: 0.9698 - val_loss: 1.0801 - val_accuracy: 0.9200 Epoch 46/50 298/298 [==============================] - 0s 46us/step - loss: 1.0185 - accuracy: 0.9698 - val_loss: 1.0759 - val_accuracy: 0.9200 Epoch 47/50 298/298 [==============================] - 0s 46us/step - loss: 1.0135 - accuracy: 0.9732 - val_loss: 1.0717 - val_accuracy: 0.9200 Epoch 48/50 298/298 [==============================] - 0s 47us/step - loss: 1.0088 - accuracy: 0.9732 - val_loss: 1.0674 - val_accuracy: 0.9200 Epoch 49/50 298/298 [==============================] - 0s 45us/step - loss: 1.0040 - accuracy: 0.9732 - val_loss: 1.0633 - val_accuracy: 0.9200 Epoch 50/50 298/298 [==============================] - 0s 45us/step - loss: 0.9994 - accuracy: 0.9732 - val_loss: 1.0591 - val_accuracy: 0.9200 171/171 [==============================] - 0s 24us/step Optimizers: <keras.optimizers.SGD object at 0x1463bb710> Epoch Sizes: 50 Neurons or Units: 64 ['loss', 'accuracy'] [1.0158185777608415, 0.9590643048286438] Test score: 1.0158185777608415 Test accuracy: 0.9590643048286438 Model: "sequential_29" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_85 (Dense) (None, 128) 3968 _________________________________________________________________ activation_85 (Activation) (None, 128) 0 _________________________________________________________________ dense_86 (Dense) (None, 128) 16512 _________________________________________________________________ activation_86 (Activation) (None, 128) 0 _________________________________________________________________ dense_87 (Dense) (None, 1) 129 _________________________________________________________________ activation_87 (Activation) (None, 1) 0 ================================================================= Total params: 20,609 Trainable params: 20,609 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/50 298/298 [==============================] - 0s 489us/step - loss: 2.3408 - accuracy: 0.8389 - val_loss: 2.2675 - val_accuracy: 0.9400 Epoch 2/50 298/298 [==============================] - 0s 48us/step - loss: 2.2396 - accuracy: 0.9161 - val_loss: 2.1837 - val_accuracy: 0.9500 Epoch 3/50 298/298 [==============================] - 0s 48us/step - loss: 2.1664 - accuracy: 0.9362 - val_loss: 2.1238 - val_accuracy: 0.9500 Epoch 4/50 298/298 [==============================] - 0s 46us/step - loss: 2.1121 - accuracy: 0.9430 - val_loss: 2.0767 - val_accuracy: 0.9500 Epoch 5/50 298/298 [==============================] - 0s 43us/step - loss: 2.0674 - accuracy: 0.9597 - val_loss: 2.0400 - val_accuracy: 0.9400 Epoch 6/50 298/298 [==============================] - 0s 44us/step - loss: 2.0316 - accuracy: 0.9597 - val_loss: 2.0094 - val_accuracy: 0.9300 Epoch 7/50 298/298 [==============================] - 0s 45us/step - loss: 2.0013 - accuracy: 0.9631 - val_loss: 1.9841 - val_accuracy: 0.9300 Epoch 8/50 298/298 [==============================] - 0s 43us/step - loss: 1.9755 - accuracy: 0.9664 - val_loss: 1.9622 - val_accuracy: 0.9300 Epoch 9/50 298/298 [==============================] - 0s 43us/step - loss: 1.9527 - accuracy: 0.9664 - val_loss: 1.9431 - val_accuracy: 0.9300 Epoch 10/50 298/298 [==============================] - 0s 43us/step - loss: 1.9324 - accuracy: 0.9631 - val_loss: 1.9257 - val_accuracy: 0.9300 Epoch 11/50 298/298 [==============================] - 0s 42us/step - loss: 1.9137 - accuracy: 0.9631 - val_loss: 1.9104 - val_accuracy: 0.9300 Epoch 12/50 298/298 [==============================] - 0s 43us/step - loss: 1.8968 - accuracy: 0.9631 - val_loss: 1.8966 - val_accuracy: 0.9300 Epoch 13/50 298/298 [==============================] - 0s 47us/step - loss: 1.8815 - accuracy: 0.9597 - val_loss: 1.8844 - val_accuracy: 0.9300 Epoch 14/50 298/298 [==============================] - 0s 44us/step - loss: 1.8675 - accuracy: 0.9597 - val_loss: 1.8725 - val_accuracy: 0.9300 Epoch 15/50 298/298 [==============================] - 0s 43us/step - loss: 1.8541 - accuracy: 0.9597 - val_loss: 1.8612 - val_accuracy: 0.9300 Epoch 16/50 298/298 [==============================] - 0s 45us/step - loss: 1.8412 - accuracy: 0.9597 - val_loss: 1.8508 - val_accuracy: 0.9300 Epoch 17/50 298/298 [==============================] - 0s 44us/step - loss: 1.8290 - accuracy: 0.9597 - val_loss: 1.8407 - val_accuracy: 0.9300 Epoch 18/50 298/298 [==============================] - 0s 43us/step - loss: 1.8175 - accuracy: 0.9597 - val_loss: 1.8310 - val_accuracy: 0.9300 Epoch 19/50 298/298 [==============================] - 0s 45us/step - loss: 1.8066 - accuracy: 0.9597 - val_loss: 1.8219 - val_accuracy: 0.9300 Epoch 20/50 298/298 [==============================] - 0s 45us/step - loss: 1.7960 - accuracy: 0.9631 - val_loss: 1.8130 - val_accuracy: 0.9300 Epoch 21/50 298/298 [==============================] - 0s 45us/step - loss: 1.7858 - accuracy: 0.9664 - val_loss: 1.8045 - val_accuracy: 0.9300 Epoch 22/50 298/298 [==============================] - 0s 44us/step - loss: 1.7759 - accuracy: 0.9698 - val_loss: 1.7964 - val_accuracy: 0.9300 Epoch 23/50 298/298 [==============================] - 0s 42us/step - loss: 1.7665 - accuracy: 0.9732 - val_loss: 1.7883 - val_accuracy: 0.9300 Epoch 24/50 298/298 [==============================] - 0s 45us/step - loss: 1.7570 - accuracy: 0.9732 - val_loss: 1.7804 - val_accuracy: 0.9300 Epoch 25/50 298/298 [==============================] - 0s 43us/step - loss: 1.7480 - accuracy: 0.9732 - val_loss: 1.7728 - val_accuracy: 0.9300 Epoch 26/50 298/298 [==============================] - 0s 42us/step - loss: 1.7391 - accuracy: 0.9732 - val_loss: 1.7653 - val_accuracy: 0.9300 Epoch 27/50 298/298 [==============================] - 0s 45us/step - loss: 1.7305 - accuracy: 0.9732 - val_loss: 1.7578 - val_accuracy: 0.9300 Epoch 28/50 298/298 [==============================] - 0s 46us/step - loss: 1.7220 - accuracy: 0.9732 - val_loss: 1.7505 - val_accuracy: 0.9300 Epoch 29/50 298/298 [==============================] - 0s 44us/step - loss: 1.7137 - accuracy: 0.9732 - val_loss: 1.7433 - val_accuracy: 0.9300 Epoch 30/50 298/298 [==============================] - 0s 41us/step - loss: 1.7055 - accuracy: 0.9732 - val_loss: 1.7363 - val_accuracy: 0.9300 Epoch 31/50 298/298 [==============================] - 0s 44us/step - loss: 1.6975 - accuracy: 0.9732 - val_loss: 1.7292 - val_accuracy: 0.9300 Epoch 32/50 298/298 [==============================] - 0s 47us/step - loss: 1.6896 - accuracy: 0.9732 - val_loss: 1.7222 - val_accuracy: 0.9300 Epoch 33/50 298/298 [==============================] - 0s 45us/step - loss: 1.6819 - accuracy: 0.9732 - val_loss: 1.7154 - val_accuracy: 0.9300 Epoch 34/50 298/298 [==============================] - 0s 43us/step - loss: 1.6742 - accuracy: 0.9732 - val_loss: 1.7087 - val_accuracy: 0.9300 Epoch 35/50 298/298 [==============================] - 0s 46us/step - loss: 1.6669 - accuracy: 0.9799 - val_loss: 1.7018 - val_accuracy: 0.9300 Epoch 36/50 298/298 [==============================] - 0s 47us/step - loss: 1.6594 - accuracy: 0.9765 - val_loss: 1.6953 - val_accuracy: 0.9300 Epoch 37/50 298/298 [==============================] - 0s 42us/step - loss: 1.6520 - accuracy: 0.9799 - val_loss: 1.6888 - val_accuracy: 0.9300 Epoch 38/50 298/298 [==============================] - 0s 45us/step - loss: 1.6447 - accuracy: 0.9799 - val_loss: 1.6825 - val_accuracy: 0.9300 Epoch 39/50 298/298 [==============================] - 0s 44us/step - loss: 1.6374 - accuracy: 0.9799 - val_loss: 1.6764 - val_accuracy: 0.9300 Epoch 40/50 298/298 [==============================] - 0s 42us/step - loss: 1.6305 - accuracy: 0.9799 - val_loss: 1.6700 - val_accuracy: 0.9300 Epoch 41/50 298/298 [==============================] - 0s 43us/step - loss: 1.6233 - accuracy: 0.9799 - val_loss: 1.6639 - val_accuracy: 0.9300 Epoch 42/50 298/298 [==============================] - 0s 46us/step - loss: 1.6163 - accuracy: 0.9799 - val_loss: 1.6576 - val_accuracy: 0.9300 Epoch 43/50 298/298 [==============================] - 0s 48us/step - loss: 1.6095 - accuracy: 0.9799 - val_loss: 1.6515 - val_accuracy: 0.9300 Epoch 44/50 298/298 [==============================] - 0s 44us/step - loss: 1.6026 - accuracy: 0.9799 - val_loss: 1.6454 - val_accuracy: 0.9300 Epoch 45/50 298/298 [==============================] - 0s 43us/step - loss: 1.5958 - accuracy: 0.9799 - val_loss: 1.6394 - val_accuracy: 0.9300 Epoch 46/50 298/298 [==============================] - 0s 43us/step - loss: 1.5891 - accuracy: 0.9799 - val_loss: 1.6334 - val_accuracy: 0.9300 Epoch 47/50 298/298 [==============================] - 0s 45us/step - loss: 1.5825 - accuracy: 0.9799 - val_loss: 1.6274 - val_accuracy: 0.9300 Epoch 48/50 298/298 [==============================] - 0s 42us/step - loss: 1.5759 - accuracy: 0.9799 - val_loss: 1.6214 - val_accuracy: 0.9300 Epoch 49/50 298/298 [==============================] - 0s 43us/step - loss: 1.5694 - accuracy: 0.9799 - val_loss: 1.6156 - val_accuracy: 0.9300 Epoch 50/50 298/298 [==============================] - 0s 46us/step - loss: 1.5628 - accuracy: 0.9832 - val_loss: 1.6097 - val_accuracy: 0.9300 171/171 [==============================] - 0s 24us/step Optimizers: <keras.optimizers.SGD object at 0x1463bb710> Epoch Sizes: 50 Neurons or Units: 128 ['loss', 'accuracy'] [1.5733462906720346, 0.9649122953414917] Test score: 1.5733462906720346 Test accuracy: 0.9649122953414917 Model: "sequential_30" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_88 (Dense) (None, 256) 7936 _________________________________________________________________ activation_88 (Activation) (None, 256) 0 _________________________________________________________________ dense_89 (Dense) (None, 256) 65792 _________________________________________________________________ activation_89 (Activation) (None, 256) 0 _________________________________________________________________ dense_90 (Dense) (None, 1) 257 _________________________________________________________________ activation_90 (Activation) (None, 1) 0 ================================================================= Total params: 73,985 Trainable params: 73,985 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/50 298/298 [==============================] - 0s 458us/step - loss: 3.7174 - accuracy: 0.7785 - val_loss: 3.6349 - val_accuracy: 0.9200 Epoch 2/50 298/298 [==============================] - 0s 55us/step - loss: 3.6084 - accuracy: 0.8926 - val_loss: 3.5502 - val_accuracy: 0.9300 Epoch 3/50 298/298 [==============================] - 0s 54us/step - loss: 3.5281 - accuracy: 0.9362 - val_loss: 3.4853 - val_accuracy: 0.9300 Epoch 4/50 298/298 [==============================] - 0s 54us/step - loss: 3.4651 - accuracy: 0.9430 - val_loss: 3.4329 - val_accuracy: 0.9200 Epoch 5/50 298/298 [==============================] - 0s 54us/step - loss: 3.4128 - accuracy: 0.9463 - val_loss: 3.3903 - val_accuracy: 0.9200 Epoch 6/50 298/298 [==============================] - 0s 54us/step - loss: 3.3695 - accuracy: 0.9463 - val_loss: 3.3542 - val_accuracy: 0.9200 Epoch 7/50 298/298 [==============================] - 0s 54us/step - loss: 3.3319 - accuracy: 0.9497 - val_loss: 3.3217 - val_accuracy: 0.9200 Epoch 8/50 298/298 [==============================] - 0s 57us/step - loss: 3.2985 - accuracy: 0.9530 - val_loss: 3.2945 - val_accuracy: 0.9200 Epoch 9/50 298/298 [==============================] - 0s 56us/step - loss: 3.2690 - accuracy: 0.9530 - val_loss: 3.2694 - val_accuracy: 0.9200 Epoch 10/50 298/298 [==============================] - 0s 56us/step - loss: 3.2420 - accuracy: 0.9564 - val_loss: 3.2471 - val_accuracy: 0.9200 Epoch 11/50 298/298 [==============================] - 0s 57us/step - loss: 3.2173 - accuracy: 0.9530 - val_loss: 3.2260 - val_accuracy: 0.9200 Epoch 12/50 298/298 [==============================] - 0s 54us/step - loss: 3.1941 - accuracy: 0.9564 - val_loss: 3.2065 - val_accuracy: 0.9200 Epoch 13/50 298/298 [==============================] - 0s 53us/step - loss: 3.1726 - accuracy: 0.9597 - val_loss: 3.1886 - val_accuracy: 0.9200 Epoch 14/50 298/298 [==============================] - 0s 54us/step - loss: 3.1525 - accuracy: 0.9597 - val_loss: 3.1715 - val_accuracy: 0.9200 Epoch 15/50 298/298 [==============================] - 0s 57us/step - loss: 3.1334 - accuracy: 0.9597 - val_loss: 3.1549 - val_accuracy: 0.9200 Epoch 16/50 298/298 [==============================] - 0s 54us/step - loss: 3.1149 - accuracy: 0.9597 - val_loss: 3.1389 - val_accuracy: 0.9200 Epoch 17/50 298/298 [==============================] - 0s 55us/step - loss: 3.0971 - accuracy: 0.9631 - val_loss: 3.1238 - val_accuracy: 0.9200 Epoch 18/50 298/298 [==============================] - 0s 55us/step - loss: 3.0802 - accuracy: 0.9631 - val_loss: 3.1091 - val_accuracy: 0.9200 Epoch 19/50 298/298 [==============================] - 0s 55us/step - loss: 3.0636 - accuracy: 0.9664 - val_loss: 3.0948 - val_accuracy: 0.9200 Epoch 20/50 298/298 [==============================] - 0s 55us/step - loss: 3.0476 - accuracy: 0.9664 - val_loss: 3.0809 - val_accuracy: 0.9200 Epoch 21/50 298/298 [==============================] - 0s 56us/step - loss: 3.0324 - accuracy: 0.9664 - val_loss: 3.0669 - val_accuracy: 0.9200 Epoch 22/50 298/298 [==============================] - 0s 56us/step - loss: 3.0171 - accuracy: 0.9664 - val_loss: 3.0536 - val_accuracy: 0.9200 Epoch 23/50 298/298 [==============================] - 0s 56us/step - loss: 3.0023 - accuracy: 0.9664 - val_loss: 3.0406 - val_accuracy: 0.9200 Epoch 24/50 298/298 [==============================] - 0s 56us/step - loss: 2.9878 - accuracy: 0.9664 - val_loss: 3.0278 - val_accuracy: 0.9200 Epoch 25/50 298/298 [==============================] - 0s 57us/step - loss: 2.9736 - accuracy: 0.9664 - val_loss: 3.0149 - val_accuracy: 0.9200 Epoch 26/50 298/298 [==============================] - 0s 54us/step - loss: 2.9595 - accuracy: 0.9698 - val_loss: 3.0026 - val_accuracy: 0.9200 Epoch 27/50 298/298 [==============================] - 0s 56us/step - loss: 2.9458 - accuracy: 0.9698 - val_loss: 2.9900 - val_accuracy: 0.9200 Epoch 28/50 298/298 [==============================] - 0s 58us/step - loss: 2.9322 - accuracy: 0.9698 - val_loss: 2.9779 - val_accuracy: 0.9200 Epoch 29/50 298/298 [==============================] - 0s 55us/step - loss: 2.9187 - accuracy: 0.9698 - val_loss: 2.9657 - val_accuracy: 0.9200 Epoch 30/50 298/298 [==============================] - 0s 58us/step - loss: 2.9056 - accuracy: 0.9698 - val_loss: 2.9539 - val_accuracy: 0.9200 Epoch 31/50 298/298 [==============================] - 0s 61us/step - loss: 2.8926 - accuracy: 0.9732 - val_loss: 2.9420 - val_accuracy: 0.9200 Epoch 32/50 298/298 [==============================] - 0s 62us/step - loss: 2.8796 - accuracy: 0.9732 - val_loss: 2.9302 - val_accuracy: 0.9200 Epoch 33/50 298/298 [==============================] - 0s 58us/step - loss: 2.8670 - accuracy: 0.9732 - val_loss: 2.9185 - val_accuracy: 0.9200 Epoch 34/50 298/298 [==============================] - 0s 56us/step - loss: 2.8545 - accuracy: 0.9732 - val_loss: 2.9068 - val_accuracy: 0.9200 Epoch 35/50 298/298 [==============================] - 0s 62us/step - loss: 2.8420 - accuracy: 0.9732 - val_loss: 2.8954 - val_accuracy: 0.9200 Epoch 36/50 298/298 [==============================] - 0s 61us/step - loss: 2.8297 - accuracy: 0.9732 - val_loss: 2.8840 - val_accuracy: 0.9200 Epoch 37/50 298/298 [==============================] - 0s 57us/step - loss: 2.8176 - accuracy: 0.9732 - val_loss: 2.8727 - val_accuracy: 0.9200 Epoch 38/50 298/298 [==============================] - 0s 58us/step - loss: 2.8055 - accuracy: 0.9732 - val_loss: 2.8616 - val_accuracy: 0.9200 Epoch 39/50 298/298 [==============================] - 0s 59us/step - loss: 2.7935 - accuracy: 0.9732 - val_loss: 2.8506 - val_accuracy: 0.9200 Epoch 40/50 298/298 [==============================] - 0s 57us/step - loss: 2.7815 - accuracy: 0.9732 - val_loss: 2.8394 - val_accuracy: 0.9200 Epoch 41/50 298/298 [==============================] - 0s 56us/step - loss: 2.7698 - accuracy: 0.9732 - val_loss: 2.8286 - val_accuracy: 0.9200 Epoch 42/50 298/298 [==============================] - 0s 60us/step - loss: 2.7581 - accuracy: 0.9732 - val_loss: 2.8176 - val_accuracy: 0.9200 Epoch 43/50 298/298 [==============================] - 0s 55us/step - loss: 2.7465 - accuracy: 0.9732 - val_loss: 2.8068 - val_accuracy: 0.9200 Epoch 44/50 298/298 [==============================] - 0s 54us/step - loss: 2.7349 - accuracy: 0.9765 - val_loss: 2.7960 - val_accuracy: 0.9200 Epoch 45/50 298/298 [==============================] - 0s 55us/step - loss: 2.7234 - accuracy: 0.9765 - val_loss: 2.7852 - val_accuracy: 0.9200 Epoch 46/50 298/298 [==============================] - 0s 54us/step - loss: 2.7120 - accuracy: 0.9765 - val_loss: 2.7744 - val_accuracy: 0.9300 Epoch 47/50 298/298 [==============================] - 0s 56us/step - loss: 2.7008 - accuracy: 0.9765 - val_loss: 2.7637 - val_accuracy: 0.9300 Epoch 48/50 298/298 [==============================] - 0s 57us/step - loss: 2.6896 - accuracy: 0.9765 - val_loss: 2.7531 - val_accuracy: 0.9300 Epoch 49/50 298/298 [==============================] - 0s 62us/step - loss: 2.6785 - accuracy: 0.9765 - val_loss: 2.7426 - val_accuracy: 0.9300 Epoch 50/50 298/298 [==============================] - 0s 67us/step - loss: 2.6674 - accuracy: 0.9765 - val_loss: 2.7322 - val_accuracy: 0.9300 171/171 [==============================] - 0s 33us/step Optimizers: <keras.optimizers.SGD object at 0x1463bb710> Epoch Sizes: 50 Neurons or Units: 256 ['loss', 'accuracy'] [2.6717954992550856, 0.9590643048286438] Test score: 2.6717954992550856 Test accuracy: 0.9590643048286438 Model: "sequential_31" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_91 (Dense) (None, 64) 1984 _________________________________________________________________ activation_91 (Activation) (None, 64) 0 _________________________________________________________________ dense_92 (Dense) (None, 64) 4160 _________________________________________________________________ activation_92 (Activation) (None, 64) 0 _________________________________________________________________ dense_93 (Dense) (None, 1) 65 _________________________________________________________________ activation_93 (Activation) (None, 1) 0 ================================================================= Total params: 6,209 Trainable params: 6,209 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/100 298/298 [==============================] - 0s 439us/step - loss: 1.8028 - accuracy: 0.4765 - val_loss: 1.7215 - val_accuracy: 0.6500 Epoch 2/100 298/298 [==============================] - 0s 41us/step - loss: 1.6784 - accuracy: 0.7148 - val_loss: 1.6192 - val_accuracy: 0.8200 Epoch 3/100 298/298 [==============================] - 0s 44us/step - loss: 1.5882 - accuracy: 0.8423 - val_loss: 1.5458 - val_accuracy: 0.8900 Epoch 4/100 298/298 [==============================] - 0s 41us/step - loss: 1.5220 - accuracy: 0.9262 - val_loss: 1.4902 - val_accuracy: 0.9000 Epoch 5/100 298/298 [==============================] - 0s 43us/step - loss: 1.4694 - accuracy: 0.9396 - val_loss: 1.4472 - val_accuracy: 0.9000 Epoch 6/100 298/298 [==============================] - 0s 44us/step - loss: 1.4277 - accuracy: 0.9430 - val_loss: 1.4123 - val_accuracy: 0.9100 Epoch 7/100 298/298 [==============================] - 0s 43us/step - loss: 1.3932 - accuracy: 0.9430 - val_loss: 1.3833 - val_accuracy: 0.9200 Epoch 8/100 298/298 [==============================] - 0s 40us/step - loss: 1.3636 - accuracy: 0.9463 - val_loss: 1.3596 - val_accuracy: 0.9200 Epoch 9/100 298/298 [==============================] - 0s 43us/step - loss: 1.3387 - accuracy: 0.9530 - val_loss: 1.3392 - val_accuracy: 0.9200 Epoch 10/100 298/298 [==============================] - 0s 43us/step - loss: 1.3167 - accuracy: 0.9597 - val_loss: 1.3212 - val_accuracy: 0.9200 Epoch 11/100 298/298 [==============================] - 0s 41us/step - loss: 1.2972 - accuracy: 0.9597 - val_loss: 1.3055 - val_accuracy: 0.9100 Epoch 12/100 298/298 [==============================] - 0s 44us/step - loss: 1.2794 - accuracy: 0.9597 - val_loss: 1.2916 - val_accuracy: 0.9100 Epoch 13/100 298/298 [==============================] - 0s 41us/step - loss: 1.2636 - accuracy: 0.9597 - val_loss: 1.2798 - val_accuracy: 0.9100 Epoch 14/100 298/298 [==============================] - 0s 41us/step - loss: 1.2492 - accuracy: 0.9698 - val_loss: 1.2685 - val_accuracy: 0.9100 Epoch 15/100 298/298 [==============================] - 0s 41us/step - loss: 1.2359 - accuracy: 0.9698 - val_loss: 1.2579 - val_accuracy: 0.9100 Epoch 16/100 298/298 [==============================] - 0s 42us/step - loss: 1.2237 - accuracy: 0.9698 - val_loss: 1.2485 - val_accuracy: 0.9100 Epoch 17/100 298/298 [==============================] - 0s 46us/step - loss: 1.2122 - accuracy: 0.9732 - val_loss: 1.2396 - val_accuracy: 0.9100 Epoch 18/100 298/298 [==============================] - 0s 42us/step - loss: 1.2014 - accuracy: 0.9732 - val_loss: 1.2314 - val_accuracy: 0.9100 Epoch 19/100 298/298 [==============================] - 0s 42us/step - loss: 1.1914 - accuracy: 0.9732 - val_loss: 1.2236 - val_accuracy: 0.9100 Epoch 20/100 298/298 [==============================] - 0s 42us/step - loss: 1.1819 - accuracy: 0.9765 - val_loss: 1.2156 - val_accuracy: 0.9200 Epoch 21/100 298/298 [==============================] - 0s 46us/step - loss: 1.1725 - accuracy: 0.9732 - val_loss: 1.2085 - val_accuracy: 0.9200 Epoch 22/100 298/298 [==============================] - 0s 51us/step - loss: 1.1638 - accuracy: 0.9732 - val_loss: 1.2018 - val_accuracy: 0.9200 Epoch 23/100 298/298 [==============================] - 0s 46us/step - loss: 1.1553 - accuracy: 0.9732 - val_loss: 1.1957 - val_accuracy: 0.9200 Epoch 24/100 298/298 [==============================] - 0s 47us/step - loss: 1.1478 - accuracy: 0.9732 - val_loss: 1.1896 - val_accuracy: 0.9200 Epoch 25/100 298/298 [==============================] - 0s 49us/step - loss: 1.1401 - accuracy: 0.9732 - val_loss: 1.1837 - val_accuracy: 0.9200 Epoch 26/100 298/298 [==============================] - 0s 45us/step - loss: 1.1328 - accuracy: 0.9732 - val_loss: 1.1782 - val_accuracy: 0.9200 Epoch 27/100 298/298 [==============================] - 0s 46us/step - loss: 1.1258 - accuracy: 0.9732 - val_loss: 1.1728 - val_accuracy: 0.9200 Epoch 28/100 298/298 [==============================] - 0s 44us/step - loss: 1.1189 - accuracy: 0.9732 - val_loss: 1.1677 - val_accuracy: 0.9200 Epoch 29/100 298/298 [==============================] - 0s 43us/step - loss: 1.1124 - accuracy: 0.9732 - val_loss: 1.1624 - val_accuracy: 0.9200 Epoch 30/100 298/298 [==============================] - 0s 44us/step - loss: 1.1057 - accuracy: 0.9732 - val_loss: 1.1569 - val_accuracy: 0.9200 Epoch 31/100 298/298 [==============================] - 0s 43us/step - loss: 1.0995 - accuracy: 0.9732 - val_loss: 1.1514 - val_accuracy: 0.9200 Epoch 32/100 298/298 [==============================] - 0s 42us/step - loss: 1.0933 - accuracy: 0.9732 - val_loss: 1.1468 - val_accuracy: 0.9200 Epoch 33/100 298/298 [==============================] - 0s 46us/step - loss: 1.0875 - accuracy: 0.9732 - val_loss: 1.1422 - val_accuracy: 0.9200 Epoch 34/100 298/298 [==============================] - 0s 44us/step - loss: 1.0817 - accuracy: 0.9732 - val_loss: 1.1374 - val_accuracy: 0.9200 Epoch 35/100 298/298 [==============================] - 0s 47us/step - loss: 1.0760 - accuracy: 0.9732 - val_loss: 1.1330 - val_accuracy: 0.9200 Epoch 36/100 298/298 [==============================] - 0s 43us/step - loss: 1.0703 - accuracy: 0.9732 - val_loss: 1.1286 - val_accuracy: 0.9200 Epoch 37/100 298/298 [==============================] - 0s 42us/step - loss: 1.0649 - accuracy: 0.9732 - val_loss: 1.1244 - val_accuracy: 0.9200 Epoch 38/100 298/298 [==============================] - 0s 41us/step - loss: 1.0595 - accuracy: 0.9732 - val_loss: 1.1209 - val_accuracy: 0.9200 Epoch 39/100 298/298 [==============================] - 0s 43us/step - loss: 1.0542 - accuracy: 0.9732 - val_loss: 1.1166 - val_accuracy: 0.9200 Epoch 40/100 298/298 [==============================] - 0s 45us/step - loss: 1.0490 - accuracy: 0.9765 - val_loss: 1.1124 - val_accuracy: 0.9200 Epoch 41/100 298/298 [==============================] - 0s 46us/step - loss: 1.0439 - accuracy: 0.9765 - val_loss: 1.1082 - val_accuracy: 0.9200 Epoch 42/100 298/298 [==============================] - 0s 44us/step - loss: 1.0389 - accuracy: 0.9765 - val_loss: 1.1040 - val_accuracy: 0.9200 Epoch 43/100 298/298 [==============================] - 0s 41us/step - loss: 1.0339 - accuracy: 0.9765 - val_loss: 1.0999 - val_accuracy: 0.9200 Epoch 44/100 298/298 [==============================] - 0s 43us/step - loss: 1.0291 - accuracy: 0.9765 - val_loss: 1.0959 - val_accuracy: 0.9200 Epoch 45/100 298/298 [==============================] - 0s 42us/step - loss: 1.0243 - accuracy: 0.9765 - val_loss: 1.0921 - val_accuracy: 0.9200 Epoch 46/100 298/298 [==============================] - 0s 42us/step - loss: 1.0196 - accuracy: 0.9765 - val_loss: 1.0881 - val_accuracy: 0.9200 Epoch 47/100 298/298 [==============================] - 0s 43us/step - loss: 1.0149 - accuracy: 0.9765 - val_loss: 1.0839 - val_accuracy: 0.9200 Epoch 48/100 298/298 [==============================] - 0s 40us/step - loss: 1.0103 - accuracy: 0.9765 - val_loss: 1.0801 - val_accuracy: 0.9200 Epoch 49/100 298/298 [==============================] - 0s 40us/step - loss: 1.0057 - accuracy: 0.9765 - val_loss: 1.0762 - val_accuracy: 0.9200 Epoch 50/100 298/298 [==============================] - 0s 40us/step - loss: 1.0013 - accuracy: 0.9765 - val_loss: 1.0721 - val_accuracy: 0.9200 Epoch 51/100 298/298 [==============================] - 0s 40us/step - loss: 0.9968 - accuracy: 0.9799 - val_loss: 1.0677 - val_accuracy: 0.9200 Epoch 52/100 298/298 [==============================] - 0s 39us/step - loss: 0.9924 - accuracy: 0.9799 - val_loss: 1.0638 - val_accuracy: 0.9200 Epoch 53/100 298/298 [==============================] - 0s 39us/step - loss: 0.9880 - accuracy: 0.9799 - val_loss: 1.0598 - val_accuracy: 0.9200 Epoch 54/100 298/298 [==============================] - 0s 41us/step - loss: 0.9837 - accuracy: 0.9799 - val_loss: 1.0559 - val_accuracy: 0.9200 Epoch 55/100 298/298 [==============================] - 0s 40us/step - loss: 0.9795 - accuracy: 0.9799 - val_loss: 1.0524 - val_accuracy: 0.9200 Epoch 56/100 298/298 [==============================] - 0s 39us/step - loss: 0.9751 - accuracy: 0.9799 - val_loss: 1.0488 - val_accuracy: 0.9200 Epoch 57/100 298/298 [==============================] - 0s 43us/step - loss: 0.9710 - accuracy: 0.9799 - val_loss: 1.0451 - val_accuracy: 0.9200 Epoch 58/100 298/298 [==============================] - 0s 44us/step - loss: 0.9669 - accuracy: 0.9799 - val_loss: 1.0416 - val_accuracy: 0.9200 Epoch 59/100 298/298 [==============================] - 0s 43us/step - loss: 0.9627 - accuracy: 0.9799 - val_loss: 1.0377 - val_accuracy: 0.9200 Epoch 60/100 298/298 [==============================] - 0s 43us/step - loss: 0.9586 - accuracy: 0.9799 - val_loss: 1.0342 - val_accuracy: 0.9200 Epoch 61/100 298/298 [==============================] - 0s 41us/step - loss: 0.9546 - accuracy: 0.9799 - val_loss: 1.0308 - val_accuracy: 0.9200 Epoch 62/100 298/298 [==============================] - 0s 43us/step - loss: 0.9506 - accuracy: 0.9799 - val_loss: 1.0269 - val_accuracy: 0.9200 Epoch 63/100 298/298 [==============================] - 0s 43us/step - loss: 0.9466 - accuracy: 0.9799 - val_loss: 1.0235 - val_accuracy: 0.9200 Epoch 64/100 298/298 [==============================] - 0s 43us/step - loss: 0.9427 - accuracy: 0.9799 - val_loss: 1.0201 - val_accuracy: 0.9300 Epoch 65/100 298/298 [==============================] - 0s 41us/step - loss: 0.9388 - accuracy: 0.9832 - val_loss: 1.0165 - val_accuracy: 0.9300 Epoch 66/100 298/298 [==============================] - 0s 40us/step - loss: 0.9349 - accuracy: 0.9832 - val_loss: 1.0132 - val_accuracy: 0.9300 Epoch 67/100 298/298 [==============================] - 0s 40us/step - loss: 0.9311 - accuracy: 0.9832 - val_loss: 1.0097 - val_accuracy: 0.9300 Epoch 68/100 298/298 [==============================] - 0s 40us/step - loss: 0.9273 - accuracy: 0.9832 - val_loss: 1.0062 - val_accuracy: 0.9300 Epoch 69/100 298/298 [==============================] - 0s 42us/step - loss: 0.9234 - accuracy: 0.9832 - val_loss: 1.0028 - val_accuracy: 0.9300 Epoch 70/100 298/298 [==============================] - 0s 41us/step - loss: 0.9196 - accuracy: 0.9832 - val_loss: 0.9992 - val_accuracy: 0.9300 Epoch 71/100 298/298 [==============================] - 0s 76us/step - loss: 0.9158 - accuracy: 0.9832 - val_loss: 0.9962 - val_accuracy: 0.9300 Epoch 72/100 298/298 [==============================] - 0s 73us/step - loss: 0.9121 - accuracy: 0.9832 - val_loss: 0.9925 - val_accuracy: 0.9300 Epoch 73/100 298/298 [==============================] - 0s 61us/step - loss: 0.9084 - accuracy: 0.9832 - val_loss: 0.9891 - val_accuracy: 0.9300 Epoch 74/100 298/298 [==============================] - 0s 43us/step - loss: 0.9047 - accuracy: 0.9832 - val_loss: 0.9858 - val_accuracy: 0.9300 Epoch 75/100 298/298 [==============================] - 0s 42us/step - loss: 0.9011 - accuracy: 0.9832 - val_loss: 0.9824 - val_accuracy: 0.9300 Epoch 76/100 298/298 [==============================] - 0s 44us/step - loss: 0.8974 - accuracy: 0.9832 - val_loss: 0.9792 - val_accuracy: 0.9300 Epoch 77/100 298/298 [==============================] - 0s 47us/step - loss: 0.8938 - accuracy: 0.9832 - val_loss: 0.9760 - val_accuracy: 0.9300 Epoch 78/100 298/298 [==============================] - 0s 45us/step - loss: 0.8903 - accuracy: 0.9832 - val_loss: 0.9729 - val_accuracy: 0.9300 Epoch 79/100 298/298 [==============================] - 0s 44us/step - loss: 0.8867 - accuracy: 0.9832 - val_loss: 0.9697 - val_accuracy: 0.9300 Epoch 80/100 298/298 [==============================] - 0s 42us/step - loss: 0.8832 - accuracy: 0.9832 - val_loss: 0.9664 - val_accuracy: 0.9300 Epoch 81/100 298/298 [==============================] - 0s 42us/step - loss: 0.8796 - accuracy: 0.9832 - val_loss: 0.9629 - val_accuracy: 0.9400 Epoch 82/100 298/298 [==============================] - 0s 42us/step - loss: 0.8761 - accuracy: 0.9832 - val_loss: 0.9597 - val_accuracy: 0.9400 Epoch 83/100 298/298 [==============================] - 0s 45us/step - loss: 0.8726 - accuracy: 0.9832 - val_loss: 0.9562 - val_accuracy: 0.9400 Epoch 84/100 298/298 [==============================] - 0s 47us/step - loss: 0.8691 - accuracy: 0.9832 - val_loss: 0.9531 - val_accuracy: 0.9400 Epoch 85/100 298/298 [==============================] - 0s 50us/step - loss: 0.8657 - accuracy: 0.9832 - val_loss: 0.9499 - val_accuracy: 0.9400 Epoch 86/100 298/298 [==============================] - 0s 46us/step - loss: 0.8623 - accuracy: 0.9832 - val_loss: 0.9470 - val_accuracy: 0.9400 Epoch 87/100 298/298 [==============================] - 0s 45us/step - loss: 0.8589 - accuracy: 0.9866 - val_loss: 0.9439 - val_accuracy: 0.9400 Epoch 88/100 298/298 [==============================] - 0s 43us/step - loss: 0.8555 - accuracy: 0.9866 - val_loss: 0.9408 - val_accuracy: 0.9400 Epoch 89/100 298/298 [==============================] - 0s 43us/step - loss: 0.8522 - accuracy: 0.9866 - val_loss: 0.9373 - val_accuracy: 0.9400 Epoch 90/100 298/298 [==============================] - 0s 43us/step - loss: 0.8488 - accuracy: 0.9866 - val_loss: 0.9343 - val_accuracy: 0.9400 Epoch 91/100 298/298 [==============================] - 0s 44us/step - loss: 0.8455 - accuracy: 0.9866 - val_loss: 0.9310 - val_accuracy: 0.9400 Epoch 92/100 298/298 [==============================] - 0s 46us/step - loss: 0.8422 - accuracy: 0.9866 - val_loss: 0.9282 - val_accuracy: 0.9400 Epoch 93/100 298/298 [==============================] - 0s 47us/step - loss: 0.8389 - accuracy: 0.9866 - val_loss: 0.9252 - val_accuracy: 0.9400 Epoch 94/100 298/298 [==============================] - 0s 43us/step - loss: 0.8356 - accuracy: 0.9866 - val_loss: 0.9224 - val_accuracy: 0.9400 Epoch 95/100 298/298 [==============================] - 0s 43us/step - loss: 0.8324 - accuracy: 0.9866 - val_loss: 0.9194 - val_accuracy: 0.9400 Epoch 96/100 298/298 [==============================] - 0s 41us/step - loss: 0.8292 - accuracy: 0.9866 - val_loss: 0.9165 - val_accuracy: 0.9400 Epoch 97/100 298/298 [==============================] - 0s 41us/step - loss: 0.8259 - accuracy: 0.9866 - val_loss: 0.9136 - val_accuracy: 0.9400 Epoch 98/100 298/298 [==============================] - 0s 39us/step - loss: 0.8227 - accuracy: 0.9866 - val_loss: 0.9107 - val_accuracy: 0.9400 Epoch 99/100 298/298 [==============================] - 0s 46us/step - loss: 0.8195 - accuracy: 0.9866 - val_loss: 0.9081 - val_accuracy: 0.9400 Epoch 100/100 298/298 [==============================] - 0s 45us/step - loss: 0.8164 - accuracy: 0.9866 - val_loss: 0.9053 - val_accuracy: 0.9400 171/171 [==============================] - 0s 25us/step Optimizers: <keras.optimizers.SGD object at 0x1463bb710> Epoch Sizes: 100 Neurons or Units: 64 ['loss', 'accuracy'] [0.8260200361759342, 0.9824561476707458] Test score: 0.8260200361759342 Test accuracy: 0.9824561476707458 Model: "sequential_32" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_94 (Dense) (None, 128) 3968 _________________________________________________________________ activation_94 (Activation) (None, 128) 0 _________________________________________________________________ dense_95 (Dense) (None, 128) 16512 _________________________________________________________________ activation_95 (Activation) (None, 128) 0 _________________________________________________________________ dense_96 (Dense) (None, 1) 129 _________________________________________________________________ activation_96 (Activation) (None, 1) 0 ================================================================= Total params: 20,609 Trainable params: 20,609 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/100 298/298 [==============================] - 0s 461us/step - loss: 2.4715 - accuracy: 0.6007 - val_loss: 2.3766 - val_accuracy: 0.7000 Epoch 2/100 298/298 [==============================] - 0s 47us/step - loss: 2.3270 - accuracy: 0.7919 - val_loss: 2.2712 - val_accuracy: 0.8400 Epoch 3/100 298/298 [==============================] - 0s 46us/step - loss: 2.2342 - accuracy: 0.8758 - val_loss: 2.1983 - val_accuracy: 0.8900 Epoch 4/100 298/298 [==============================] - 0s 47us/step - loss: 2.1673 - accuracy: 0.9262 - val_loss: 2.1448 - val_accuracy: 0.9000 Epoch 5/100 298/298 [==============================] - 0s 45us/step - loss: 2.1156 - accuracy: 0.9329 - val_loss: 2.1026 - val_accuracy: 0.9000 Epoch 6/100 298/298 [==============================] - 0s 48us/step - loss: 2.0746 - accuracy: 0.9329 - val_loss: 2.0690 - val_accuracy: 0.9000 Epoch 7/100 298/298 [==============================] - 0s 45us/step - loss: 2.0398 - accuracy: 0.9396 - val_loss: 2.0412 - val_accuracy: 0.9000 Epoch 8/100 298/298 [==============================] - 0s 44us/step - loss: 2.0113 - accuracy: 0.9463 - val_loss: 2.0175 - val_accuracy: 0.9100 Epoch 9/100 298/298 [==============================] - 0s 46us/step - loss: 1.9859 - accuracy: 0.9463 - val_loss: 1.9969 - val_accuracy: 0.9100 Epoch 10/100 298/298 [==============================] - 0s 43us/step - loss: 1.9639 - accuracy: 0.9530 - val_loss: 1.9784 - val_accuracy: 0.9100 Epoch 11/100 298/298 [==============================] - 0s 44us/step - loss: 1.9438 - accuracy: 0.9530 - val_loss: 1.9622 - val_accuracy: 0.9100 Epoch 12/100 298/298 [==============================] - 0s 45us/step - loss: 1.9257 - accuracy: 0.9530 - val_loss: 1.9467 - val_accuracy: 0.9100 Epoch 13/100 298/298 [==============================] - 0s 49us/step - loss: 1.9089 - accuracy: 0.9564 - val_loss: 1.9329 - val_accuracy: 0.9100 Epoch 14/100 298/298 [==============================] - 0s 44us/step - loss: 1.8934 - accuracy: 0.9564 - val_loss: 1.9202 - val_accuracy: 0.9100 Epoch 15/100 298/298 [==============================] - 0s 43us/step - loss: 1.8790 - accuracy: 0.9597 - val_loss: 1.9080 - val_accuracy: 0.9100 Epoch 16/100 298/298 [==============================] - 0s 45us/step - loss: 1.8655 - accuracy: 0.9597 - val_loss: 1.8967 - val_accuracy: 0.9100 Epoch 17/100 298/298 [==============================] - 0s 44us/step - loss: 1.8528 - accuracy: 0.9664 - val_loss: 1.8859 - val_accuracy: 0.9100 Epoch 18/100 298/298 [==============================] - 0s 42us/step - loss: 1.8406 - accuracy: 0.9664 - val_loss: 1.8756 - val_accuracy: 0.9100 Epoch 19/100 298/298 [==============================] - 0s 44us/step - loss: 1.8289 - accuracy: 0.9664 - val_loss: 1.8656 - val_accuracy: 0.9100 Epoch 20/100 298/298 [==============================] - 0s 46us/step - loss: 1.8176 - accuracy: 0.9664 - val_loss: 1.8560 - val_accuracy: 0.9100 Epoch 21/100 298/298 [==============================] - 0s 43us/step - loss: 1.8069 - accuracy: 0.9664 - val_loss: 1.8467 - val_accuracy: 0.9100 Epoch 22/100 298/298 [==============================] - 0s 43us/step - loss: 1.7965 - accuracy: 0.9664 - val_loss: 1.8377 - val_accuracy: 0.9100 Epoch 23/100 298/298 [==============================] - 0s 45us/step - loss: 1.7865 - accuracy: 0.9664 - val_loss: 1.8284 - val_accuracy: 0.9100 Epoch 24/100 298/298 [==============================] - 0s 44us/step - loss: 1.7766 - accuracy: 0.9698 - val_loss: 1.8196 - val_accuracy: 0.9100 Epoch 25/100 298/298 [==============================] - 0s 45us/step - loss: 1.7669 - accuracy: 0.9698 - val_loss: 1.8111 - val_accuracy: 0.9100 Epoch 26/100 298/298 [==============================] - 0s 47us/step - loss: 1.7577 - accuracy: 0.9698 - val_loss: 1.8028 - val_accuracy: 0.9100 Epoch 27/100 298/298 [==============================] - 0s 48us/step - loss: 1.7486 - accuracy: 0.9698 - val_loss: 1.7946 - val_accuracy: 0.9100 Epoch 28/100 298/298 [==============================] - 0s 51us/step - loss: 1.7398 - accuracy: 0.9698 - val_loss: 1.7866 - val_accuracy: 0.9100 Epoch 29/100 298/298 [==============================] - 0s 47us/step - loss: 1.7311 - accuracy: 0.9698 - val_loss: 1.7789 - val_accuracy: 0.9100 Epoch 30/100 298/298 [==============================] - 0s 51us/step - loss: 1.7226 - accuracy: 0.9698 - val_loss: 1.7711 - val_accuracy: 0.9100 Epoch 31/100 298/298 [==============================] - 0s 55us/step - loss: 1.7143 - accuracy: 0.9698 - val_loss: 1.7632 - val_accuracy: 0.9100 Epoch 32/100 298/298 [==============================] - 0s 44us/step - loss: 1.7060 - accuracy: 0.9698 - val_loss: 1.7554 - val_accuracy: 0.9100 Epoch 33/100 298/298 [==============================] - 0s 47us/step - loss: 1.6978 - accuracy: 0.9698 - val_loss: 1.7483 - val_accuracy: 0.9200 Epoch 34/100 298/298 [==============================] - 0s 49us/step - loss: 1.6899 - accuracy: 0.9698 - val_loss: 1.7408 - val_accuracy: 0.9200 Epoch 35/100 298/298 [==============================] - 0s 46us/step - loss: 1.6820 - accuracy: 0.9698 - val_loss: 1.7334 - val_accuracy: 0.9200 Epoch 36/100 298/298 [==============================] - 0s 46us/step - loss: 1.6743 - accuracy: 0.9698 - val_loss: 1.7258 - val_accuracy: 0.9200 Epoch 37/100 298/298 [==============================] - 0s 45us/step - loss: 1.6666 - accuracy: 0.9698 - val_loss: 1.7185 - val_accuracy: 0.9200 Epoch 38/100 298/298 [==============================] - 0s 46us/step - loss: 1.6591 - accuracy: 0.9698 - val_loss: 1.7115 - val_accuracy: 0.9200 Epoch 39/100 298/298 [==============================] - 0s 42us/step - loss: 1.6516 - accuracy: 0.9698 - val_loss: 1.7047 - val_accuracy: 0.9200 Epoch 40/100 298/298 [==============================] - 0s 44us/step - loss: 1.6442 - accuracy: 0.9698 - val_loss: 1.6976 - val_accuracy: 0.9200 Epoch 41/100 298/298 [==============================] - 0s 46us/step - loss: 1.6369 - accuracy: 0.9698 - val_loss: 1.6907 - val_accuracy: 0.9200 Epoch 42/100 298/298 [==============================] - 0s 44us/step - loss: 1.6296 - accuracy: 0.9698 - val_loss: 1.6836 - val_accuracy: 0.9200 Epoch 43/100 298/298 [==============================] - 0s 42us/step - loss: 1.6224 - accuracy: 0.9698 - val_loss: 1.6767 - val_accuracy: 0.9200 Epoch 44/100 298/298 [==============================] - 0s 44us/step - loss: 1.6154 - accuracy: 0.9698 - val_loss: 1.6699 - val_accuracy: 0.9300 Epoch 45/100 298/298 [==============================] - 0s 46us/step - loss: 1.6085 - accuracy: 0.9698 - val_loss: 1.6632 - val_accuracy: 0.9400 Epoch 46/100 298/298 [==============================] - 0s 43us/step - loss: 1.6016 - accuracy: 0.9698 - val_loss: 1.6567 - val_accuracy: 0.9400 Epoch 47/100 298/298 [==============================] - 0s 43us/step - loss: 1.5947 - accuracy: 0.9698 - val_loss: 1.6503 - val_accuracy: 0.9400 Epoch 48/100 298/298 [==============================] - 0s 44us/step - loss: 1.5879 - accuracy: 0.9765 - val_loss: 1.6438 - val_accuracy: 0.9400 Epoch 49/100 298/298 [==============================] - 0s 46us/step - loss: 1.5812 - accuracy: 0.9799 - val_loss: 1.6375 - val_accuracy: 0.9400 Epoch 50/100 298/298 [==============================] - 0s 46us/step - loss: 1.5746 - accuracy: 0.9799 - val_loss: 1.6313 - val_accuracy: 0.9400 Epoch 51/100 298/298 [==============================] - 0s 45us/step - loss: 1.5678 - accuracy: 0.9799 - val_loss: 1.6253 - val_accuracy: 0.9400 Epoch 52/100 298/298 [==============================] - 0s 43us/step - loss: 1.5613 - accuracy: 0.9799 - val_loss: 1.6189 - val_accuracy: 0.9400 Epoch 53/100 298/298 [==============================] - 0s 44us/step - loss: 1.5548 - accuracy: 0.9799 - val_loss: 1.6127 - val_accuracy: 0.9400 Epoch 54/100 298/298 [==============================] - 0s 45us/step - loss: 1.5483 - accuracy: 0.9799 - val_loss: 1.6065 - val_accuracy: 0.9400 Epoch 55/100 298/298 [==============================] - 0s 46us/step - loss: 1.5419 - accuracy: 0.9799 - val_loss: 1.6004 - val_accuracy: 0.9400 Epoch 56/100 298/298 [==============================] - 0s 47us/step - loss: 1.5355 - accuracy: 0.9799 - val_loss: 1.5944 - val_accuracy: 0.9400 Epoch 57/100 298/298 [==============================] - 0s 47us/step - loss: 1.5292 - accuracy: 0.9799 - val_loss: 1.5885 - val_accuracy: 0.9400 Epoch 58/100 298/298 [==============================] - 0s 45us/step - loss: 1.5229 - accuracy: 0.9799 - val_loss: 1.5826 - val_accuracy: 0.9400 Epoch 59/100 298/298 [==============================] - 0s 44us/step - loss: 1.5166 - accuracy: 0.9799 - val_loss: 1.5767 - val_accuracy: 0.9400 Epoch 60/100 298/298 [==============================] - 0s 45us/step - loss: 1.5105 - accuracy: 0.9799 - val_loss: 1.5712 - val_accuracy: 0.9400 Epoch 61/100 298/298 [==============================] - 0s 47us/step - loss: 1.5043 - accuracy: 0.9799 - val_loss: 1.5652 - val_accuracy: 0.9400 Epoch 62/100 298/298 [==============================] - 0s 46us/step - loss: 1.4981 - accuracy: 0.9799 - val_loss: 1.5596 - val_accuracy: 0.9400 Epoch 63/100 298/298 [==============================] - 0s 47us/step - loss: 1.4921 - accuracy: 0.9799 - val_loss: 1.5538 - val_accuracy: 0.9400 Epoch 64/100 298/298 [==============================] - 0s 45us/step - loss: 1.4860 - accuracy: 0.9799 - val_loss: 1.5484 - val_accuracy: 0.9400 Epoch 65/100 298/298 [==============================] - 0s 47us/step - loss: 1.4800 - accuracy: 0.9799 - val_loss: 1.5427 - val_accuracy: 0.9400 Epoch 66/100 298/298 [==============================] - 0s 49us/step - loss: 1.4741 - accuracy: 0.9799 - val_loss: 1.5371 - val_accuracy: 0.9400 Epoch 67/100 298/298 [==============================] - 0s 47us/step - loss: 1.4682 - accuracy: 0.9799 - val_loss: 1.5315 - val_accuracy: 0.9400 Epoch 68/100 298/298 [==============================] - 0s 46us/step - loss: 1.4623 - accuracy: 0.9799 - val_loss: 1.5257 - val_accuracy: 0.9400 Epoch 69/100 298/298 [==============================] - 0s 44us/step - loss: 1.4564 - accuracy: 0.9799 - val_loss: 1.5199 - val_accuracy: 0.9400 Epoch 70/100 298/298 [==============================] - 0s 42us/step - loss: 1.4505 - accuracy: 0.9799 - val_loss: 1.5145 - val_accuracy: 0.9400 Epoch 71/100 298/298 [==============================] - 0s 45us/step - loss: 1.4448 - accuracy: 0.9799 - val_loss: 1.5090 - val_accuracy: 0.9400 Epoch 72/100 298/298 [==============================] - 0s 47us/step - loss: 1.4390 - accuracy: 0.9799 - val_loss: 1.5036 - val_accuracy: 0.9400 Epoch 73/100 298/298 [==============================] - 0s 43us/step - loss: 1.4333 - accuracy: 0.9799 - val_loss: 1.4983 - val_accuracy: 0.9400 Epoch 74/100 298/298 [==============================] - 0s 44us/step - loss: 1.4276 - accuracy: 0.9832 - val_loss: 1.4925 - val_accuracy: 0.9400 Epoch 75/100 298/298 [==============================] - 0s 43us/step - loss: 1.4219 - accuracy: 0.9832 - val_loss: 1.4875 - val_accuracy: 0.9400 Epoch 76/100 298/298 [==============================] - 0s 45us/step - loss: 1.4163 - accuracy: 0.9832 - val_loss: 1.4822 - val_accuracy: 0.9400 Epoch 77/100 298/298 [==============================] - 0s 43us/step - loss: 1.4106 - accuracy: 0.9832 - val_loss: 1.4770 - val_accuracy: 0.9400 Epoch 78/100 298/298 [==============================] - 0s 44us/step - loss: 1.4051 - accuracy: 0.9832 - val_loss: 1.4718 - val_accuracy: 0.9400 Epoch 79/100 298/298 [==============================] - 0s 44us/step - loss: 1.3995 - accuracy: 0.9832 - val_loss: 1.4666 - val_accuracy: 0.9400 Epoch 80/100 298/298 [==============================] - 0s 44us/step - loss: 1.3940 - accuracy: 0.9866 - val_loss: 1.4614 - val_accuracy: 0.9400 Epoch 81/100 298/298 [==============================] - 0s 43us/step - loss: 1.3886 - accuracy: 0.9866 - val_loss: 1.4562 - val_accuracy: 0.9400 Epoch 82/100 298/298 [==============================] - 0s 44us/step - loss: 1.3831 - accuracy: 0.9832 - val_loss: 1.4511 - val_accuracy: 0.9400 Epoch 83/100 298/298 [==============================] - 0s 45us/step - loss: 1.3777 - accuracy: 0.9832 - val_loss: 1.4461 - val_accuracy: 0.9400 Epoch 84/100 298/298 [==============================] - 0s 43us/step - loss: 1.3722 - accuracy: 0.9866 - val_loss: 1.4409 - val_accuracy: 0.9400 Epoch 85/100 298/298 [==============================] - 0s 43us/step - loss: 1.3669 - accuracy: 0.9866 - val_loss: 1.4359 - val_accuracy: 0.9400 Epoch 86/100 298/298 [==============================] - 0s 45us/step - loss: 1.3615 - accuracy: 0.9866 - val_loss: 1.4308 - val_accuracy: 0.9400 Epoch 87/100 298/298 [==============================] - 0s 44us/step - loss: 1.3562 - accuracy: 0.9866 - val_loss: 1.4258 - val_accuracy: 0.9400 Epoch 88/100 298/298 [==============================] - 0s 45us/step - loss: 1.3509 - accuracy: 0.9866 - val_loss: 1.4208 - val_accuracy: 0.9400 Epoch 89/100 298/298 [==============================] - 0s 49us/step - loss: 1.3457 - accuracy: 0.9866 - val_loss: 1.4159 - val_accuracy: 0.9400 Epoch 90/100 298/298 [==============================] - 0s 46us/step - loss: 1.3403 - accuracy: 0.9866 - val_loss: 1.4109 - val_accuracy: 0.9500 Epoch 91/100 298/298 [==============================] - 0s 47us/step - loss: 1.3351 - accuracy: 0.9866 - val_loss: 1.4062 - val_accuracy: 0.9500 Epoch 92/100 298/298 [==============================] - 0s 45us/step - loss: 1.3299 - accuracy: 0.9866 - val_loss: 1.4011 - val_accuracy: 0.9500 Epoch 93/100 298/298 [==============================] - 0s 47us/step - loss: 1.3247 - accuracy: 0.9866 - val_loss: 1.3961 - val_accuracy: 0.9500 Epoch 94/100 298/298 [==============================] - 0s 46us/step - loss: 1.3195 - accuracy: 0.9866 - val_loss: 1.3913 - val_accuracy: 0.9500 Epoch 95/100 298/298 [==============================] - 0s 46us/step - loss: 1.3143 - accuracy: 0.9866 - val_loss: 1.3860 - val_accuracy: 0.9500 Epoch 96/100 298/298 [==============================] - 0s 56us/step - loss: 1.3092 - accuracy: 0.9866 - val_loss: 1.3811 - val_accuracy: 0.9500 Epoch 97/100 298/298 [==============================] - 0s 45us/step - loss: 1.3041 - accuracy: 0.9866 - val_loss: 1.3763 - val_accuracy: 0.9500 Epoch 98/100 298/298 [==============================] - 0s 48us/step - loss: 1.2990 - accuracy: 0.9866 - val_loss: 1.3712 - val_accuracy: 0.9500 Epoch 99/100 298/298 [==============================] - 0s 47us/step - loss: 1.2939 - accuracy: 0.9866 - val_loss: 1.3665 - val_accuracy: 0.9500 Epoch 100/100 298/298 [==============================] - 0s 48us/step - loss: 1.2889 - accuracy: 0.9866 - val_loss: 1.3618 - val_accuracy: 0.9500 171/171 [==============================] - 0s 28us/step Optimizers: <keras.optimizers.SGD object at 0x1463bb710> Epoch Sizes: 100 Neurons or Units: 128 ['loss', 'accuracy'] [1.2918432051675361, 0.988304078578949] Test score: 1.2918432051675361 Test accuracy: 0.988304078578949 Model: "sequential_33" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_97 (Dense) (None, 256) 7936 _________________________________________________________________ activation_97 (Activation) (None, 256) 0 _________________________________________________________________ dense_98 (Dense) (None, 256) 65792 _________________________________________________________________ activation_98 (Activation) (None, 256) 0 _________________________________________________________________ dense_99 (Dense) (None, 1) 257 _________________________________________________________________ activation_99 (Activation) (None, 1) 0 ================================================================= Total params: 73,985 Trainable params: 73,985 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/100 298/298 [==============================] - 0s 507us/step - loss: 3.7040 - accuracy: 0.7685 - val_loss: 3.6288 - val_accuracy: 0.9000 Epoch 2/100 298/298 [==============================] - 0s 67us/step - loss: 3.5979 - accuracy: 0.8758 - val_loss: 3.5444 - val_accuracy: 0.9200 Epoch 3/100 298/298 [==============================] - 0s 63us/step - loss: 3.5200 - accuracy: 0.9161 - val_loss: 3.4778 - val_accuracy: 0.9300 Epoch 4/100 298/298 [==============================] - 0s 62us/step - loss: 3.4571 - accuracy: 0.9362 - val_loss: 3.4255 - val_accuracy: 0.9300 Epoch 5/100 298/298 [==============================] - 0s 62us/step - loss: 3.4058 - accuracy: 0.9497 - val_loss: 3.3823 - val_accuracy: 0.9300 Epoch 6/100 298/298 [==============================] - 0s 66us/step - loss: 3.3629 - accuracy: 0.9463 - val_loss: 3.3458 - val_accuracy: 0.9300 Epoch 7/100 298/298 [==============================] - 0s 61us/step - loss: 3.3255 - accuracy: 0.9530 - val_loss: 3.3139 - val_accuracy: 0.9300 Epoch 8/100 298/298 [==============================] - 0s 62us/step - loss: 3.2923 - accuracy: 0.9530 - val_loss: 3.2858 - val_accuracy: 0.9300 Epoch 9/100 298/298 [==============================] - 0s 59us/step - loss: 3.2624 - accuracy: 0.9530 - val_loss: 3.2607 - val_accuracy: 0.9300 Epoch 10/100 298/298 [==============================] - 0s 59us/step - loss: 3.2355 - accuracy: 0.9530 - val_loss: 3.2383 - val_accuracy: 0.9300 Epoch 11/100 298/298 [==============================] - 0s 60us/step - loss: 3.2103 - accuracy: 0.9564 - val_loss: 3.2174 - val_accuracy: 0.9300 Epoch 12/100 298/298 [==============================] - 0s 67us/step - loss: 3.1877 - accuracy: 0.9564 - val_loss: 3.1977 - val_accuracy: 0.9300 Epoch 13/100 298/298 [==============================] - 0s 63us/step - loss: 3.1661 - accuracy: 0.9564 - val_loss: 3.1794 - val_accuracy: 0.9300 Epoch 14/100 298/298 [==============================] - 0s 63us/step - loss: 3.1455 - accuracy: 0.9564 - val_loss: 3.1616 - val_accuracy: 0.9300 Epoch 15/100 298/298 [==============================] - 0s 64us/step - loss: 3.1261 - accuracy: 0.9564 - val_loss: 3.1449 - val_accuracy: 0.9300 Epoch 16/100 298/298 [==============================] - 0s 64us/step - loss: 3.1074 - accuracy: 0.9564 - val_loss: 3.1286 - val_accuracy: 0.9300 Epoch 17/100 298/298 [==============================] - 0s 64us/step - loss: 3.0894 - accuracy: 0.9564 - val_loss: 3.1134 - val_accuracy: 0.9300 Epoch 18/100 298/298 [==============================] - 0s 67us/step - loss: 3.0722 - accuracy: 0.9631 - val_loss: 3.0985 - val_accuracy: 0.9300 Epoch 19/100 298/298 [==============================] - 0s 69us/step - loss: 3.0556 - accuracy: 0.9631 - val_loss: 3.0834 - val_accuracy: 0.9300 Epoch 20/100 298/298 [==============================] - 0s 67us/step - loss: 3.0393 - accuracy: 0.9698 - val_loss: 3.0693 - val_accuracy: 0.9300 Epoch 21/100 298/298 [==============================] - 0s 69us/step - loss: 3.0236 - accuracy: 0.9698 - val_loss: 3.0554 - val_accuracy: 0.9300 Epoch 22/100 298/298 [==============================] - 0s 69us/step - loss: 3.0083 - accuracy: 0.9732 - val_loss: 3.0419 - val_accuracy: 0.9300 Epoch 23/100 298/298 [==============================] - 0s 67us/step - loss: 2.9934 - accuracy: 0.9732 - val_loss: 3.0282 - val_accuracy: 0.9300 Epoch 24/100 298/298 [==============================] - 0s 72us/step - loss: 2.9785 - accuracy: 0.9732 - val_loss: 3.0152 - val_accuracy: 0.9300 Epoch 25/100 298/298 [==============================] - 0s 62us/step - loss: 2.9641 - accuracy: 0.9732 - val_loss: 3.0024 - val_accuracy: 0.9300 Epoch 26/100 298/298 [==============================] - 0s 56us/step - loss: 2.9501 - accuracy: 0.9732 - val_loss: 2.9898 - val_accuracy: 0.9300 Epoch 27/100 298/298 [==============================] - 0s 54us/step - loss: 2.9361 - accuracy: 0.9732 - val_loss: 2.9774 - val_accuracy: 0.9300 Epoch 28/100 298/298 [==============================] - 0s 53us/step - loss: 2.9224 - accuracy: 0.9732 - val_loss: 2.9647 - val_accuracy: 0.9300 Epoch 29/100 298/298 [==============================] - 0s 62us/step - loss: 2.9091 - accuracy: 0.9732 - val_loss: 2.9525 - val_accuracy: 0.9300 Epoch 30/100 298/298 [==============================] - 0s 58us/step - loss: 2.8957 - accuracy: 0.9765 - val_loss: 2.9404 - val_accuracy: 0.9300 Epoch 31/100 298/298 [==============================] - 0s 57us/step - loss: 2.8827 - accuracy: 0.9765 - val_loss: 2.9286 - val_accuracy: 0.9300 Epoch 32/100 298/298 [==============================] - 0s 55us/step - loss: 2.8698 - accuracy: 0.9765 - val_loss: 2.9168 - val_accuracy: 0.9300 Epoch 33/100 298/298 [==============================] - 0s 53us/step - loss: 2.8570 - accuracy: 0.9765 - val_loss: 2.9056 - val_accuracy: 0.9300 Epoch 34/100 298/298 [==============================] - 0s 53us/step - loss: 2.8443 - accuracy: 0.9765 - val_loss: 2.8939 - val_accuracy: 0.9300 Epoch 35/100 298/298 [==============================] - 0s 54us/step - loss: 2.8317 - accuracy: 0.9765 - val_loss: 2.8826 - val_accuracy: 0.9300 Epoch 36/100 298/298 [==============================] - 0s 56us/step - loss: 2.8193 - accuracy: 0.9765 - val_loss: 2.8711 - val_accuracy: 0.9300 Epoch 37/100 298/298 [==============================] - 0s 53us/step - loss: 2.8071 - accuracy: 0.9765 - val_loss: 2.8598 - val_accuracy: 0.9300 Epoch 38/100 298/298 [==============================] - 0s 56us/step - loss: 2.7949 - accuracy: 0.9765 - val_loss: 2.8487 - val_accuracy: 0.9300 Epoch 39/100 298/298 [==============================] - 0s 55us/step - loss: 2.7830 - accuracy: 0.9765 - val_loss: 2.8374 - val_accuracy: 0.9300 Epoch 40/100 298/298 [==============================] - 0s 67us/step - loss: 2.7711 - accuracy: 0.9765 - val_loss: 2.8264 - val_accuracy: 0.9300 Epoch 41/100 298/298 [==============================] - 0s 62us/step - loss: 2.7593 - accuracy: 0.9765 - val_loss: 2.8155 - val_accuracy: 0.9300 Epoch 42/100 298/298 [==============================] - 0s 62us/step - loss: 2.7476 - accuracy: 0.9765 - val_loss: 2.8047 - val_accuracy: 0.9300 Epoch 43/100 298/298 [==============================] - 0s 59us/step - loss: 2.7360 - accuracy: 0.9799 - val_loss: 2.7939 - val_accuracy: 0.9300 Epoch 44/100 298/298 [==============================] - 0s 56us/step - loss: 2.7245 - accuracy: 0.9799 - val_loss: 2.7832 - val_accuracy: 0.9300 Epoch 45/100 298/298 [==============================] - 0s 55us/step - loss: 2.7130 - accuracy: 0.9799 - val_loss: 2.7719 - val_accuracy: 0.9300 Epoch 46/100 298/298 [==============================] - 0s 58us/step - loss: 2.7016 - accuracy: 0.9799 - val_loss: 2.7612 - val_accuracy: 0.9300 Epoch 47/100 298/298 [==============================] - 0s 59us/step - loss: 2.6903 - accuracy: 0.9799 - val_loss: 2.7507 - val_accuracy: 0.9400 Epoch 48/100 298/298 [==============================] - 0s 56us/step - loss: 2.6791 - accuracy: 0.9799 - val_loss: 2.7401 - val_accuracy: 0.9400 Epoch 49/100 298/298 [==============================] - 0s 54us/step - loss: 2.6680 - accuracy: 0.9799 - val_loss: 2.7294 - val_accuracy: 0.9400 Epoch 50/100 298/298 [==============================] - 0s 57us/step - loss: 2.6570 - accuracy: 0.9799 - val_loss: 2.7190 - val_accuracy: 0.9400 Epoch 51/100 298/298 [==============================] - 0s 57us/step - loss: 2.6460 - accuracy: 0.9799 - val_loss: 2.7086 - val_accuracy: 0.9400 Epoch 52/100 298/298 [==============================] - 0s 55us/step - loss: 2.6351 - accuracy: 0.9799 - val_loss: 2.6985 - val_accuracy: 0.9400 Epoch 53/100 298/298 [==============================] - 0s 60us/step - loss: 2.6243 - accuracy: 0.9799 - val_loss: 2.6885 - val_accuracy: 0.9400 Epoch 54/100 298/298 [==============================] - 0s 58us/step - loss: 2.6135 - accuracy: 0.9832 - val_loss: 2.6784 - val_accuracy: 0.9400 Epoch 55/100 298/298 [==============================] - 0s 67us/step - loss: 2.6029 - accuracy: 0.9832 - val_loss: 2.6684 - val_accuracy: 0.9400 Epoch 56/100 298/298 [==============================] - 0s 53us/step - loss: 2.5922 - accuracy: 0.9832 - val_loss: 2.6581 - val_accuracy: 0.9400 Epoch 57/100 298/298 [==============================] - 0s 55us/step - loss: 2.5816 - accuracy: 0.9832 - val_loss: 2.6480 - val_accuracy: 0.9400 Epoch 58/100 298/298 [==============================] - 0s 54us/step - loss: 2.5711 - accuracy: 0.9832 - val_loss: 2.6376 - val_accuracy: 0.9400 Epoch 59/100 298/298 [==============================] - 0s 57us/step - loss: 2.5606 - accuracy: 0.9832 - val_loss: 2.6277 - val_accuracy: 0.9400 Epoch 60/100 298/298 [==============================] - 0s 55us/step - loss: 2.5503 - accuracy: 0.9832 - val_loss: 2.6180 - val_accuracy: 0.9400 Epoch 61/100 298/298 [==============================] - 0s 73us/step - loss: 2.5399 - accuracy: 0.9832 - val_loss: 2.6076 - val_accuracy: 0.9400 Epoch 62/100 298/298 [==============================] - 0s 68us/step - loss: 2.5297 - accuracy: 0.9832 - val_loss: 2.5978 - val_accuracy: 0.9400 Epoch 63/100 298/298 [==============================] - 0s 88us/step - loss: 2.5194 - accuracy: 0.9832 - val_loss: 2.5882 - val_accuracy: 0.9400 Epoch 64/100 298/298 [==============================] - 0s 92us/step - loss: 2.5093 - accuracy: 0.9832 - val_loss: 2.5785 - val_accuracy: 0.9400 Epoch 65/100 298/298 [==============================] - 0s 64us/step - loss: 2.4991 - accuracy: 0.9832 - val_loss: 2.5689 - val_accuracy: 0.9400 Epoch 66/100 298/298 [==============================] - 0s 57us/step - loss: 2.4891 - accuracy: 0.9832 - val_loss: 2.5596 - val_accuracy: 0.9400 Epoch 67/100 298/298 [==============================] - 0s 55us/step - loss: 2.4791 - accuracy: 0.9832 - val_loss: 2.5502 - val_accuracy: 0.9400 Epoch 68/100 298/298 [==============================] - 0s 57us/step - loss: 2.4692 - accuracy: 0.9832 - val_loss: 2.5407 - val_accuracy: 0.9400 Epoch 69/100 298/298 [==============================] - 0s 59us/step - loss: 2.4593 - accuracy: 0.9832 - val_loss: 2.5312 - val_accuracy: 0.9400 Epoch 70/100 298/298 [==============================] - 0s 56us/step - loss: 2.4494 - accuracy: 0.9832 - val_loss: 2.5219 - val_accuracy: 0.9400 Epoch 71/100 298/298 [==============================] - 0s 56us/step - loss: 2.4396 - accuracy: 0.9866 - val_loss: 2.5127 - val_accuracy: 0.9400 Epoch 72/100 298/298 [==============================] - 0s 55us/step - loss: 2.4299 - accuracy: 0.9866 - val_loss: 2.5033 - val_accuracy: 0.9400 Epoch 73/100 298/298 [==============================] - 0s 57us/step - loss: 2.4202 - accuracy: 0.9866 - val_loss: 2.4938 - val_accuracy: 0.9400 Epoch 74/100 298/298 [==============================] - 0s 62us/step - loss: 2.4104 - accuracy: 0.9866 - val_loss: 2.4849 - val_accuracy: 0.9400 Epoch 75/100 298/298 [==============================] - 0s 57us/step - loss: 2.4008 - accuracy: 0.9866 - val_loss: 2.4757 - val_accuracy: 0.9400 Epoch 76/100 298/298 [==============================] - 0s 54us/step - loss: 2.3913 - accuracy: 0.9866 - val_loss: 2.4665 - val_accuracy: 0.9400 Epoch 77/100 298/298 [==============================] - 0s 57us/step - loss: 2.3818 - accuracy: 0.9866 - val_loss: 2.4574 - val_accuracy: 0.9400 Epoch 78/100 298/298 [==============================] - 0s 67us/step - loss: 2.3723 - accuracy: 0.9866 - val_loss: 2.4483 - val_accuracy: 0.9400 Epoch 79/100 298/298 [==============================] - 0s 56us/step - loss: 2.3629 - accuracy: 0.9866 - val_loss: 2.4394 - val_accuracy: 0.9400 Epoch 80/100 298/298 [==============================] - 0s 57us/step - loss: 2.3535 - accuracy: 0.9866 - val_loss: 2.4305 - val_accuracy: 0.9400 Epoch 81/100 298/298 [==============================] - 0s 55us/step - loss: 2.3441 - accuracy: 0.9866 - val_loss: 2.4215 - val_accuracy: 0.9400 Epoch 82/100 298/298 [==============================] - 0s 59us/step - loss: 2.3349 - accuracy: 0.9866 - val_loss: 2.4127 - val_accuracy: 0.9400 Epoch 83/100 298/298 [==============================] - 0s 56us/step - loss: 2.3257 - accuracy: 0.9866 - val_loss: 2.4030 - val_accuracy: 0.9400 Epoch 84/100 298/298 [==============================] - 0s 57us/step - loss: 2.3163 - accuracy: 0.9866 - val_loss: 2.3943 - val_accuracy: 0.9400 Epoch 85/100 298/298 [==============================] - 0s 56us/step - loss: 2.3071 - accuracy: 0.9866 - val_loss: 2.3856 - val_accuracy: 0.9400 Epoch 86/100 298/298 [==============================] - 0s 54us/step - loss: 2.2980 - accuracy: 0.9866 - val_loss: 2.3771 - val_accuracy: 0.9400 Epoch 87/100 298/298 [==============================] - 0s 56us/step - loss: 2.2889 - accuracy: 0.9866 - val_loss: 2.3684 - val_accuracy: 0.9400 Epoch 88/100 298/298 [==============================] - 0s 58us/step - loss: 2.2799 - accuracy: 0.9866 - val_loss: 2.3597 - val_accuracy: 0.9400 Epoch 89/100 298/298 [==============================] - 0s 57us/step - loss: 2.2709 - accuracy: 0.9866 - val_loss: 2.3511 - val_accuracy: 0.9400 Epoch 90/100 298/298 [==============================] - 0s 59us/step - loss: 2.2619 - accuracy: 0.9866 - val_loss: 2.3421 - val_accuracy: 0.9400 Epoch 91/100 298/298 [==============================] - 0s 55us/step - loss: 2.2530 - accuracy: 0.9866 - val_loss: 2.3336 - val_accuracy: 0.9400 Epoch 92/100 298/298 [==============================] - 0s 56us/step - loss: 2.2441 - accuracy: 0.9866 - val_loss: 2.3251 - val_accuracy: 0.9400 Epoch 93/100 298/298 [==============================] - 0s 59us/step - loss: 2.2352 - accuracy: 0.9866 - val_loss: 2.3166 - val_accuracy: 0.9400 Epoch 94/100 298/298 [==============================] - 0s 60us/step - loss: 2.2265 - accuracy: 0.9866 - val_loss: 2.3085 - val_accuracy: 0.9400 Epoch 95/100 298/298 [==============================] - 0s 62us/step - loss: 2.2177 - accuracy: 0.9866 - val_loss: 2.2997 - val_accuracy: 0.9400 Epoch 96/100 298/298 [==============================] - 0s 56us/step - loss: 2.2089 - accuracy: 0.9866 - val_loss: 2.2910 - val_accuracy: 0.9400 Epoch 97/100 298/298 [==============================] - 0s 53us/step - loss: 2.2003 - accuracy: 0.9899 - val_loss: 2.2828 - val_accuracy: 0.9400 Epoch 98/100 298/298 [==============================] - 0s 58us/step - loss: 2.1916 - accuracy: 0.9899 - val_loss: 2.2745 - val_accuracy: 0.9400 Epoch 99/100 298/298 [==============================] - 0s 55us/step - loss: 2.1830 - accuracy: 0.9899 - val_loss: 2.2665 - val_accuracy: 0.9400 Epoch 100/100 298/298 [==============================] - 0s 56us/step - loss: 2.1744 - accuracy: 0.9899 - val_loss: 2.2583 - val_accuracy: 0.9400 171/171 [==============================] - 0s 26us/step Optimizers: <keras.optimizers.SGD object at 0x1463bb710> Epoch Sizes: 100 Neurons or Units: 256 ['loss', 'accuracy'] [2.1798717696764314, 0.988304078578949] Test score: 2.1798717696764314 Test accuracy: 0.988304078578949 Model: "sequential_34" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_100 (Dense) (None, 64) 1984 _________________________________________________________________ activation_100 (Activation) (None, 64) 0 _________________________________________________________________ dense_101 (Dense) (None, 64) 4160 _________________________________________________________________ activation_101 (Activation) (None, 64) 0 _________________________________________________________________ dense_102 (Dense) (None, 1) 65 _________________________________________________________________ activation_102 (Activation) (None, 1) 0 ================================================================= Total params: 6,209 Trainable params: 6,209 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/200 298/298 [==============================] - 0s 463us/step - loss: 1.7616 - accuracy: 0.6174 - val_loss: 1.6689 - val_accuracy: 0.7300 Epoch 2/200 298/298 [==============================] - 0s 42us/step - loss: 1.6411 - accuracy: 0.7685 - val_loss: 1.5705 - val_accuracy: 0.8500 Epoch 3/200 298/298 [==============================] - 0s 42us/step - loss: 1.5532 - accuracy: 0.8691 - val_loss: 1.5027 - val_accuracy: 0.8800 Epoch 4/200 298/298 [==============================] - 0s 40us/step - loss: 1.4886 - accuracy: 0.8926 - val_loss: 1.4520 - val_accuracy: 0.9000 Epoch 5/200 298/298 [==============================] - 0s 41us/step - loss: 1.4378 - accuracy: 0.9262 - val_loss: 1.4119 - val_accuracy: 0.9000 Epoch 6/200 298/298 [==============================] - 0s 41us/step - loss: 1.3960 - accuracy: 0.9362 - val_loss: 1.3799 - val_accuracy: 0.9000 Epoch 7/200 298/298 [==============================] - 0s 42us/step - loss: 1.3619 - accuracy: 0.9463 - val_loss: 1.3532 - val_accuracy: 0.9000 Epoch 8/200 298/298 [==============================] - 0s 42us/step - loss: 1.3322 - accuracy: 0.9497 - val_loss: 1.3300 - val_accuracy: 0.9000 Epoch 9/200 298/298 [==============================] - 0s 41us/step - loss: 1.3057 - accuracy: 0.9497 - val_loss: 1.3103 - val_accuracy: 0.9000 Epoch 10/200 298/298 [==============================] - 0s 41us/step - loss: 1.2829 - accuracy: 0.9497 - val_loss: 1.2932 - val_accuracy: 0.9000 Epoch 11/200 298/298 [==============================] - 0s 41us/step - loss: 1.2627 - accuracy: 0.9530 - val_loss: 1.2780 - val_accuracy: 0.9000 Epoch 12/200 298/298 [==============================] - 0s 41us/step - loss: 1.2443 - accuracy: 0.9564 - val_loss: 1.2644 - val_accuracy: 0.9000 Epoch 13/200 298/298 [==============================] - 0s 42us/step - loss: 1.2278 - accuracy: 0.9597 - val_loss: 1.2527 - val_accuracy: 0.9000 Epoch 14/200 298/298 [==============================] - 0s 46us/step - loss: 1.2130 - accuracy: 0.9597 - val_loss: 1.2421 - val_accuracy: 0.9000 Epoch 15/200 298/298 [==============================] - 0s 46us/step - loss: 1.1994 - accuracy: 0.9597 - val_loss: 1.2324 - val_accuracy: 0.9000 Epoch 16/200 298/298 [==============================] - 0s 43us/step - loss: 1.1868 - accuracy: 0.9664 - val_loss: 1.2237 - val_accuracy: 0.9000 Epoch 17/200 298/298 [==============================] - 0s 43us/step - loss: 1.1753 - accuracy: 0.9664 - val_loss: 1.2151 - val_accuracy: 0.9000 Epoch 18/200 298/298 [==============================] - 0s 40us/step - loss: 1.1645 - accuracy: 0.9698 - val_loss: 1.2076 - val_accuracy: 0.9000 Epoch 19/200 298/298 [==============================] - 0s 42us/step - loss: 1.1543 - accuracy: 0.9698 - val_loss: 1.2003 - val_accuracy: 0.9000 Epoch 20/200 298/298 [==============================] - 0s 41us/step - loss: 1.1447 - accuracy: 0.9698 - val_loss: 1.1938 - val_accuracy: 0.9000 Epoch 21/200 298/298 [==============================] - 0s 41us/step - loss: 1.1357 - accuracy: 0.9698 - val_loss: 1.1875 - val_accuracy: 0.9000 Epoch 22/200 298/298 [==============================] - 0s 43us/step - loss: 1.1270 - accuracy: 0.9698 - val_loss: 1.1816 - val_accuracy: 0.9000 Epoch 23/200 298/298 [==============================] - 0s 42us/step - loss: 1.1189 - accuracy: 0.9698 - val_loss: 1.1757 - val_accuracy: 0.9000 Epoch 24/200 298/298 [==============================] - 0s 44us/step - loss: 1.1112 - accuracy: 0.9698 - val_loss: 1.1704 - val_accuracy: 0.9100 Epoch 25/200 298/298 [==============================] - 0s 42us/step - loss: 1.1037 - accuracy: 0.9698 - val_loss: 1.1650 - val_accuracy: 0.9100 Epoch 26/200 298/298 [==============================] - 0s 42us/step - loss: 1.0966 - accuracy: 0.9698 - val_loss: 1.1600 - val_accuracy: 0.9100 Epoch 27/200 298/298 [==============================] - 0s 40us/step - loss: 1.0898 - accuracy: 0.9698 - val_loss: 1.1550 - val_accuracy: 0.9000 Epoch 28/200 298/298 [==============================] - 0s 44us/step - loss: 1.0831 - accuracy: 0.9698 - val_loss: 1.1502 - val_accuracy: 0.9100 Epoch 29/200 298/298 [==============================] - 0s 42us/step - loss: 1.0767 - accuracy: 0.9698 - val_loss: 1.1455 - val_accuracy: 0.9000 Epoch 30/200 298/298 [==============================] - 0s 42us/step - loss: 1.0706 - accuracy: 0.9698 - val_loss: 1.1410 - val_accuracy: 0.9000 Epoch 31/200 298/298 [==============================] - 0s 44us/step - loss: 1.0645 - accuracy: 0.9698 - val_loss: 1.1363 - val_accuracy: 0.9000 Epoch 32/200 298/298 [==============================] - 0s 40us/step - loss: 1.0586 - accuracy: 0.9698 - val_loss: 1.1318 - val_accuracy: 0.9000 Epoch 33/200 298/298 [==============================] - 0s 40us/step - loss: 1.0527 - accuracy: 0.9732 - val_loss: 1.1275 - val_accuracy: 0.9000 Epoch 34/200 298/298 [==============================] - 0s 42us/step - loss: 1.0471 - accuracy: 0.9732 - val_loss: 1.1231 - val_accuracy: 0.9000 Epoch 35/200 298/298 [==============================] - 0s 40us/step - loss: 1.0416 - accuracy: 0.9732 - val_loss: 1.1186 - val_accuracy: 0.9000 Epoch 36/200 298/298 [==============================] - 0s 41us/step - loss: 1.0363 - accuracy: 0.9732 - val_loss: 1.1137 - val_accuracy: 0.9000 Epoch 37/200 298/298 [==============================] - 0s 41us/step - loss: 1.0310 - accuracy: 0.9732 - val_loss: 1.1096 - val_accuracy: 0.9000 Epoch 38/200 298/298 [==============================] - 0s 43us/step - loss: 1.0258 - accuracy: 0.9732 - val_loss: 1.1054 - val_accuracy: 0.9000 Epoch 39/200 298/298 [==============================] - 0s 45us/step - loss: 1.0208 - accuracy: 0.9732 - val_loss: 1.1014 - val_accuracy: 0.9000 Epoch 40/200 298/298 [==============================] - 0s 42us/step - loss: 1.0158 - accuracy: 0.9732 - val_loss: 1.0976 - val_accuracy: 0.9000 Epoch 41/200 298/298 [==============================] - 0s 41us/step - loss: 1.0110 - accuracy: 0.9732 - val_loss: 1.0939 - val_accuracy: 0.9000 Epoch 42/200 298/298 [==============================] - 0s 41us/step - loss: 1.0062 - accuracy: 0.9732 - val_loss: 1.0901 - val_accuracy: 0.9000 Epoch 43/200 298/298 [==============================] - 0s 40us/step - loss: 1.0015 - accuracy: 0.9732 - val_loss: 1.0864 - val_accuracy: 0.9000 Epoch 44/200 298/298 [==============================] - 0s 41us/step - loss: 0.9969 - accuracy: 0.9732 - val_loss: 1.0824 - val_accuracy: 0.9000 Epoch 45/200 298/298 [==============================] - 0s 43us/step - loss: 0.9923 - accuracy: 0.9732 - val_loss: 1.0785 - val_accuracy: 0.9000 Epoch 46/200 298/298 [==============================] - 0s 40us/step - loss: 0.9877 - accuracy: 0.9732 - val_loss: 1.0749 - val_accuracy: 0.9000 Epoch 47/200 298/298 [==============================] - 0s 42us/step - loss: 0.9833 - accuracy: 0.9732 - val_loss: 1.0711 - val_accuracy: 0.9000 Epoch 48/200 298/298 [==============================] - 0s 41us/step - loss: 0.9789 - accuracy: 0.9732 - val_loss: 1.0676 - val_accuracy: 0.9000 Epoch 49/200 298/298 [==============================] - 0s 39us/step - loss: 0.9745 - accuracy: 0.9732 - val_loss: 1.0640 - val_accuracy: 0.9100 Epoch 50/200 298/298 [==============================] - 0s 42us/step - loss: 0.9702 - accuracy: 0.9732 - val_loss: 1.0602 - val_accuracy: 0.9100 Epoch 51/200 298/298 [==============================] - 0s 39us/step - loss: 0.9661 - accuracy: 0.9732 - val_loss: 1.0567 - val_accuracy: 0.9100 Epoch 52/200 298/298 [==============================] - 0s 41us/step - loss: 0.9620 - accuracy: 0.9732 - val_loss: 1.0532 - val_accuracy: 0.9100 Epoch 53/200 298/298 [==============================] - 0s 41us/step - loss: 0.9579 - accuracy: 0.9732 - val_loss: 1.0498 - val_accuracy: 0.9100 Epoch 54/200 298/298 [==============================] - 0s 39us/step - loss: 0.9538 - accuracy: 0.9732 - val_loss: 1.0463 - val_accuracy: 0.9100 Epoch 55/200 298/298 [==============================] - 0s 44us/step - loss: 0.9498 - accuracy: 0.9732 - val_loss: 1.0428 - val_accuracy: 0.9100 Epoch 56/200 298/298 [==============================] - 0s 41us/step - loss: 0.9458 - accuracy: 0.9732 - val_loss: 1.0391 - val_accuracy: 0.9100 Epoch 57/200 298/298 [==============================] - 0s 42us/step - loss: 0.9418 - accuracy: 0.9732 - val_loss: 1.0349 - val_accuracy: 0.9100 Epoch 58/200 298/298 [==============================] - 0s 40us/step - loss: 0.9379 - accuracy: 0.9732 - val_loss: 1.0316 - val_accuracy: 0.9100 Epoch 59/200 298/298 [==============================] - 0s 40us/step - loss: 0.9340 - accuracy: 0.9732 - val_loss: 1.0282 - val_accuracy: 0.9100 Epoch 60/200 298/298 [==============================] - 0s 40us/step - loss: 0.9301 - accuracy: 0.9765 - val_loss: 1.0248 - val_accuracy: 0.9100 Epoch 61/200 298/298 [==============================] - 0s 42us/step - loss: 0.9263 - accuracy: 0.9765 - val_loss: 1.0216 - val_accuracy: 0.9100 Epoch 62/200 298/298 [==============================] - 0s 40us/step - loss: 0.9226 - accuracy: 0.9765 - val_loss: 1.0182 - val_accuracy: 0.9100 Epoch 63/200 298/298 [==============================] - 0s 42us/step - loss: 0.9188 - accuracy: 0.9765 - val_loss: 1.0148 - val_accuracy: 0.9100 Epoch 64/200 298/298 [==============================] - 0s 42us/step - loss: 0.9151 - accuracy: 0.9765 - val_loss: 1.0115 - val_accuracy: 0.9100 Epoch 65/200 298/298 [==============================] - 0s 42us/step - loss: 0.9114 - accuracy: 0.9765 - val_loss: 1.0081 - val_accuracy: 0.9100 Epoch 66/200 298/298 [==============================] - 0s 40us/step - loss: 0.9077 - accuracy: 0.9765 - val_loss: 1.0049 - val_accuracy: 0.9100 Epoch 67/200 298/298 [==============================] - 0s 40us/step - loss: 0.9042 - accuracy: 0.9765 - val_loss: 1.0016 - val_accuracy: 0.9100 Epoch 68/200 298/298 [==============================] - 0s 41us/step - loss: 0.9005 - accuracy: 0.9765 - val_loss: 0.9982 - val_accuracy: 0.9100 Epoch 69/200 298/298 [==============================] - 0s 40us/step - loss: 0.8969 - accuracy: 0.9765 - val_loss: 0.9953 - val_accuracy: 0.9100 Epoch 70/200 298/298 [==============================] - 0s 43us/step - loss: 0.8933 - accuracy: 0.9765 - val_loss: 0.9918 - val_accuracy: 0.9100 Epoch 71/200 298/298 [==============================] - 0s 43us/step - loss: 0.8898 - accuracy: 0.9765 - val_loss: 0.9885 - val_accuracy: 0.9100 Epoch 72/200 298/298 [==============================] - 0s 40us/step - loss: 0.8863 - accuracy: 0.9765 - val_loss: 0.9853 - val_accuracy: 0.9100 Epoch 73/200 298/298 [==============================] - 0s 41us/step - loss: 0.8829 - accuracy: 0.9765 - val_loss: 0.9823 - val_accuracy: 0.9100 Epoch 74/200 298/298 [==============================] - 0s 40us/step - loss: 0.8794 - accuracy: 0.9765 - val_loss: 0.9793 - val_accuracy: 0.9100 Epoch 75/200 298/298 [==============================] - 0s 42us/step - loss: 0.8760 - accuracy: 0.9765 - val_loss: 0.9760 - val_accuracy: 0.9100 Epoch 76/200 298/298 [==============================] - 0s 42us/step - loss: 0.8725 - accuracy: 0.9765 - val_loss: 0.9728 - val_accuracy: 0.9100 Epoch 77/200 298/298 [==============================] - 0s 41us/step - loss: 0.8691 - accuracy: 0.9799 - val_loss: 0.9696 - val_accuracy: 0.9100 Epoch 78/200 298/298 [==============================] - 0s 41us/step - loss: 0.8658 - accuracy: 0.9799 - val_loss: 0.9665 - val_accuracy: 0.9100 Epoch 79/200 298/298 [==============================] - 0s 40us/step - loss: 0.8624 - accuracy: 0.9799 - val_loss: 0.9628 - val_accuracy: 0.9100 Epoch 80/200 298/298 [==============================] - 0s 41us/step - loss: 0.8590 - accuracy: 0.9799 - val_loss: 0.9598 - val_accuracy: 0.9100 Epoch 81/200 298/298 [==============================] - 0s 41us/step - loss: 0.8556 - accuracy: 0.9799 - val_loss: 0.9565 - val_accuracy: 0.9100 Epoch 82/200 298/298 [==============================] - 0s 40us/step - loss: 0.8524 - accuracy: 0.9799 - val_loss: 0.9533 - val_accuracy: 0.9100 Epoch 83/200 298/298 [==============================] - 0s 41us/step - loss: 0.8490 - accuracy: 0.9799 - val_loss: 0.9503 - val_accuracy: 0.9100 Epoch 84/200 298/298 [==============================] - 0s 42us/step - loss: 0.8458 - accuracy: 0.9799 - val_loss: 0.9472 - val_accuracy: 0.9100 Epoch 85/200 298/298 [==============================] - 0s 40us/step - loss: 0.8425 - accuracy: 0.9799 - val_loss: 0.9434 - val_accuracy: 0.9100 Epoch 86/200 298/298 [==============================] - 0s 42us/step - loss: 0.8393 - accuracy: 0.9799 - val_loss: 0.9404 - val_accuracy: 0.9100 Epoch 87/200 298/298 [==============================] - 0s 40us/step - loss: 0.8361 - accuracy: 0.9832 - val_loss: 0.9371 - val_accuracy: 0.9100 Epoch 88/200 298/298 [==============================] - 0s 42us/step - loss: 0.8329 - accuracy: 0.9832 - val_loss: 0.9342 - val_accuracy: 0.9100 Epoch 89/200 298/298 [==============================] - 0s 40us/step - loss: 0.8297 - accuracy: 0.9832 - val_loss: 0.9312 - val_accuracy: 0.9100 Epoch 90/200 298/298 [==============================] - 0s 41us/step - loss: 0.8266 - accuracy: 0.9832 - val_loss: 0.9282 - val_accuracy: 0.9100 Epoch 91/200 298/298 [==============================] - 0s 42us/step - loss: 0.8234 - accuracy: 0.9832 - val_loss: 0.9252 - val_accuracy: 0.9200 Epoch 92/200 298/298 [==============================] - 0s 38us/step - loss: 0.8204 - accuracy: 0.9832 - val_loss: 0.9224 - val_accuracy: 0.9200 Epoch 93/200 298/298 [==============================] - 0s 41us/step - loss: 0.8172 - accuracy: 0.9832 - val_loss: 0.9194 - val_accuracy: 0.9200 Epoch 94/200 298/298 [==============================] - 0s 44us/step - loss: 0.8141 - accuracy: 0.9832 - val_loss: 0.9164 - val_accuracy: 0.9200 Epoch 95/200 298/298 [==============================] - 0s 39us/step - loss: 0.8111 - accuracy: 0.9832 - val_loss: 0.9135 - val_accuracy: 0.9200 Epoch 96/200 298/298 [==============================] - 0s 43us/step - loss: 0.8080 - accuracy: 0.9832 - val_loss: 0.9107 - val_accuracy: 0.9200 Epoch 97/200 298/298 [==============================] - 0s 42us/step - loss: 0.8050 - accuracy: 0.9832 - val_loss: 0.9081 - val_accuracy: 0.9200 Epoch 98/200 298/298 [==============================] - 0s 40us/step - loss: 0.8020 - accuracy: 0.9832 - val_loss: 0.9053 - val_accuracy: 0.9200 Epoch 99/200 298/298 [==============================] - 0s 40us/step - loss: 0.7989 - accuracy: 0.9832 - val_loss: 0.9016 - val_accuracy: 0.9200 Epoch 100/200 298/298 [==============================] - 0s 44us/step - loss: 0.7959 - accuracy: 0.9832 - val_loss: 0.8988 - val_accuracy: 0.9200 Epoch 101/200 298/298 [==============================] - 0s 43us/step - loss: 0.7929 - accuracy: 0.9832 - val_loss: 0.8960 - val_accuracy: 0.9200 Epoch 102/200 298/298 [==============================] - 0s 41us/step - loss: 0.7900 - accuracy: 0.9832 - val_loss: 0.8934 - val_accuracy: 0.9200 Epoch 103/200 298/298 [==============================] - 0s 39us/step - loss: 0.7870 - accuracy: 0.9832 - val_loss: 0.8905 - val_accuracy: 0.9200 Epoch 104/200 298/298 [==============================] - 0s 39us/step - loss: 0.7841 - accuracy: 0.9832 - val_loss: 0.8878 - val_accuracy: 0.9200 Epoch 105/200 298/298 [==============================] - 0s 41us/step - loss: 0.7811 - accuracy: 0.9832 - val_loss: 0.8850 - val_accuracy: 0.9200 Epoch 106/200 298/298 [==============================] - 0s 42us/step - loss: 0.7782 - accuracy: 0.9832 - val_loss: 0.8824 - val_accuracy: 0.9200 Epoch 107/200 298/298 [==============================] - 0s 43us/step - loss: 0.7753 - accuracy: 0.9832 - val_loss: 0.8797 - val_accuracy: 0.9200 Epoch 108/200 298/298 [==============================] - 0s 46us/step - loss: 0.7724 - accuracy: 0.9832 - val_loss: 0.8770 - val_accuracy: 0.9200 Epoch 109/200 298/298 [==============================] - 0s 74us/step - loss: 0.7696 - accuracy: 0.9832 - val_loss: 0.8742 - val_accuracy: 0.9200 Epoch 110/200 298/298 [==============================] - 0s 51us/step - loss: 0.7667 - accuracy: 0.9832 - val_loss: 0.8709 - val_accuracy: 0.9200 Epoch 111/200 298/298 [==============================] - 0s 50us/step - loss: 0.7638 - accuracy: 0.9866 - val_loss: 0.8682 - val_accuracy: 0.9200 Epoch 112/200 298/298 [==============================] - 0s 49us/step - loss: 0.7610 - accuracy: 0.9866 - val_loss: 0.8658 - val_accuracy: 0.9200 Epoch 113/200 298/298 [==============================] - 0s 49us/step - loss: 0.7581 - accuracy: 0.9866 - val_loss: 0.8630 - val_accuracy: 0.9200 Epoch 114/200 298/298 [==============================] - 0s 45us/step - loss: 0.7554 - accuracy: 0.9866 - val_loss: 0.8603 - val_accuracy: 0.9200 Epoch 115/200 298/298 [==============================] - 0s 43us/step - loss: 0.7526 - accuracy: 0.9866 - val_loss: 0.8577 - val_accuracy: 0.9200 Epoch 116/200 298/298 [==============================] - 0s 46us/step - loss: 0.7498 - accuracy: 0.9866 - val_loss: 0.8550 - val_accuracy: 0.9200 Epoch 117/200 298/298 [==============================] - 0s 43us/step - loss: 0.7470 - accuracy: 0.9866 - val_loss: 0.8523 - val_accuracy: 0.9200 Epoch 118/200 298/298 [==============================] - 0s 43us/step - loss: 0.7442 - accuracy: 0.9866 - val_loss: 0.8494 - val_accuracy: 0.9200 Epoch 119/200 298/298 [==============================] - 0s 41us/step - loss: 0.7414 - accuracy: 0.9866 - val_loss: 0.8460 - val_accuracy: 0.9200 Epoch 120/200 298/298 [==============================] - 0s 42us/step - loss: 0.7386 - accuracy: 0.9866 - val_loss: 0.8431 - val_accuracy: 0.9200 Epoch 121/200 298/298 [==============================] - 0s 40us/step - loss: 0.7359 - accuracy: 0.9866 - val_loss: 0.8397 - val_accuracy: 0.9200 Epoch 122/200 298/298 [==============================] - 0s 39us/step - loss: 0.7332 - accuracy: 0.9866 - val_loss: 0.8373 - val_accuracy: 0.9200 Epoch 123/200 298/298 [==============================] - 0s 43us/step - loss: 0.7304 - accuracy: 0.9866 - val_loss: 0.8348 - val_accuracy: 0.9200 Epoch 124/200 298/298 [==============================] - 0s 40us/step - loss: 0.7277 - accuracy: 0.9866 - val_loss: 0.8323 - val_accuracy: 0.9200 Epoch 125/200 298/298 [==============================] - 0s 45us/step - loss: 0.7251 - accuracy: 0.9866 - val_loss: 0.8298 - val_accuracy: 0.9200 Epoch 126/200 298/298 [==============================] - 0s 44us/step - loss: 0.7224 - accuracy: 0.9866 - val_loss: 0.8271 - val_accuracy: 0.9200 Epoch 127/200 298/298 [==============================] - 0s 42us/step - loss: 0.7197 - accuracy: 0.9866 - val_loss: 0.8248 - val_accuracy: 0.9200 Epoch 128/200 298/298 [==============================] - 0s 41us/step - loss: 0.7171 - accuracy: 0.9866 - val_loss: 0.8222 - val_accuracy: 0.9200 Epoch 129/200 298/298 [==============================] - 0s 40us/step - loss: 0.7145 - accuracy: 0.9866 - val_loss: 0.8199 - val_accuracy: 0.9200 Epoch 130/200 298/298 [==============================] - 0s 41us/step - loss: 0.7119 - accuracy: 0.9866 - val_loss: 0.8175 - val_accuracy: 0.9200 Epoch 131/200 298/298 [==============================] - 0s 41us/step - loss: 0.7092 - accuracy: 0.9866 - val_loss: 0.8150 - val_accuracy: 0.9200 Epoch 132/200 298/298 [==============================] - 0s 39us/step - loss: 0.7067 - accuracy: 0.9866 - val_loss: 0.8126 - val_accuracy: 0.9200 Epoch 133/200 298/298 [==============================] - 0s 41us/step - loss: 0.7041 - accuracy: 0.9866 - val_loss: 0.8099 - val_accuracy: 0.9200 Epoch 134/200 298/298 [==============================] - 0s 40us/step - loss: 0.7015 - accuracy: 0.9899 - val_loss: 0.8079 - val_accuracy: 0.9200 Epoch 135/200 298/298 [==============================] - 0s 40us/step - loss: 0.6989 - accuracy: 0.9899 - val_loss: 0.8055 - val_accuracy: 0.9200 Epoch 136/200 298/298 [==============================] - 0s 43us/step - loss: 0.6963 - accuracy: 0.9899 - val_loss: 0.8024 - val_accuracy: 0.9200 Epoch 137/200 298/298 [==============================] - 0s 40us/step - loss: 0.6938 - accuracy: 0.9899 - val_loss: 0.8000 - val_accuracy: 0.9200 Epoch 138/200 298/298 [==============================] - 0s 43us/step - loss: 0.6913 - accuracy: 0.9899 - val_loss: 0.7977 - val_accuracy: 0.9200 Epoch 139/200 298/298 [==============================] - 0s 42us/step - loss: 0.6887 - accuracy: 0.9899 - val_loss: 0.7954 - val_accuracy: 0.9200 Epoch 140/200 298/298 [==============================] - 0s 40us/step - loss: 0.6862 - accuracy: 0.9899 - val_loss: 0.7922 - val_accuracy: 0.9200 Epoch 141/200 298/298 [==============================] - 0s 41us/step - loss: 0.6837 - accuracy: 0.9899 - val_loss: 0.7899 - val_accuracy: 0.9200 Epoch 142/200 298/298 [==============================] - 0s 43us/step - loss: 0.6812 - accuracy: 0.9899 - val_loss: 0.7876 - val_accuracy: 0.9200 Epoch 143/200 298/298 [==============================] - 0s 41us/step - loss: 0.6787 - accuracy: 0.9899 - val_loss: 0.7853 - val_accuracy: 0.9300 Epoch 144/200 298/298 [==============================] - 0s 41us/step - loss: 0.6763 - accuracy: 0.9899 - val_loss: 0.7830 - val_accuracy: 0.9300 Epoch 145/200 298/298 [==============================] - 0s 40us/step - loss: 0.6738 - accuracy: 0.9899 - val_loss: 0.7806 - val_accuracy: 0.9300 Epoch 146/200 298/298 [==============================] - 0s 41us/step - loss: 0.6714 - accuracy: 0.9899 - val_loss: 0.7774 - val_accuracy: 0.9300 Epoch 147/200 298/298 [==============================] - 0s 41us/step - loss: 0.6689 - accuracy: 0.9899 - val_loss: 0.7752 - val_accuracy: 0.9400 Epoch 148/200 298/298 [==============================] - 0s 40us/step - loss: 0.6664 - accuracy: 0.9899 - val_loss: 0.7725 - val_accuracy: 0.9400 Epoch 149/200 298/298 [==============================] - 0s 40us/step - loss: 0.6640 - accuracy: 0.9899 - val_loss: 0.7704 - val_accuracy: 0.9400 Epoch 150/200 298/298 [==============================] - 0s 39us/step - loss: 0.6616 - accuracy: 0.9899 - val_loss: 0.7684 - val_accuracy: 0.9400 Epoch 151/200 298/298 [==============================] - 0s 41us/step - loss: 0.6592 - accuracy: 0.9899 - val_loss: 0.7661 - val_accuracy: 0.9400 Epoch 152/200 298/298 [==============================] - 0s 41us/step - loss: 0.6568 - accuracy: 0.9899 - val_loss: 0.7639 - val_accuracy: 0.9400 Epoch 153/200 298/298 [==============================] - 0s 42us/step - loss: 0.6544 - accuracy: 0.9899 - val_loss: 0.7617 - val_accuracy: 0.9400 Epoch 154/200 298/298 [==============================] - 0s 40us/step - loss: 0.6521 - accuracy: 0.9899 - val_loss: 0.7587 - val_accuracy: 0.9400 Epoch 155/200 298/298 [==============================] - 0s 42us/step - loss: 0.6497 - accuracy: 0.9899 - val_loss: 0.7564 - val_accuracy: 0.9400 Epoch 156/200 298/298 [==============================] - 0s 42us/step - loss: 0.6473 - accuracy: 0.9899 - val_loss: 0.7538 - val_accuracy: 0.9400 Epoch 157/200 298/298 [==============================] - 0s 46us/step - loss: 0.6450 - accuracy: 0.9899 - val_loss: 0.7518 - val_accuracy: 0.9400 Epoch 158/200 298/298 [==============================] - 0s 41us/step - loss: 0.6427 - accuracy: 0.9899 - val_loss: 0.7497 - val_accuracy: 0.9400 Epoch 159/200 298/298 [==============================] - 0s 40us/step - loss: 0.6404 - accuracy: 0.9899 - val_loss: 0.7475 - val_accuracy: 0.9400 Epoch 160/200 298/298 [==============================] - 0s 40us/step - loss: 0.6381 - accuracy: 0.9899 - val_loss: 0.7451 - val_accuracy: 0.9400 Epoch 161/200 298/298 [==============================] - 0s 40us/step - loss: 0.6358 - accuracy: 0.9899 - val_loss: 0.7431 - val_accuracy: 0.9400 Epoch 162/200 298/298 [==============================] - 0s 40us/step - loss: 0.6334 - accuracy: 0.9899 - val_loss: 0.7410 - val_accuracy: 0.9400 Epoch 163/200 298/298 [==============================] - 0s 41us/step - loss: 0.6311 - accuracy: 0.9899 - val_loss: 0.7385 - val_accuracy: 0.9400 Epoch 164/200 298/298 [==============================] - 0s 40us/step - loss: 0.6289 - accuracy: 0.9899 - val_loss: 0.7365 - val_accuracy: 0.9400 Epoch 165/200 298/298 [==============================] - 0s 41us/step - loss: 0.6266 - accuracy: 0.9899 - val_loss: 0.7347 - val_accuracy: 0.9400 Epoch 166/200 298/298 [==============================] - 0s 40us/step - loss: 0.6243 - accuracy: 0.9899 - val_loss: 0.7327 - val_accuracy: 0.9400 Epoch 167/200 298/298 [==============================] - 0s 42us/step - loss: 0.6221 - accuracy: 0.9899 - val_loss: 0.7306 - val_accuracy: 0.9400 Epoch 168/200 298/298 [==============================] - 0s 40us/step - loss: 0.6199 - accuracy: 0.9899 - val_loss: 0.7286 - val_accuracy: 0.9400 Epoch 169/200 298/298 [==============================] - 0s 44us/step - loss: 0.6177 - accuracy: 0.9899 - val_loss: 0.7266 - val_accuracy: 0.9400 Epoch 170/200 298/298 [==============================] - 0s 44us/step - loss: 0.6154 - accuracy: 0.9899 - val_loss: 0.7246 - val_accuracy: 0.9400 Epoch 171/200 298/298 [==============================] - 0s 45us/step - loss: 0.6132 - accuracy: 0.9899 - val_loss: 0.7229 - val_accuracy: 0.9400 Epoch 172/200 298/298 [==============================] - 0s 42us/step - loss: 0.6110 - accuracy: 0.9899 - val_loss: 0.7208 - val_accuracy: 0.9400 Epoch 173/200 298/298 [==============================] - 0s 41us/step - loss: 0.6088 - accuracy: 0.9899 - val_loss: 0.7187 - val_accuracy: 0.9400 Epoch 174/200 298/298 [==============================] - 0s 41us/step - loss: 0.6067 - accuracy: 0.9899 - val_loss: 0.7166 - val_accuracy: 0.9400 Epoch 175/200 298/298 [==============================] - 0s 42us/step - loss: 0.6045 - accuracy: 0.9899 - val_loss: 0.7146 - val_accuracy: 0.9400 Epoch 176/200 298/298 [==============================] - 0s 41us/step - loss: 0.6024 - accuracy: 0.9899 - val_loss: 0.7127 - val_accuracy: 0.9400 Epoch 177/200 298/298 [==============================] - 0s 41us/step - loss: 0.6003 - accuracy: 0.9899 - val_loss: 0.7107 - val_accuracy: 0.9400 Epoch 178/200 298/298 [==============================] - 0s 42us/step - loss: 0.5981 - accuracy: 0.9899 - val_loss: 0.7087 - val_accuracy: 0.9400 Epoch 179/200 298/298 [==============================] - 0s 42us/step - loss: 0.5960 - accuracy: 0.9899 - val_loss: 0.7066 - val_accuracy: 0.9400 Epoch 180/200 298/298 [==============================] - 0s 41us/step - loss: 0.5939 - accuracy: 0.9899 - val_loss: 0.7047 - val_accuracy: 0.9400 Epoch 181/200 298/298 [==============================] - 0s 40us/step - loss: 0.5918 - accuracy: 0.9933 - val_loss: 0.7026 - val_accuracy: 0.9400 Epoch 182/200 298/298 [==============================] - 0s 41us/step - loss: 0.5897 - accuracy: 0.9899 - val_loss: 0.7006 - val_accuracy: 0.9400 Epoch 183/200 298/298 [==============================] - 0s 44us/step - loss: 0.5876 - accuracy: 0.9899 - val_loss: 0.6989 - val_accuracy: 0.9400 Epoch 184/200 298/298 [==============================] - 0s 42us/step - loss: 0.5855 - accuracy: 0.9933 - val_loss: 0.6972 - val_accuracy: 0.9400 Epoch 185/200 298/298 [==============================] - 0s 40us/step - loss: 0.5834 - accuracy: 0.9933 - val_loss: 0.6952 - val_accuracy: 0.9400 Epoch 186/200 298/298 [==============================] - 0s 40us/step - loss: 0.5813 - accuracy: 0.9899 - val_loss: 0.6933 - val_accuracy: 0.9400 Epoch 187/200 298/298 [==============================] - 0s 42us/step - loss: 0.5793 - accuracy: 0.9933 - val_loss: 0.6913 - val_accuracy: 0.9400 Epoch 188/200 298/298 [==============================] - 0s 45us/step - loss: 0.5772 - accuracy: 0.9933 - val_loss: 0.6893 - val_accuracy: 0.9400 Epoch 189/200 298/298 [==============================] - 0s 43us/step - loss: 0.5752 - accuracy: 0.9933 - val_loss: 0.6871 - val_accuracy: 0.9400 Epoch 190/200 298/298 [==============================] - 0s 40us/step - loss: 0.5731 - accuracy: 0.9933 - val_loss: 0.6852 - val_accuracy: 0.9400 Epoch 191/200 298/298 [==============================] - 0s 41us/step - loss: 0.5711 - accuracy: 0.9933 - val_loss: 0.6833 - val_accuracy: 0.9400 Epoch 192/200 298/298 [==============================] - 0s 41us/step - loss: 0.5691 - accuracy: 0.9933 - val_loss: 0.6814 - val_accuracy: 0.9400 Epoch 193/200 298/298 [==============================] - 0s 41us/step - loss: 0.5671 - accuracy: 0.9933 - val_loss: 0.6783 - val_accuracy: 0.9400 Epoch 194/200 298/298 [==============================] - 0s 42us/step - loss: 0.5651 - accuracy: 0.9933 - val_loss: 0.6765 - val_accuracy: 0.9400 Epoch 195/200 298/298 [==============================] - 0s 41us/step - loss: 0.5631 - accuracy: 0.9933 - val_loss: 0.6749 - val_accuracy: 0.9400 Epoch 196/200 298/298 [==============================] - 0s 40us/step - loss: 0.5611 - accuracy: 0.9933 - val_loss: 0.6728 - val_accuracy: 0.9400 Epoch 197/200 298/298 [==============================] - 0s 40us/step - loss: 0.5591 - accuracy: 0.9933 - val_loss: 0.6711 - val_accuracy: 0.9400 Epoch 198/200 298/298 [==============================] - 0s 42us/step - loss: 0.5572 - accuracy: 0.9933 - val_loss: 0.6693 - val_accuracy: 0.9400 Epoch 199/200 298/298 [==============================] - 0s 42us/step - loss: 0.5552 - accuracy: 0.9933 - val_loss: 0.6674 - val_accuracy: 0.9400 Epoch 200/200 298/298 [==============================] - 0s 43us/step - loss: 0.5533 - accuracy: 0.9933 - val_loss: 0.6656 - val_accuracy: 0.9400 171/171 [==============================] - 0s 20us/step Optimizers: <keras.optimizers.SGD object at 0x1463bb710> Epoch Sizes: 200 Neurons or Units: 64 ['loss', 'accuracy'] [0.5696304911061337, 0.9766082167625427] Test score: 0.5696304911061337 Test accuracy: 0.9766082167625427 Model: "sequential_35" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_103 (Dense) (None, 128) 3968 _________________________________________________________________ activation_103 (Activation) (None, 128) 0 _________________________________________________________________ dense_104 (Dense) (None, 128) 16512 _________________________________________________________________ activation_104 (Activation) (None, 128) 0 _________________________________________________________________ dense_105 (Dense) (None, 1) 129 _________________________________________________________________ activation_105 (Activation) (None, 1) 0 ================================================================= Total params: 20,609 Trainable params: 20,609 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/200 298/298 [==============================] - 0s 459us/step - loss: 2.4756 - accuracy: 0.4899 - val_loss: 2.3962 - val_accuracy: 0.7300 Epoch 2/200 298/298 [==============================] - 0s 43us/step - loss: 2.3738 - accuracy: 0.7987 - val_loss: 2.3121 - val_accuracy: 0.9000 Epoch 3/200 298/298 [==============================] - 0s 44us/step - loss: 2.2950 - accuracy: 0.9094 - val_loss: 2.2468 - val_accuracy: 0.9100 Epoch 4/200 298/298 [==============================] - 0s 43us/step - loss: 2.2322 - accuracy: 0.9295 - val_loss: 2.1918 - val_accuracy: 0.9100 Epoch 5/200 298/298 [==============================] - 0s 43us/step - loss: 2.1771 - accuracy: 0.9430 - val_loss: 2.1472 - val_accuracy: 0.9200 Epoch 6/200 298/298 [==============================] - 0s 43us/step - loss: 2.1320 - accuracy: 0.9530 - val_loss: 2.1085 - val_accuracy: 0.9200 Epoch 7/200 298/298 [==============================] - 0s 43us/step - loss: 2.0918 - accuracy: 0.9564 - val_loss: 2.0760 - val_accuracy: 0.9200 Epoch 8/200 298/298 [==============================] - 0s 46us/step - loss: 2.0574 - accuracy: 0.9564 - val_loss: 2.0471 - val_accuracy: 0.9200 Epoch 9/200 298/298 [==============================] - 0s 44us/step - loss: 2.0260 - accuracy: 0.9597 - val_loss: 2.0214 - val_accuracy: 0.9200 Epoch 10/200 298/298 [==============================] - 0s 44us/step - loss: 1.9986 - accuracy: 0.9597 - val_loss: 1.9998 - val_accuracy: 0.9100 Epoch 11/200 298/298 [==============================] - 0s 42us/step - loss: 1.9740 - accuracy: 0.9597 - val_loss: 1.9792 - val_accuracy: 0.9200 Epoch 12/200 298/298 [==============================] - 0s 42us/step - loss: 1.9517 - accuracy: 0.9597 - val_loss: 1.9614 - val_accuracy: 0.9100 Epoch 13/200 298/298 [==============================] - 0s 43us/step - loss: 1.9313 - accuracy: 0.9597 - val_loss: 1.9461 - val_accuracy: 0.9100 Epoch 14/200 298/298 [==============================] - 0s 44us/step - loss: 1.9134 - accuracy: 0.9597 - val_loss: 1.9310 - val_accuracy: 0.9100 Epoch 15/200 298/298 [==============================] - 0s 45us/step - loss: 1.8960 - accuracy: 0.9597 - val_loss: 1.9169 - val_accuracy: 0.9100 Epoch 16/200 298/298 [==============================] - 0s 46us/step - loss: 1.8798 - accuracy: 0.9597 - val_loss: 1.9040 - val_accuracy: 0.9100 Epoch 17/200 298/298 [==============================] - 0s 45us/step - loss: 1.8646 - accuracy: 0.9597 - val_loss: 1.8922 - val_accuracy: 0.9100 Epoch 18/200 298/298 [==============================] - 0s 53us/step - loss: 1.8503 - accuracy: 0.9631 - val_loss: 1.8811 - val_accuracy: 0.9100 Epoch 19/200 298/298 [==============================] - 0s 43us/step - loss: 1.8368 - accuracy: 0.9664 - val_loss: 1.8704 - val_accuracy: 0.9000 Epoch 20/200 298/298 [==============================] - 0s 44us/step - loss: 1.8241 - accuracy: 0.9631 - val_loss: 1.8616 - val_accuracy: 0.9000 Epoch 21/200 298/298 [==============================] - 0s 43us/step - loss: 1.8122 - accuracy: 0.9664 - val_loss: 1.8515 - val_accuracy: 0.9000 Epoch 22/200 298/298 [==============================] - 0s 41us/step - loss: 1.8005 - accuracy: 0.9698 - val_loss: 1.8423 - val_accuracy: 0.9000 Epoch 23/200 298/298 [==============================] - 0s 43us/step - loss: 1.7892 - accuracy: 0.9698 - val_loss: 1.8333 - val_accuracy: 0.9000 Epoch 24/200 298/298 [==============================] - 0s 43us/step - loss: 1.7784 - accuracy: 0.9698 - val_loss: 1.8246 - val_accuracy: 0.9000 Epoch 25/200 298/298 [==============================] - 0s 42us/step - loss: 1.7680 - accuracy: 0.9765 - val_loss: 1.8163 - val_accuracy: 0.9000 Epoch 26/200 298/298 [==============================] - 0s 43us/step - loss: 1.7580 - accuracy: 0.9765 - val_loss: 1.8082 - val_accuracy: 0.9000 Epoch 27/200 298/298 [==============================] - 0s 43us/step - loss: 1.7481 - accuracy: 0.9765 - val_loss: 1.8001 - val_accuracy: 0.9000 Epoch 28/200 298/298 [==============================] - 0s 42us/step - loss: 1.7386 - accuracy: 0.9765 - val_loss: 1.7925 - val_accuracy: 0.9000 Epoch 29/200 298/298 [==============================] - 0s 45us/step - loss: 1.7293 - accuracy: 0.9765 - val_loss: 1.7850 - val_accuracy: 0.9000 Epoch 30/200 298/298 [==============================] - 0s 46us/step - loss: 1.7203 - accuracy: 0.9765 - val_loss: 1.7772 - val_accuracy: 0.9000 Epoch 31/200 298/298 [==============================] - 0s 48us/step - loss: 1.7113 - accuracy: 0.9765 - val_loss: 1.7698 - val_accuracy: 0.9000 Epoch 32/200 298/298 [==============================] - 0s 48us/step - loss: 1.7026 - accuracy: 0.9799 - val_loss: 1.7623 - val_accuracy: 0.9000 Epoch 33/200 298/298 [==============================] - 0s 45us/step - loss: 1.6941 - accuracy: 0.9799 - val_loss: 1.7551 - val_accuracy: 0.9000 Epoch 34/200 298/298 [==============================] - 0s 44us/step - loss: 1.6858 - accuracy: 0.9799 - val_loss: 1.7479 - val_accuracy: 0.9000 Epoch 35/200 298/298 [==============================] - 0s 45us/step - loss: 1.6776 - accuracy: 0.9799 - val_loss: 1.7408 - val_accuracy: 0.9000 Epoch 36/200 298/298 [==============================] - 0s 43us/step - loss: 1.6694 - accuracy: 0.9799 - val_loss: 1.7338 - val_accuracy: 0.9000 Epoch 37/200 298/298 [==============================] - 0s 42us/step - loss: 1.6615 - accuracy: 0.9799 - val_loss: 1.7270 - val_accuracy: 0.9000 Epoch 38/200 298/298 [==============================] - 0s 43us/step - loss: 1.6538 - accuracy: 0.9799 - val_loss: 1.7198 - val_accuracy: 0.9000 Epoch 39/200 298/298 [==============================] - 0s 48us/step - loss: 1.6460 - accuracy: 0.9799 - val_loss: 1.7133 - val_accuracy: 0.9000 Epoch 40/200 298/298 [==============================] - 0s 43us/step - loss: 1.6383 - accuracy: 0.9799 - val_loss: 1.7066 - val_accuracy: 0.9000 Epoch 41/200 298/298 [==============================] - 0s 44us/step - loss: 1.6309 - accuracy: 0.9799 - val_loss: 1.7001 - val_accuracy: 0.9100 Epoch 42/200 298/298 [==============================] - 0s 43us/step - loss: 1.6235 - accuracy: 0.9799 - val_loss: 1.6937 - val_accuracy: 0.9100 Epoch 43/200 298/298 [==============================] - 0s 43us/step - loss: 1.6161 - accuracy: 0.9799 - val_loss: 1.6872 - val_accuracy: 0.9100 Epoch 44/200 298/298 [==============================] - 0s 43us/step - loss: 1.6089 - accuracy: 0.9799 - val_loss: 1.6808 - val_accuracy: 0.9100 Epoch 45/200 298/298 [==============================] - 0s 45us/step - loss: 1.6018 - accuracy: 0.9799 - val_loss: 1.6745 - val_accuracy: 0.9100 Epoch 46/200 298/298 [==============================] - 0s 46us/step - loss: 1.5948 - accuracy: 0.9799 - val_loss: 1.6679 - val_accuracy: 0.9100 Epoch 47/200 298/298 [==============================] - 0s 45us/step - loss: 1.5877 - accuracy: 0.9799 - val_loss: 1.6616 - val_accuracy: 0.9100 Epoch 48/200 298/298 [==============================] - 0s 48us/step - loss: 1.5809 - accuracy: 0.9799 - val_loss: 1.6556 - val_accuracy: 0.9100 Epoch 49/200 298/298 [==============================] - 0s 45us/step - loss: 1.5740 - accuracy: 0.9799 - val_loss: 1.6494 - val_accuracy: 0.9100 Epoch 50/200 298/298 [==============================] - 0s 44us/step - loss: 1.5673 - accuracy: 0.9799 - val_loss: 1.6431 - val_accuracy: 0.9100 Epoch 51/200 298/298 [==============================] - 0s 43us/step - loss: 1.5604 - accuracy: 0.9799 - val_loss: 1.6371 - val_accuracy: 0.9200 Epoch 52/200 298/298 [==============================] - 0s 44us/step - loss: 1.5538 - accuracy: 0.9799 - val_loss: 1.6309 - val_accuracy: 0.9200 Epoch 53/200 298/298 [==============================] - 0s 43us/step - loss: 1.5472 - accuracy: 0.9799 - val_loss: 1.6250 - val_accuracy: 0.9200 Epoch 54/200 298/298 [==============================] - 0s 43us/step - loss: 1.5407 - accuracy: 0.9799 - val_loss: 1.6185 - val_accuracy: 0.9200 Epoch 55/200 298/298 [==============================] - 0s 42us/step - loss: 1.5341 - accuracy: 0.9799 - val_loss: 1.6126 - val_accuracy: 0.9200 Epoch 56/200 298/298 [==============================] - 0s 44us/step - loss: 1.5278 - accuracy: 0.9799 - val_loss: 1.6065 - val_accuracy: 0.9200 Epoch 57/200 298/298 [==============================] - 0s 43us/step - loss: 1.5214 - accuracy: 0.9799 - val_loss: 1.6007 - val_accuracy: 0.9200 Epoch 58/200 298/298 [==============================] - 0s 42us/step - loss: 1.5151 - accuracy: 0.9799 - val_loss: 1.5948 - val_accuracy: 0.9200 Epoch 59/200 298/298 [==============================] - 0s 44us/step - loss: 1.5088 - accuracy: 0.9799 - val_loss: 1.5889 - val_accuracy: 0.9200 Epoch 60/200 298/298 [==============================] - 0s 46us/step - loss: 1.5025 - accuracy: 0.9799 - val_loss: 1.5831 - val_accuracy: 0.9200 Epoch 61/200 298/298 [==============================] - 0s 44us/step - loss: 1.4964 - accuracy: 0.9799 - val_loss: 1.5777 - val_accuracy: 0.9200 Epoch 62/200 298/298 [==============================] - 0s 44us/step - loss: 1.4902 - accuracy: 0.9832 - val_loss: 1.5717 - val_accuracy: 0.9200 Epoch 63/200 298/298 [==============================] - 0s 41us/step - loss: 1.4841 - accuracy: 0.9866 - val_loss: 1.5661 - val_accuracy: 0.9200 Epoch 64/200 298/298 [==============================] - 0s 44us/step - loss: 1.4780 - accuracy: 0.9866 - val_loss: 1.5604 - val_accuracy: 0.9300 Epoch 65/200 298/298 [==============================] - 0s 46us/step - loss: 1.4720 - accuracy: 0.9866 - val_loss: 1.5548 - val_accuracy: 0.9300 Epoch 66/200 298/298 [==============================] - 0s 45us/step - loss: 1.4660 - accuracy: 0.9866 - val_loss: 1.5493 - val_accuracy: 0.9300 Epoch 67/200 298/298 [==============================] - 0s 46us/step - loss: 1.4600 - accuracy: 0.9866 - val_loss: 1.5437 - val_accuracy: 0.9300 Epoch 68/200 298/298 [==============================] - 0s 45us/step - loss: 1.4542 - accuracy: 0.9866 - val_loss: 1.5383 - val_accuracy: 0.9300 Epoch 69/200 298/298 [==============================] - 0s 42us/step - loss: 1.4482 - accuracy: 0.9866 - val_loss: 1.5328 - val_accuracy: 0.9300 Epoch 70/200 298/298 [==============================] - 0s 44us/step - loss: 1.4424 - accuracy: 0.9866 - val_loss: 1.5274 - val_accuracy: 0.9300 Epoch 71/200 298/298 [==============================] - 0s 44us/step - loss: 1.4366 - accuracy: 0.9866 - val_loss: 1.5219 - val_accuracy: 0.9300 Epoch 72/200 298/298 [==============================] - 0s 43us/step - loss: 1.4308 - accuracy: 0.9866 - val_loss: 1.5168 - val_accuracy: 0.9300 Epoch 73/200 298/298 [==============================] - 0s 44us/step - loss: 1.4251 - accuracy: 0.9866 - val_loss: 1.5113 - val_accuracy: 0.9300 Epoch 74/200 298/298 [==============================] - 0s 44us/step - loss: 1.4194 - accuracy: 0.9866 - val_loss: 1.5063 - val_accuracy: 0.9300 Epoch 75/200 298/298 [==============================] - 0s 51us/step - loss: 1.4137 - accuracy: 0.9866 - val_loss: 1.5007 - val_accuracy: 0.9300 Epoch 76/200 298/298 [==============================] - 0s 42us/step - loss: 1.4081 - accuracy: 0.9866 - val_loss: 1.4955 - val_accuracy: 0.9300 Epoch 77/200 298/298 [==============================] - 0s 42us/step - loss: 1.4025 - accuracy: 0.9866 - val_loss: 1.4905 - val_accuracy: 0.9300 Epoch 78/200 298/298 [==============================] - 0s 44us/step - loss: 1.3969 - accuracy: 0.9866 - val_loss: 1.4852 - val_accuracy: 0.9300 Epoch 79/200 298/298 [==============================] - 0s 45us/step - loss: 1.3913 - accuracy: 0.9866 - val_loss: 1.4800 - val_accuracy: 0.9300 Epoch 80/200 298/298 [==============================] - 0s 45us/step - loss: 1.3858 - accuracy: 0.9866 - val_loss: 1.4746 - val_accuracy: 0.9300 Epoch 81/200 298/298 [==============================] - 0s 45us/step - loss: 1.3803 - accuracy: 0.9866 - val_loss: 1.4693 - val_accuracy: 0.9300 Epoch 82/200 298/298 [==============================] - 0s 44us/step - loss: 1.3749 - accuracy: 0.9866 - val_loss: 1.4641 - val_accuracy: 0.9300 Epoch 83/200 298/298 [==============================] - 0s 44us/step - loss: 1.3695 - accuracy: 0.9866 - val_loss: 1.4590 - val_accuracy: 0.9300 Epoch 84/200 298/298 [==============================] - 0s 44us/step - loss: 1.3641 - accuracy: 0.9866 - val_loss: 1.4540 - val_accuracy: 0.9300 Epoch 85/200 298/298 [==============================] - 0s 45us/step - loss: 1.3587 - accuracy: 0.9866 - val_loss: 1.4489 - val_accuracy: 0.9300 Epoch 86/200 298/298 [==============================] - 0s 45us/step - loss: 1.3534 - accuracy: 0.9866 - val_loss: 1.4439 - val_accuracy: 0.9300 Epoch 87/200 298/298 [==============================] - 0s 44us/step - loss: 1.3481 - accuracy: 0.9866 - val_loss: 1.4389 - val_accuracy: 0.9300 Epoch 88/200 298/298 [==============================] - 0s 44us/step - loss: 1.3428 - accuracy: 0.9866 - val_loss: 1.4337 - val_accuracy: 0.9300 Epoch 89/200 298/298 [==============================] - 0s 44us/step - loss: 1.3375 - accuracy: 0.9866 - val_loss: 1.4285 - val_accuracy: 0.9300 Epoch 90/200 298/298 [==============================] - 0s 44us/step - loss: 1.3322 - accuracy: 0.9866 - val_loss: 1.4237 - val_accuracy: 0.9300 Epoch 91/200 298/298 [==============================] - 0s 45us/step - loss: 1.3270 - accuracy: 0.9866 - val_loss: 1.4189 - val_accuracy: 0.9300 Epoch 92/200 298/298 [==============================] - 0s 42us/step - loss: 1.3218 - accuracy: 0.9866 - val_loss: 1.4140 - val_accuracy: 0.9300 Epoch 93/200 298/298 [==============================] - 0s 44us/step - loss: 1.3166 - accuracy: 0.9866 - val_loss: 1.4091 - val_accuracy: 0.9300 Epoch 94/200 298/298 [==============================] - 0s 43us/step - loss: 1.3116 - accuracy: 0.9866 - val_loss: 1.4043 - val_accuracy: 0.9300 Epoch 95/200 298/298 [==============================] - 0s 43us/step - loss: 1.3064 - accuracy: 0.9866 - val_loss: 1.3993 - val_accuracy: 0.9300 Epoch 96/200 298/298 [==============================] - 0s 46us/step - loss: 1.3014 - accuracy: 0.9866 - val_loss: 1.3945 - val_accuracy: 0.9300 Epoch 97/200 298/298 [==============================] - 0s 48us/step - loss: 1.2963 - accuracy: 0.9866 - val_loss: 1.3897 - val_accuracy: 0.9300 Epoch 98/200 298/298 [==============================] - 0s 45us/step - loss: 1.2913 - accuracy: 0.9866 - val_loss: 1.3847 - val_accuracy: 0.9200 Epoch 99/200 298/298 [==============================] - 0s 46us/step - loss: 1.2862 - accuracy: 0.9866 - val_loss: 1.3799 - val_accuracy: 0.9200 Epoch 100/200 298/298 [==============================] - 0s 43us/step - loss: 1.2812 - accuracy: 0.9866 - val_loss: 1.3751 - val_accuracy: 0.9200 Epoch 101/200 298/298 [==============================] - 0s 44us/step - loss: 1.2763 - accuracy: 0.9866 - val_loss: 1.3703 - val_accuracy: 0.9200 Epoch 102/200 298/298 [==============================] - 0s 42us/step - loss: 1.2713 - accuracy: 0.9866 - val_loss: 1.3657 - val_accuracy: 0.9200 Epoch 103/200 298/298 [==============================] - 0s 43us/step - loss: 1.2664 - accuracy: 0.9866 - val_loss: 1.3610 - val_accuracy: 0.9200 Epoch 104/200 298/298 [==============================] - 0s 46us/step - loss: 1.2615 - accuracy: 0.9866 - val_loss: 1.3564 - val_accuracy: 0.9200 Epoch 105/200 298/298 [==============================] - 0s 47us/step - loss: 1.2567 - accuracy: 0.9866 - val_loss: 1.3518 - val_accuracy: 0.9200 Epoch 106/200 298/298 [==============================] - 0s 49us/step - loss: 1.2518 - accuracy: 0.9866 - val_loss: 1.3474 - val_accuracy: 0.9200 Epoch 107/200 298/298 [==============================] - 0s 46us/step - loss: 1.2470 - accuracy: 0.9866 - val_loss: 1.3429 - val_accuracy: 0.9200 Epoch 108/200 298/298 [==============================] - 0s 45us/step - loss: 1.2422 - accuracy: 0.9866 - val_loss: 1.3383 - val_accuracy: 0.9200 Epoch 109/200 298/298 [==============================] - 0s 45us/step - loss: 1.2374 - accuracy: 0.9866 - val_loss: 1.3340 - val_accuracy: 0.9200 Epoch 110/200 298/298 [==============================] - 0s 44us/step - loss: 1.2326 - accuracy: 0.9866 - val_loss: 1.3296 - val_accuracy: 0.9200 Epoch 111/200 298/298 [==============================] - 0s 45us/step - loss: 1.2279 - accuracy: 0.9866 - val_loss: 1.3250 - val_accuracy: 0.9200 Epoch 112/200 298/298 [==============================] - 0s 42us/step - loss: 1.2232 - accuracy: 0.9866 - val_loss: 1.3205 - val_accuracy: 0.9200 Epoch 113/200 298/298 [==============================] - 0s 46us/step - loss: 1.2185 - accuracy: 0.9866 - val_loss: 1.3159 - val_accuracy: 0.9200 Epoch 114/200 298/298 [==============================] - 0s 47us/step - loss: 1.2138 - accuracy: 0.9866 - val_loss: 1.3114 - val_accuracy: 0.9200 Epoch 115/200 298/298 [==============================] - 0s 49us/step - loss: 1.2092 - accuracy: 0.9866 - val_loss: 1.3069 - val_accuracy: 0.9200 Epoch 116/200 298/298 [==============================] - 0s 42us/step - loss: 1.2045 - accuracy: 0.9866 - val_loss: 1.3024 - val_accuracy: 0.9200 Epoch 117/200 298/298 [==============================] - 0s 44us/step - loss: 1.1999 - accuracy: 0.9866 - val_loss: 1.2980 - val_accuracy: 0.9200 Epoch 118/200 298/298 [==============================] - 0s 44us/step - loss: 1.1953 - accuracy: 0.9866 - val_loss: 1.2936 - val_accuracy: 0.9200 Epoch 119/200 298/298 [==============================] - 0s 42us/step - loss: 1.1907 - accuracy: 0.9866 - val_loss: 1.2893 - val_accuracy: 0.9200 Epoch 120/200 298/298 [==============================] - 0s 43us/step - loss: 1.1861 - accuracy: 0.9866 - val_loss: 1.2851 - val_accuracy: 0.9200 Epoch 121/200 298/298 [==============================] - 0s 42us/step - loss: 1.1816 - accuracy: 0.9866 - val_loss: 1.2810 - val_accuracy: 0.9200 Epoch 122/200 298/298 [==============================] - 0s 42us/step - loss: 1.1771 - accuracy: 0.9866 - val_loss: 1.2767 - val_accuracy: 0.9200 Epoch 123/200 298/298 [==============================] - 0s 42us/step - loss: 1.1725 - accuracy: 0.9866 - val_loss: 1.2726 - val_accuracy: 0.9200 Epoch 124/200 298/298 [==============================] - 0s 45us/step - loss: 1.1681 - accuracy: 0.9866 - val_loss: 1.2683 - val_accuracy: 0.9200 Epoch 125/200 298/298 [==============================] - 0s 43us/step - loss: 1.1636 - accuracy: 0.9866 - val_loss: 1.2644 - val_accuracy: 0.9200 Epoch 126/200 298/298 [==============================] - 0s 45us/step - loss: 1.1592 - accuracy: 0.9866 - val_loss: 1.2603 - val_accuracy: 0.9200 Epoch 127/200 298/298 [==============================] - 0s 44us/step - loss: 1.1547 - accuracy: 0.9866 - val_loss: 1.2562 - val_accuracy: 0.9200 Epoch 128/200 298/298 [==============================] - 0s 47us/step - loss: 1.1503 - accuracy: 0.9866 - val_loss: 1.2520 - val_accuracy: 0.9200 Epoch 129/200 298/298 [==============================] - 0s 45us/step - loss: 1.1460 - accuracy: 0.9866 - val_loss: 1.2478 - val_accuracy: 0.9200 Epoch 130/200 298/298 [==============================] - 0s 45us/step - loss: 1.1416 - accuracy: 0.9866 - val_loss: 1.2432 - val_accuracy: 0.9200 Epoch 131/200 298/298 [==============================] - 0s 45us/step - loss: 1.1373 - accuracy: 0.9866 - val_loss: 1.2393 - val_accuracy: 0.9200 Epoch 132/200 298/298 [==============================] - 0s 44us/step - loss: 1.1329 - accuracy: 0.9866 - val_loss: 1.2351 - val_accuracy: 0.9200 Epoch 133/200 298/298 [==============================] - 0s 49us/step - loss: 1.1286 - accuracy: 0.9866 - val_loss: 1.2309 - val_accuracy: 0.9200 Epoch 134/200 298/298 [==============================] - 0s 42us/step - loss: 1.1243 - accuracy: 0.9866 - val_loss: 1.2269 - val_accuracy: 0.9200 Epoch 135/200 298/298 [==============================] - 0s 42us/step - loss: 1.1200 - accuracy: 0.9899 - val_loss: 1.2229 - val_accuracy: 0.9300 Epoch 136/200 298/298 [==============================] - 0s 43us/step - loss: 1.1158 - accuracy: 0.9899 - val_loss: 1.2183 - val_accuracy: 0.9300 Epoch 137/200 298/298 [==============================] - 0s 44us/step - loss: 1.1115 - accuracy: 0.9899 - val_loss: 1.2143 - val_accuracy: 0.9300 Epoch 138/200 298/298 [==============================] - 0s 42us/step - loss: 1.1073 - accuracy: 0.9899 - val_loss: 1.2102 - val_accuracy: 0.9300 Epoch 139/200 298/298 [==============================] - 0s 44us/step - loss: 1.1031 - accuracy: 0.9899 - val_loss: 1.2062 - val_accuracy: 0.9300 Epoch 140/200 298/298 [==============================] - 0s 46us/step - loss: 1.0990 - accuracy: 0.9899 - val_loss: 1.2023 - val_accuracy: 0.9300 Epoch 141/200 298/298 [==============================] - 0s 42us/step - loss: 1.0948 - accuracy: 0.9899 - val_loss: 1.1983 - val_accuracy: 0.9300 Epoch 142/200 298/298 [==============================] - 0s 44us/step - loss: 1.0906 - accuracy: 0.9899 - val_loss: 1.1943 - val_accuracy: 0.9300 Epoch 143/200 298/298 [==============================] - 0s 48us/step - loss: 1.0865 - accuracy: 0.9899 - val_loss: 1.1904 - val_accuracy: 0.9300 Epoch 144/200 298/298 [==============================] - 0s 48us/step - loss: 1.0824 - accuracy: 0.9899 - val_loss: 1.1865 - val_accuracy: 0.9300 Epoch 145/200 298/298 [==============================] - 0s 43us/step - loss: 1.0783 - accuracy: 0.9899 - val_loss: 1.1826 - val_accuracy: 0.9300 Epoch 146/200 298/298 [==============================] - 0s 47us/step - loss: 1.0742 - accuracy: 0.9899 - val_loss: 1.1783 - val_accuracy: 0.9300 Epoch 147/200 298/298 [==============================] - 0s 45us/step - loss: 1.0701 - accuracy: 0.9899 - val_loss: 1.1744 - val_accuracy: 0.9300 Epoch 148/200 298/298 [==============================] - 0s 44us/step - loss: 1.0661 - accuracy: 0.9899 - val_loss: 1.1705 - val_accuracy: 0.9300 Epoch 149/200 298/298 [==============================] - 0s 42us/step - loss: 1.0621 - accuracy: 0.9899 - val_loss: 1.1666 - val_accuracy: 0.9400 Epoch 150/200 298/298 [==============================] - 0s 44us/step - loss: 1.0581 - accuracy: 0.9933 - val_loss: 1.1629 - val_accuracy: 0.9400 Epoch 151/200 298/298 [==============================] - 0s 46us/step - loss: 1.0541 - accuracy: 0.9933 - val_loss: 1.1591 - val_accuracy: 0.9400 Epoch 152/200 298/298 [==============================] - 0s 42us/step - loss: 1.0501 - accuracy: 0.9933 - val_loss: 1.1552 - val_accuracy: 0.9400 Epoch 153/200 298/298 [==============================] - 0s 42us/step - loss: 1.0462 - accuracy: 0.9933 - val_loss: 1.1515 - val_accuracy: 0.9400 Epoch 154/200 298/298 [==============================] - 0s 44us/step - loss: 1.0423 - accuracy: 0.9933 - val_loss: 1.1477 - val_accuracy: 0.9400 Epoch 155/200 298/298 [==============================] - 0s 43us/step - loss: 1.0384 - accuracy: 0.9933 - val_loss: 1.1439 - val_accuracy: 0.9400 Epoch 156/200 298/298 [==============================] - 0s 43us/step - loss: 1.0344 - accuracy: 0.9933 - val_loss: 1.1407 - val_accuracy: 0.9400 Epoch 157/200 298/298 [==============================] - 0s 42us/step - loss: 1.0305 - accuracy: 0.9933 - val_loss: 1.1369 - val_accuracy: 0.9400 Epoch 158/200 298/298 [==============================] - 0s 47us/step - loss: 1.0267 - accuracy: 0.9933 - val_loss: 1.1332 - val_accuracy: 0.9400 Epoch 159/200 298/298 [==============================] - 0s 49us/step - loss: 1.0229 - accuracy: 0.9933 - val_loss: 1.1294 - val_accuracy: 0.9400 Epoch 160/200 298/298 [==============================] - 0s 46us/step - loss: 1.0190 - accuracy: 0.9933 - val_loss: 1.1257 - val_accuracy: 0.9400 Epoch 161/200 298/298 [==============================] - 0s 42us/step - loss: 1.0152 - accuracy: 0.9933 - val_loss: 1.1224 - val_accuracy: 0.9400 Epoch 162/200 298/298 [==============================] - 0s 43us/step - loss: 1.0114 - accuracy: 0.9933 - val_loss: 1.1187 - val_accuracy: 0.9400 Epoch 163/200 298/298 [==============================] - 0s 46us/step - loss: 1.0076 - accuracy: 0.9933 - val_loss: 1.1151 - val_accuracy: 0.9400 Epoch 164/200 298/298 [==============================] - 0s 44us/step - loss: 1.0038 - accuracy: 0.9933 - val_loss: 1.1115 - val_accuracy: 0.9400 Epoch 165/200 298/298 [==============================] - 0s 45us/step - loss: 1.0001 - accuracy: 0.9933 - val_loss: 1.1079 - val_accuracy: 0.9400 Epoch 166/200 298/298 [==============================] - 0s 46us/step - loss: 0.9963 - accuracy: 0.9933 - val_loss: 1.1044 - val_accuracy: 0.9400 Epoch 167/200 298/298 [==============================] - 0s 45us/step - loss: 0.9926 - accuracy: 0.9933 - val_loss: 1.1008 - val_accuracy: 0.9400 Epoch 168/200 298/298 [==============================] - 0s 44us/step - loss: 0.9889 - accuracy: 0.9933 - val_loss: 1.0970 - val_accuracy: 0.9400 Epoch 169/200 298/298 [==============================] - 0s 45us/step - loss: 0.9851 - accuracy: 0.9933 - val_loss: 1.0935 - val_accuracy: 0.9400 Epoch 170/200 298/298 [==============================] - 0s 44us/step - loss: 0.9815 - accuracy: 0.9933 - val_loss: 1.0900 - val_accuracy: 0.9400 Epoch 171/200 298/298 [==============================] - 0s 45us/step - loss: 0.9778 - accuracy: 0.9933 - val_loss: 1.0865 - val_accuracy: 0.9400 Epoch 172/200 298/298 [==============================] - 0s 46us/step - loss: 0.9742 - accuracy: 0.9933 - val_loss: 1.0830 - val_accuracy: 0.9400 Epoch 173/200 298/298 [==============================] - 0s 50us/step - loss: 0.9705 - accuracy: 0.9933 - val_loss: 1.0798 - val_accuracy: 0.9400 Epoch 174/200 298/298 [==============================] - 0s 47us/step - loss: 0.9669 - accuracy: 0.9933 - val_loss: 1.0763 - val_accuracy: 0.9400 Epoch 175/200 298/298 [==============================] - 0s 48us/step - loss: 0.9633 - accuracy: 0.9933 - val_loss: 1.0728 - val_accuracy: 0.9400 Epoch 176/200 298/298 [==============================] - 0s 46us/step - loss: 0.9597 - accuracy: 0.9933 - val_loss: 1.0697 - val_accuracy: 0.9400 Epoch 177/200 298/298 [==============================] - 0s 45us/step - loss: 0.9561 - accuracy: 0.9933 - val_loss: 1.0662 - val_accuracy: 0.9400 Epoch 178/200 298/298 [==============================] - 0s 42us/step - loss: 0.9526 - accuracy: 0.9933 - val_loss: 1.0627 - val_accuracy: 0.9400 Epoch 179/200 298/298 [==============================] - 0s 43us/step - loss: 0.9490 - accuracy: 0.9933 - val_loss: 1.0592 - val_accuracy: 0.9400 Epoch 180/200 298/298 [==============================] - 0s 42us/step - loss: 0.9455 - accuracy: 0.9933 - val_loss: 1.0557 - val_accuracy: 0.9400 Epoch 181/200 298/298 [==============================] - 0s 42us/step - loss: 0.9420 - accuracy: 0.9933 - val_loss: 1.0523 - val_accuracy: 0.9400 Epoch 182/200 298/298 [==============================] - 0s 43us/step - loss: 0.9385 - accuracy: 0.9933 - val_loss: 1.0489 - val_accuracy: 0.9400 Epoch 183/200 298/298 [==============================] - 0s 43us/step - loss: 0.9350 - accuracy: 0.9933 - val_loss: 1.0455 - val_accuracy: 0.9400 Epoch 184/200 298/298 [==============================] - 0s 41us/step - loss: 0.9315 - accuracy: 0.9933 - val_loss: 1.0422 - val_accuracy: 0.9400 Epoch 185/200 298/298 [==============================] - 0s 45us/step - loss: 0.9280 - accuracy: 0.9933 - val_loss: 1.0388 - val_accuracy: 0.9400 Epoch 186/200 298/298 [==============================] - 0s 43us/step - loss: 0.9246 - accuracy: 0.9933 - val_loss: 1.0353 - val_accuracy: 0.9400 Epoch 187/200 298/298 [==============================] - 0s 42us/step - loss: 0.9212 - accuracy: 0.9933 - val_loss: 1.0321 - val_accuracy: 0.9400 Epoch 188/200 298/298 [==============================] - 0s 46us/step - loss: 0.9178 - accuracy: 0.9933 - val_loss: 1.0287 - val_accuracy: 0.9400 Epoch 189/200 298/298 [==============================] - 0s 46us/step - loss: 0.9143 - accuracy: 0.9933 - val_loss: 1.0254 - val_accuracy: 0.9400 Epoch 190/200 298/298 [==============================] - 0s 45us/step - loss: 0.9109 - accuracy: 0.9933 - val_loss: 1.0222 - val_accuracy: 0.9400 Epoch 191/200 298/298 [==============================] - 0s 45us/step - loss: 0.9076 - accuracy: 0.9933 - val_loss: 1.0189 - val_accuracy: 0.9400 Epoch 192/200 298/298 [==============================] - 0s 47us/step - loss: 0.9042 - accuracy: 0.9933 - val_loss: 1.0155 - val_accuracy: 0.9400 Epoch 193/200 298/298 [==============================] - 0s 48us/step - loss: 0.9009 - accuracy: 0.9933 - val_loss: 1.0122 - val_accuracy: 0.9400 Epoch 194/200 298/298 [==============================] - 0s 43us/step - loss: 0.8975 - accuracy: 0.9933 - val_loss: 1.0088 - val_accuracy: 0.9400 Epoch 195/200 298/298 [==============================] - 0s 45us/step - loss: 0.8942 - accuracy: 0.9933 - val_loss: 1.0057 - val_accuracy: 0.9400 Epoch 196/200 298/298 [==============================] - 0s 43us/step - loss: 0.8909 - accuracy: 0.9933 - val_loss: 1.0024 - val_accuracy: 0.9400 Epoch 197/200 298/298 [==============================] - 0s 44us/step - loss: 0.8876 - accuracy: 0.9933 - val_loss: 0.9992 - val_accuracy: 0.9400 Epoch 198/200 298/298 [==============================] - 0s 45us/step - loss: 0.8843 - accuracy: 0.9933 - val_loss: 0.9959 - val_accuracy: 0.9400 Epoch 199/200 298/298 [==============================] - 0s 43us/step - loss: 0.8810 - accuracy: 0.9933 - val_loss: 0.9928 - val_accuracy: 0.9400 Epoch 200/200 298/298 [==============================] - 0s 43us/step - loss: 0.8778 - accuracy: 0.9933 - val_loss: 0.9897 - val_accuracy: 0.9400 171/171 [==============================] - 0s 22us/step Optimizers: <keras.optimizers.SGD object at 0x1463bb710> Epoch Sizes: 200 Neurons or Units: 128 ['loss', 'accuracy'] [0.8919281642339383, 0.988304078578949] Test score: 0.8919281642339383 Test accuracy: 0.988304078578949 Model: "sequential_36" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_106 (Dense) (None, 256) 7936 _________________________________________________________________ activation_106 (Activation) (None, 256) 0 _________________________________________________________________ dense_107 (Dense) (None, 256) 65792 _________________________________________________________________ activation_107 (Activation) (None, 256) 0 _________________________________________________________________ dense_108 (Dense) (None, 1) 257 _________________________________________________________________ activation_108 (Activation) (None, 1) 0 ================================================================= Total params: 73,985 Trainable params: 73,985 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/200 298/298 [==============================] - 0s 484us/step - loss: 3.8292 - accuracy: 0.4866 - val_loss: 3.7423 - val_accuracy: 0.7000 Epoch 2/200 298/298 [==============================] - 0s 64us/step - loss: 3.6723 - accuracy: 0.8221 - val_loss: 3.6152 - val_accuracy: 0.8900 Epoch 3/200 298/298 [==============================] - 0s 63us/step - loss: 3.5638 - accuracy: 0.9161 - val_loss: 3.5279 - val_accuracy: 0.9100 Epoch 4/200 298/298 [==============================] - 0s 62us/step - loss: 3.4851 - accuracy: 0.9362 - val_loss: 3.4637 - val_accuracy: 0.9100 Epoch 5/200 298/298 [==============================] - 0s 62us/step - loss: 3.4252 - accuracy: 0.9362 - val_loss: 3.4127 - val_accuracy: 0.9000 Epoch 6/200 298/298 [==============================] - 0s 63us/step - loss: 3.3763 - accuracy: 0.9430 - val_loss: 3.3713 - val_accuracy: 0.9000 Epoch 7/200 298/298 [==============================] - 0s 62us/step - loss: 3.3347 - accuracy: 0.9564 - val_loss: 3.3358 - val_accuracy: 0.9100 Epoch 8/200 298/298 [==============================] - 0s 58us/step - loss: 3.2989 - accuracy: 0.9597 - val_loss: 3.3050 - val_accuracy: 0.9100 Epoch 9/200 298/298 [==============================] - 0s 59us/step - loss: 3.2674 - accuracy: 0.9631 - val_loss: 3.2781 - val_accuracy: 0.9200 Epoch 10/200 298/298 [==============================] - 0s 57us/step - loss: 3.2393 - accuracy: 0.9631 - val_loss: 3.2546 - val_accuracy: 0.9200 Epoch 11/200 298/298 [==============================] - 0s 56us/step - loss: 3.2140 - accuracy: 0.9664 - val_loss: 3.2322 - val_accuracy: 0.9200 Epoch 12/200 298/298 [==============================] - 0s 58us/step - loss: 3.1898 - accuracy: 0.9664 - val_loss: 3.2105 - val_accuracy: 0.9200 Epoch 13/200 298/298 [==============================] - 0s 57us/step - loss: 3.1674 - accuracy: 0.9698 - val_loss: 3.1915 - val_accuracy: 0.9200 Epoch 14/200 298/298 [==============================] - 0s 60us/step - loss: 3.1471 - accuracy: 0.9698 - val_loss: 3.1732 - val_accuracy: 0.9200 Epoch 15/200 298/298 [==============================] - 0s 58us/step - loss: 3.1271 - accuracy: 0.9698 - val_loss: 3.1558 - val_accuracy: 0.9200 Epoch 16/200 298/298 [==============================] - 0s 61us/step - loss: 3.1081 - accuracy: 0.9732 - val_loss: 3.1389 - val_accuracy: 0.9200 Epoch 17/200 298/298 [==============================] - 0s 58us/step - loss: 3.0899 - accuracy: 0.9732 - val_loss: 3.1230 - val_accuracy: 0.9200 Epoch 18/200 298/298 [==============================] - 0s 58us/step - loss: 3.0725 - accuracy: 0.9732 - val_loss: 3.1076 - val_accuracy: 0.9200 Epoch 19/200 298/298 [==============================] - 0s 57us/step - loss: 3.0555 - accuracy: 0.9732 - val_loss: 3.0926 - val_accuracy: 0.9200 Epoch 20/200 298/298 [==============================] - 0s 57us/step - loss: 3.0393 - accuracy: 0.9732 - val_loss: 3.0787 - val_accuracy: 0.9200 Epoch 21/200 298/298 [==============================] - 0s 57us/step - loss: 3.0236 - accuracy: 0.9765 - val_loss: 3.0646 - val_accuracy: 0.9200 Epoch 22/200 298/298 [==============================] - 0s 56us/step - loss: 3.0083 - accuracy: 0.9765 - val_loss: 3.0510 - val_accuracy: 0.9200 Epoch 23/200 298/298 [==============================] - 0s 59us/step - loss: 2.9934 - accuracy: 0.9765 - val_loss: 3.0374 - val_accuracy: 0.9200 Epoch 24/200 298/298 [==============================] - 0s 57us/step - loss: 2.9787 - accuracy: 0.9765 - val_loss: 3.0244 - val_accuracy: 0.9200 Epoch 25/200 298/298 [==============================] - 0s 59us/step - loss: 2.9644 - accuracy: 0.9765 - val_loss: 3.0111 - val_accuracy: 0.9200 Epoch 26/200 298/298 [==============================] - 0s 56us/step - loss: 2.9502 - accuracy: 0.9765 - val_loss: 2.9979 - val_accuracy: 0.9200 Epoch 27/200 298/298 [==============================] - 0s 57us/step - loss: 2.9362 - accuracy: 0.9765 - val_loss: 2.9851 - val_accuracy: 0.9200 Epoch 28/200 298/298 [==============================] - 0s 57us/step - loss: 2.9225 - accuracy: 0.9765 - val_loss: 2.9725 - val_accuracy: 0.9300 Epoch 29/200 298/298 [==============================] - 0s 57us/step - loss: 2.9092 - accuracy: 0.9765 - val_loss: 2.9602 - val_accuracy: 0.9300 Epoch 30/200 298/298 [==============================] - 0s 54us/step - loss: 2.8961 - accuracy: 0.9765 - val_loss: 2.9478 - val_accuracy: 0.9400 Epoch 31/200 298/298 [==============================] - 0s 59us/step - loss: 2.8830 - accuracy: 0.9765 - val_loss: 2.9357 - val_accuracy: 0.9400 Epoch 32/200 298/298 [==============================] - 0s 57us/step - loss: 2.8702 - accuracy: 0.9799 - val_loss: 2.9240 - val_accuracy: 0.9400 Epoch 33/200 298/298 [==============================] - 0s 55us/step - loss: 2.8574 - accuracy: 0.9799 - val_loss: 2.9120 - val_accuracy: 0.9400 Epoch 34/200 298/298 [==============================] - 0s 55us/step - loss: 2.8450 - accuracy: 0.9799 - val_loss: 2.9006 - val_accuracy: 0.9400 Epoch 35/200 298/298 [==============================] - 0s 58us/step - loss: 2.8325 - accuracy: 0.9799 - val_loss: 2.8889 - val_accuracy: 0.9400 Epoch 36/200 298/298 [==============================] - 0s 54us/step - loss: 2.8201 - accuracy: 0.9799 - val_loss: 2.8775 - val_accuracy: 0.9400 Epoch 37/200 298/298 [==============================] - 0s 57us/step - loss: 2.8079 - accuracy: 0.9799 - val_loss: 2.8661 - val_accuracy: 0.9400 Epoch 38/200 298/298 [==============================] - 0s 57us/step - loss: 2.7959 - accuracy: 0.9799 - val_loss: 2.8549 - val_accuracy: 0.9400 Epoch 39/200 298/298 [==============================] - 0s 60us/step - loss: 2.7839 - accuracy: 0.9799 - val_loss: 2.8437 - val_accuracy: 0.9400 Epoch 40/200 298/298 [==============================] - 0s 58us/step - loss: 2.7720 - accuracy: 0.9799 - val_loss: 2.8326 - val_accuracy: 0.9400 Epoch 41/200 298/298 [==============================] - 0s 62us/step - loss: 2.7603 - accuracy: 0.9832 - val_loss: 2.8216 - val_accuracy: 0.9400 Epoch 42/200 298/298 [==============================] - 0s 56us/step - loss: 2.7487 - accuracy: 0.9832 - val_loss: 2.8109 - val_accuracy: 0.9400 Epoch 43/200 298/298 [==============================] - 0s 59us/step - loss: 2.7371 - accuracy: 0.9832 - val_loss: 2.8001 - val_accuracy: 0.9400 Epoch 44/200 298/298 [==============================] - 0s 60us/step - loss: 2.7256 - accuracy: 0.9832 - val_loss: 2.7894 - val_accuracy: 0.9400 Epoch 45/200 298/298 [==============================] - 0s 57us/step - loss: 2.7143 - accuracy: 0.9832 - val_loss: 2.7788 - val_accuracy: 0.9400 Epoch 46/200 298/298 [==============================] - 0s 59us/step - loss: 2.7029 - accuracy: 0.9832 - val_loss: 2.7682 - val_accuracy: 0.9400 Epoch 47/200 298/298 [==============================] - 0s 58us/step - loss: 2.6917 - accuracy: 0.9832 - val_loss: 2.7574 - val_accuracy: 0.9400 Epoch 48/200 298/298 [==============================] - 0s 57us/step - loss: 2.6805 - accuracy: 0.9832 - val_loss: 2.7471 - val_accuracy: 0.9400 Epoch 49/200 298/298 [==============================] - 0s 58us/step - loss: 2.6695 - accuracy: 0.9832 - val_loss: 2.7365 - val_accuracy: 0.9400 Epoch 50/200 298/298 [==============================] - 0s 60us/step - loss: 2.6585 - accuracy: 0.9832 - val_loss: 2.7261 - val_accuracy: 0.9400 Epoch 51/200 298/298 [==============================] - 0s 57us/step - loss: 2.6476 - accuracy: 0.9832 - val_loss: 2.7156 - val_accuracy: 0.9400 Epoch 52/200 298/298 [==============================] - 0s 61us/step - loss: 2.6368 - accuracy: 0.9832 - val_loss: 2.7049 - val_accuracy: 0.9400 Epoch 53/200 298/298 [==============================] - 0s 59us/step - loss: 2.6260 - accuracy: 0.9832 - val_loss: 2.6941 - val_accuracy: 0.9500 Epoch 54/200 298/298 [==============================] - 0s 61us/step - loss: 2.6152 - accuracy: 0.9832 - val_loss: 2.6836 - val_accuracy: 0.9500 Epoch 55/200 298/298 [==============================] - 0s 58us/step - loss: 2.6045 - accuracy: 0.9832 - val_loss: 2.6735 - val_accuracy: 0.9500 Epoch 56/200 298/298 [==============================] - 0s 60us/step - loss: 2.5940 - accuracy: 0.9832 - val_loss: 2.6634 - val_accuracy: 0.9500 Epoch 57/200 298/298 [==============================] - 0s 63us/step - loss: 2.5834 - accuracy: 0.9832 - val_loss: 2.6534 - val_accuracy: 0.9500 Epoch 58/200 298/298 [==============================] - 0s 62us/step - loss: 2.5730 - accuracy: 0.9832 - val_loss: 2.6434 - val_accuracy: 0.9500 Epoch 59/200 298/298 [==============================] - 0s 62us/step - loss: 2.5626 - accuracy: 0.9832 - val_loss: 2.6336 - val_accuracy: 0.9500 Epoch 60/200 298/298 [==============================] - 0s 62us/step - loss: 2.5522 - accuracy: 0.9832 - val_loss: 2.6240 - val_accuracy: 0.9500 Epoch 61/200 298/298 [==============================] - 0s 60us/step - loss: 2.5419 - accuracy: 0.9832 - val_loss: 2.6142 - val_accuracy: 0.9500 Epoch 62/200 298/298 [==============================] - 0s 60us/step - loss: 2.5317 - accuracy: 0.9832 - val_loss: 2.6042 - val_accuracy: 0.9500 Epoch 63/200 298/298 [==============================] - 0s 67us/step - loss: 2.5216 - accuracy: 0.9832 - val_loss: 2.5945 - val_accuracy: 0.9500 Epoch 64/200 298/298 [==============================] - 0s 61us/step - loss: 2.5114 - accuracy: 0.9832 - val_loss: 2.5844 - val_accuracy: 0.9500 Epoch 65/200 298/298 [==============================] - 0s 59us/step - loss: 2.5013 - accuracy: 0.9832 - val_loss: 2.5747 - val_accuracy: 0.9500 Epoch 66/200 298/298 [==============================] - 0s 58us/step - loss: 2.4913 - accuracy: 0.9832 - val_loss: 2.5652 - val_accuracy: 0.9500 Epoch 67/200 298/298 [==============================] - 0s 65us/step - loss: 2.4813 - accuracy: 0.9832 - val_loss: 2.5555 - val_accuracy: 0.9500 Epoch 68/200 298/298 [==============================] - 0s 66us/step - loss: 2.4714 - accuracy: 0.9866 - val_loss: 2.5460 - val_accuracy: 0.9500 Epoch 69/200 298/298 [==============================] - 0s 58us/step - loss: 2.4615 - accuracy: 0.9866 - val_loss: 2.5367 - val_accuracy: 0.9500 Epoch 70/200 298/298 [==============================] - 0s 57us/step - loss: 2.4516 - accuracy: 0.9866 - val_loss: 2.5273 - val_accuracy: 0.9500 Epoch 71/200 298/298 [==============================] - 0s 58us/step - loss: 2.4419 - accuracy: 0.9866 - val_loss: 2.5179 - val_accuracy: 0.9500 Epoch 72/200 298/298 [==============================] - 0s 62us/step - loss: 2.4322 - accuracy: 0.9866 - val_loss: 2.5086 - val_accuracy: 0.9500 Epoch 73/200 298/298 [==============================] - 0s 57us/step - loss: 2.4225 - accuracy: 0.9866 - val_loss: 2.4992 - val_accuracy: 0.9500 Epoch 74/200 298/298 [==============================] - 0s 57us/step - loss: 2.4129 - accuracy: 0.9866 - val_loss: 2.4901 - val_accuracy: 0.9500 Epoch 75/200 298/298 [==============================] - 0s 56us/step - loss: 2.4033 - accuracy: 0.9866 - val_loss: 2.4809 - val_accuracy: 0.9500 Epoch 76/200 298/298 [==============================] - 0s 60us/step - loss: 2.3937 - accuracy: 0.9866 - val_loss: 2.4718 - val_accuracy: 0.9500 Epoch 77/200 298/298 [==============================] - 0s 55us/step - loss: 2.3843 - accuracy: 0.9866 - val_loss: 2.4628 - val_accuracy: 0.9500 Epoch 78/200 298/298 [==============================] - 0s 56us/step - loss: 2.3748 - accuracy: 0.9866 - val_loss: 2.4537 - val_accuracy: 0.9500 Epoch 79/200 298/298 [==============================] - 0s 63us/step - loss: 2.3654 - accuracy: 0.9866 - val_loss: 2.4447 - val_accuracy: 0.9500 Epoch 80/200 298/298 [==============================] - 0s 57us/step - loss: 2.3561 - accuracy: 0.9866 - val_loss: 2.4353 - val_accuracy: 0.9500 Epoch 81/200 298/298 [==============================] - 0s 60us/step - loss: 2.3467 - accuracy: 0.9866 - val_loss: 2.4257 - val_accuracy: 0.9500 Epoch 82/200 298/298 [==============================] - 0s 58us/step - loss: 2.3374 - accuracy: 0.9866 - val_loss: 2.4169 - val_accuracy: 0.9500 Epoch 83/200 298/298 [==============================] - 0s 62us/step - loss: 2.3281 - accuracy: 0.9866 - val_loss: 2.4080 - val_accuracy: 0.9500 Epoch 84/200 298/298 [==============================] - 0s 61us/step - loss: 2.3189 - accuracy: 0.9866 - val_loss: 2.3993 - val_accuracy: 0.9500 Epoch 85/200 298/298 [==============================] - 0s 56us/step - loss: 2.3098 - accuracy: 0.9866 - val_loss: 2.3904 - val_accuracy: 0.9500 Epoch 86/200 298/298 [==============================] - 0s 57us/step - loss: 2.3007 - accuracy: 0.9866 - val_loss: 2.3816 - val_accuracy: 0.9500 Epoch 87/200 298/298 [==============================] - 0s 63us/step - loss: 2.2916 - accuracy: 0.9866 - val_loss: 2.3728 - val_accuracy: 0.9500 Epoch 88/200 298/298 [==============================] - 0s 58us/step - loss: 2.2825 - accuracy: 0.9866 - val_loss: 2.3643 - val_accuracy: 0.9500 Epoch 89/200 298/298 [==============================] - 0s 56us/step - loss: 2.2735 - accuracy: 0.9866 - val_loss: 2.3557 - val_accuracy: 0.9500 Epoch 90/200 298/298 [==============================] - 0s 62us/step - loss: 2.2646 - accuracy: 0.9866 - val_loss: 2.3470 - val_accuracy: 0.9500 Epoch 91/200 298/298 [==============================] - 0s 58us/step - loss: 2.2557 - accuracy: 0.9866 - val_loss: 2.3384 - val_accuracy: 0.9500 Epoch 92/200 298/298 [==============================] - 0s 60us/step - loss: 2.2468 - accuracy: 0.9866 - val_loss: 2.3299 - val_accuracy: 0.9500 Epoch 93/200 298/298 [==============================] - 0s 63us/step - loss: 2.2380 - accuracy: 0.9866 - val_loss: 2.3214 - val_accuracy: 0.9500 Epoch 94/200 298/298 [==============================] - 0s 63us/step - loss: 2.2292 - accuracy: 0.9866 - val_loss: 2.3129 - val_accuracy: 0.9500 Epoch 95/200 298/298 [==============================] - 0s 63us/step - loss: 2.2204 - accuracy: 0.9866 - val_loss: 2.3042 - val_accuracy: 0.9500 Epoch 96/200 298/298 [==============================] - 0s 60us/step - loss: 2.2117 - accuracy: 0.9866 - val_loss: 2.2958 - val_accuracy: 0.9500 Epoch 97/200 298/298 [==============================] - 0s 61us/step - loss: 2.2031 - accuracy: 0.9866 - val_loss: 2.2873 - val_accuracy: 0.9500 Epoch 98/200 298/298 [==============================] - 0s 59us/step - loss: 2.1944 - accuracy: 0.9899 - val_loss: 2.2790 - val_accuracy: 0.9500 Epoch 99/200 298/298 [==============================] - 0s 69us/step - loss: 2.1858 - accuracy: 0.9899 - val_loss: 2.2702 - val_accuracy: 0.9500 Epoch 100/200 298/298 [==============================] - 0s 63us/step - loss: 2.1773 - accuracy: 0.9933 - val_loss: 2.2620 - val_accuracy: 0.9500 Epoch 101/200 298/298 [==============================] - 0s 64us/step - loss: 2.1687 - accuracy: 0.9933 - val_loss: 2.2537 - val_accuracy: 0.9500 Epoch 102/200 298/298 [==============================] - 0s 69us/step - loss: 2.1602 - accuracy: 0.9933 - val_loss: 2.2455 - val_accuracy: 0.9500 Epoch 103/200 298/298 [==============================] - 0s 63us/step - loss: 2.1517 - accuracy: 0.9933 - val_loss: 2.2374 - val_accuracy: 0.9500 Epoch 104/200 298/298 [==============================] - 0s 64us/step - loss: 2.1433 - accuracy: 0.9933 - val_loss: 2.2293 - val_accuracy: 0.9500 Epoch 105/200 298/298 [==============================] - 0s 65us/step - loss: 2.1349 - accuracy: 0.9933 - val_loss: 2.2211 - val_accuracy: 0.9500 Epoch 106/200 298/298 [==============================] - 0s 58us/step - loss: 2.1266 - accuracy: 0.9933 - val_loss: 2.2130 - val_accuracy: 0.9500 Epoch 107/200 298/298 [==============================] - 0s 58us/step - loss: 2.1183 - accuracy: 0.9933 - val_loss: 2.2049 - val_accuracy: 0.9500 Epoch 108/200 298/298 [==============================] - 0s 61us/step - loss: 2.1100 - accuracy: 0.9933 - val_loss: 2.1969 - val_accuracy: 0.9500 Epoch 109/200 298/298 [==============================] - 0s 65us/step - loss: 2.1018 - accuracy: 0.9933 - val_loss: 2.1885 - val_accuracy: 0.9500 Epoch 110/200 298/298 [==============================] - 0s 62us/step - loss: 2.0936 - accuracy: 0.9933 - val_loss: 2.1805 - val_accuracy: 0.9500 Epoch 111/200 298/298 [==============================] - 0s 61us/step - loss: 2.0853 - accuracy: 0.9933 - val_loss: 2.1726 - val_accuracy: 0.9500 Epoch 112/200 298/298 [==============================] - 0s 65us/step - loss: 2.0772 - accuracy: 0.9933 - val_loss: 2.1644 - val_accuracy: 0.9500 Epoch 113/200 298/298 [==============================] - 0s 67us/step - loss: 2.0691 - accuracy: 0.9933 - val_loss: 2.1567 - val_accuracy: 0.9500 Epoch 114/200 298/298 [==============================] - 0s 67us/step - loss: 2.0610 - accuracy: 0.9933 - val_loss: 2.1489 - val_accuracy: 0.9500 Epoch 115/200 298/298 [==============================] - 0s 61us/step - loss: 2.0529 - accuracy: 0.9933 - val_loss: 2.1405 - val_accuracy: 0.9500 Epoch 116/200 298/298 [==============================] - 0s 60us/step - loss: 2.0449 - accuracy: 0.9933 - val_loss: 2.1328 - val_accuracy: 0.9500 Epoch 117/200 298/298 [==============================] - 0s 63us/step - loss: 2.0369 - accuracy: 0.9933 - val_loss: 2.1250 - val_accuracy: 0.9500 Epoch 118/200 298/298 [==============================] - 0s 63us/step - loss: 2.0290 - accuracy: 0.9933 - val_loss: 2.1174 - val_accuracy: 0.9500 Epoch 119/200 298/298 [==============================] - 0s 63us/step - loss: 2.0211 - accuracy: 0.9933 - val_loss: 2.1098 - val_accuracy: 0.9500 Epoch 120/200 298/298 [==============================] - 0s 59us/step - loss: 2.0132 - accuracy: 0.9933 - val_loss: 2.1022 - val_accuracy: 0.9500 Epoch 121/200 298/298 [==============================] - 0s 62us/step - loss: 2.0054 - accuracy: 0.9933 - val_loss: 2.0947 - val_accuracy: 0.9500 Epoch 122/200 298/298 [==============================] - 0s 62us/step - loss: 1.9977 - accuracy: 0.9933 - val_loss: 2.0865 - val_accuracy: 0.9500 Epoch 123/200 298/298 [==============================] - 0s 64us/step - loss: 1.9899 - accuracy: 0.9933 - val_loss: 2.0790 - val_accuracy: 0.9500 Epoch 124/200 298/298 [==============================] - 0s 61us/step - loss: 1.9821 - accuracy: 0.9933 - val_loss: 2.0715 - val_accuracy: 0.9500 Epoch 125/200 298/298 [==============================] - 0s 62us/step - loss: 1.9744 - accuracy: 0.9933 - val_loss: 2.0640 - val_accuracy: 0.9500 Epoch 126/200 298/298 [==============================] - 0s 63us/step - loss: 1.9667 - accuracy: 0.9933 - val_loss: 2.0568 - val_accuracy: 0.9500 Epoch 127/200 298/298 [==============================] - 0s 63us/step - loss: 1.9591 - accuracy: 0.9933 - val_loss: 2.0495 - val_accuracy: 0.9500 Epoch 128/200 298/298 [==============================] - 0s 61us/step - loss: 1.9515 - accuracy: 0.9933 - val_loss: 2.0421 - val_accuracy: 0.9500 Epoch 129/200 298/298 [==============================] - 0s 63us/step - loss: 1.9439 - accuracy: 0.9933 - val_loss: 2.0347 - val_accuracy: 0.9500 Epoch 130/200 298/298 [==============================] - 0s 62us/step - loss: 1.9364 - accuracy: 0.9933 - val_loss: 2.0276 - val_accuracy: 0.9500 Epoch 131/200 298/298 [==============================] - 0s 63us/step - loss: 1.9289 - accuracy: 0.9933 - val_loss: 2.0202 - val_accuracy: 0.9500 Epoch 132/200 298/298 [==============================] - 0s 59us/step - loss: 1.9214 - accuracy: 0.9933 - val_loss: 2.0131 - val_accuracy: 0.9500 Epoch 133/200 298/298 [==============================] - 0s 62us/step - loss: 1.9139 - accuracy: 0.9933 - val_loss: 2.0058 - val_accuracy: 0.9500 Epoch 134/200 298/298 [==============================] - 0s 59us/step - loss: 1.9065 - accuracy: 0.9933 - val_loss: 1.9986 - val_accuracy: 0.9500 Epoch 135/200 298/298 [==============================] - 0s 59us/step - loss: 1.8991 - accuracy: 0.9933 - val_loss: 1.9916 - val_accuracy: 0.9500 Epoch 136/200 298/298 [==============================] - 0s 56us/step - loss: 1.8918 - accuracy: 0.9933 - val_loss: 1.9845 - val_accuracy: 0.9500 Epoch 137/200 298/298 [==============================] - 0s 54us/step - loss: 1.8844 - accuracy: 0.9933 - val_loss: 1.9773 - val_accuracy: 0.9500 Epoch 138/200 298/298 [==============================] - 0s 54us/step - loss: 1.8771 - accuracy: 0.9933 - val_loss: 1.9701 - val_accuracy: 0.9500 Epoch 139/200 298/298 [==============================] - 0s 59us/step - loss: 1.8698 - accuracy: 0.9933 - val_loss: 1.9629 - val_accuracy: 0.9500 Epoch 140/200 298/298 [==============================] - 0s 57us/step - loss: 1.8626 - accuracy: 0.9933 - val_loss: 1.9559 - val_accuracy: 0.9500 Epoch 141/200 298/298 [==============================] - 0s 54us/step - loss: 1.8554 - accuracy: 0.9933 - val_loss: 1.9489 - val_accuracy: 0.9500 Epoch 142/200 298/298 [==============================] - 0s 54us/step - loss: 1.8482 - accuracy: 0.9933 - val_loss: 1.9416 - val_accuracy: 0.9500 Epoch 143/200 298/298 [==============================] - 0s 57us/step - loss: 1.8410 - accuracy: 0.9933 - val_loss: 1.9348 - val_accuracy: 0.9500 Epoch 144/200 298/298 [==============================] - 0s 59us/step - loss: 1.8339 - accuracy: 0.9933 - val_loss: 1.9280 - val_accuracy: 0.9500 Epoch 145/200 298/298 [==============================] - 0s 60us/step - loss: 1.8269 - accuracy: 0.9933 - val_loss: 1.9211 - val_accuracy: 0.9500 Epoch 146/200 298/298 [==============================] - 0s 60us/step - loss: 1.8197 - accuracy: 0.9933 - val_loss: 1.9143 - val_accuracy: 0.9500 Epoch 147/200 298/298 [==============================] - 0s 59us/step - loss: 1.8127 - accuracy: 0.9933 - val_loss: 1.9071 - val_accuracy: 0.9500 Epoch 148/200 298/298 [==============================] - 0s 56us/step - loss: 1.8057 - accuracy: 0.9933 - val_loss: 1.9005 - val_accuracy: 0.9500 Epoch 149/200 298/298 [==============================] - 0s 55us/step - loss: 1.7987 - accuracy: 0.9933 - val_loss: 1.8936 - val_accuracy: 0.9500 Epoch 150/200 298/298 [==============================] - 0s 57us/step - loss: 1.7918 - accuracy: 0.9933 - val_loss: 1.8870 - val_accuracy: 0.9500 Epoch 151/200 298/298 [==============================] - 0s 58us/step - loss: 1.7848 - accuracy: 0.9933 - val_loss: 1.8803 - val_accuracy: 0.9500 Epoch 152/200 298/298 [==============================] - 0s 56us/step - loss: 1.7780 - accuracy: 0.9933 - val_loss: 1.8735 - val_accuracy: 0.9500 Epoch 153/200 298/298 [==============================] - 0s 56us/step - loss: 1.7711 - accuracy: 0.9933 - val_loss: 1.8668 - val_accuracy: 0.9500 Epoch 154/200 298/298 [==============================] - 0s 60us/step - loss: 1.7643 - accuracy: 0.9933 - val_loss: 1.8601 - val_accuracy: 0.9500 Epoch 155/200 298/298 [==============================] - 0s 61us/step - loss: 1.7575 - accuracy: 0.9933 - val_loss: 1.8535 - val_accuracy: 0.9500 Epoch 156/200 298/298 [==============================] - 0s 57us/step - loss: 1.7507 - accuracy: 0.9933 - val_loss: 1.8467 - val_accuracy: 0.9500 Epoch 157/200 298/298 [==============================] - 0s 56us/step - loss: 1.7439 - accuracy: 0.9933 - val_loss: 1.8402 - val_accuracy: 0.9500 Epoch 158/200 298/298 [==============================] - 0s 56us/step - loss: 1.7372 - accuracy: 0.9933 - val_loss: 1.8337 - val_accuracy: 0.9500 Epoch 159/200 298/298 [==============================] - 0s 56us/step - loss: 1.7305 - accuracy: 0.9933 - val_loss: 1.8271 - val_accuracy: 0.9500 Epoch 160/200 298/298 [==============================] - 0s 56us/step - loss: 1.7238 - accuracy: 0.9933 - val_loss: 1.8206 - val_accuracy: 0.9500 Epoch 161/200 298/298 [==============================] - 0s 56us/step - loss: 1.7171 - accuracy: 0.9933 - val_loss: 1.8142 - val_accuracy: 0.9500 Epoch 162/200 298/298 [==============================] - 0s 62us/step - loss: 1.7106 - accuracy: 0.9933 - val_loss: 1.8078 - val_accuracy: 0.9500 Epoch 163/200 298/298 [==============================] - 0s 55us/step - loss: 1.7040 - accuracy: 0.9933 - val_loss: 1.8016 - val_accuracy: 0.9500 Epoch 164/200 298/298 [==============================] - 0s 56us/step - loss: 1.6974 - accuracy: 0.9933 - val_loss: 1.7952 - val_accuracy: 0.9500 Epoch 165/200 298/298 [==============================] - 0s 57us/step - loss: 1.6909 - accuracy: 0.9933 - val_loss: 1.7888 - val_accuracy: 0.9500 Epoch 166/200 298/298 [==============================] - 0s 60us/step - loss: 1.6844 - accuracy: 0.9933 - val_loss: 1.7825 - val_accuracy: 0.9500 Epoch 167/200 298/298 [==============================] - 0s 63us/step - loss: 1.6779 - accuracy: 0.9933 - val_loss: 1.7755 - val_accuracy: 0.9500 Epoch 168/200 298/298 [==============================] - 0s 57us/step - loss: 1.6714 - accuracy: 0.9966 - val_loss: 1.7693 - val_accuracy: 0.9500 Epoch 169/200 298/298 [==============================] - 0s 55us/step - loss: 1.6650 - accuracy: 0.9966 - val_loss: 1.7630 - val_accuracy: 0.9500 Epoch 170/200 298/298 [==============================] - 0s 57us/step - loss: 1.6586 - accuracy: 0.9966 - val_loss: 1.7567 - val_accuracy: 0.9500 Epoch 171/200 298/298 [==============================] - 0s 57us/step - loss: 1.6522 - accuracy: 0.9966 - val_loss: 1.7505 - val_accuracy: 0.9500 Epoch 172/200 298/298 [==============================] - 0s 58us/step - loss: 1.6458 - accuracy: 0.9966 - val_loss: 1.7443 - val_accuracy: 0.9500 Epoch 173/200 298/298 [==============================] - 0s 60us/step - loss: 1.6396 - accuracy: 0.9966 - val_loss: 1.7377 - val_accuracy: 0.9400 Epoch 174/200 298/298 [==============================] - 0s 58us/step - loss: 1.6332 - accuracy: 0.9966 - val_loss: 1.7314 - val_accuracy: 0.9400 Epoch 175/200 298/298 [==============================] - 0s 57us/step - loss: 1.6269 - accuracy: 0.9966 - val_loss: 1.7253 - val_accuracy: 0.9400 Epoch 176/200 298/298 [==============================] - 0s 59us/step - loss: 1.6206 - accuracy: 0.9966 - val_loss: 1.7193 - val_accuracy: 0.9400 Epoch 177/200 298/298 [==============================] - 0s 58us/step - loss: 1.6144 - accuracy: 0.9966 - val_loss: 1.7134 - val_accuracy: 0.9400 Epoch 178/200 298/298 [==============================] - 0s 56us/step - loss: 1.6082 - accuracy: 0.9966 - val_loss: 1.7072 - val_accuracy: 0.9400 Epoch 179/200 298/298 [==============================] - 0s 57us/step - loss: 1.6020 - accuracy: 0.9966 - val_loss: 1.7014 - val_accuracy: 0.9400 Epoch 180/200 298/298 [==============================] - 0s 57us/step - loss: 1.5959 - accuracy: 0.9966 - val_loss: 1.6956 - val_accuracy: 0.9400 Epoch 181/200 298/298 [==============================] - 0s 57us/step - loss: 1.5898 - accuracy: 0.9966 - val_loss: 1.6897 - val_accuracy: 0.9400 Epoch 182/200 298/298 [==============================] - 0s 67us/step - loss: 1.5837 - accuracy: 0.9966 - val_loss: 1.6837 - val_accuracy: 0.9400 Epoch 183/200 298/298 [==============================] - 0s 60us/step - loss: 1.5776 - accuracy: 0.9966 - val_loss: 1.6779 - val_accuracy: 0.9400 Epoch 184/200 298/298 [==============================] - 0s 61us/step - loss: 1.5716 - accuracy: 0.9966 - val_loss: 1.6721 - val_accuracy: 0.9400 Epoch 185/200 298/298 [==============================] - 0s 58us/step - loss: 1.5655 - accuracy: 0.9966 - val_loss: 1.6662 - val_accuracy: 0.9400 Epoch 186/200 298/298 [==============================] - 0s 56us/step - loss: 1.5596 - accuracy: 0.9966 - val_loss: 1.6604 - val_accuracy: 0.9400 Epoch 187/200 298/298 [==============================] - 0s 55us/step - loss: 1.5536 - accuracy: 0.9966 - val_loss: 1.6539 - val_accuracy: 0.9400 Epoch 188/200 298/298 [==============================] - 0s 56us/step - loss: 1.5476 - accuracy: 0.9966 - val_loss: 1.6481 - val_accuracy: 0.9400 Epoch 189/200 298/298 [==============================] - 0s 56us/step - loss: 1.5417 - accuracy: 0.9966 - val_loss: 1.6424 - val_accuracy: 0.9400 Epoch 190/200 298/298 [==============================] - 0s 55us/step - loss: 1.5358 - accuracy: 0.9966 - val_loss: 1.6367 - val_accuracy: 0.9400 Epoch 191/200 298/298 [==============================] - 0s 55us/step - loss: 1.5299 - accuracy: 0.9966 - val_loss: 1.6310 - val_accuracy: 0.9400 Epoch 192/200 298/298 [==============================] - 0s 55us/step - loss: 1.5240 - accuracy: 0.9966 - val_loss: 1.6255 - val_accuracy: 0.9400 Epoch 193/200 298/298 [==============================] - 0s 57us/step - loss: 1.5182 - accuracy: 0.9966 - val_loss: 1.6194 - val_accuracy: 0.9400 Epoch 194/200 298/298 [==============================] - 0s 58us/step - loss: 1.5124 - accuracy: 0.9966 - val_loss: 1.6138 - val_accuracy: 0.9400 Epoch 195/200 298/298 [==============================] - 0s 63us/step - loss: 1.5066 - accuracy: 0.9966 - val_loss: 1.6084 - val_accuracy: 0.9400 Epoch 196/200 298/298 [==============================] - 0s 57us/step - loss: 1.5009 - accuracy: 0.9966 - val_loss: 1.6027 - val_accuracy: 0.9400 Epoch 197/200 298/298 [==============================] - 0s 58us/step - loss: 1.4952 - accuracy: 0.9966 - val_loss: 1.5972 - val_accuracy: 0.9400 Epoch 198/200 298/298 [==============================] - 0s 58us/step - loss: 1.4894 - accuracy: 0.9966 - val_loss: 1.5915 - val_accuracy: 0.9400 Epoch 199/200 298/298 [==============================] - 0s 56us/step - loss: 1.4837 - accuracy: 0.9966 - val_loss: 1.5853 - val_accuracy: 0.9400 Epoch 200/200 298/298 [==============================] - 0s 56us/step - loss: 1.4781 - accuracy: 0.9966 - val_loss: 1.5799 - val_accuracy: 0.9400 171/171 [==============================] - 0s 26us/step Optimizers: <keras.optimizers.SGD object at 0x1463bb710> Epoch Sizes: 200 Neurons or Units: 256 ['loss', 'accuracy'] [1.487289780064633, 0.988304078578949] Test score: 1.487289780064633 Test accuracy: 0.988304078578949 Model: "sequential_37" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_109 (Dense) (None, 64) 1984 _________________________________________________________________ activation_109 (Activation) (None, 64) 0 _________________________________________________________________ dense_110 (Dense) (None, 64) 4160 _________________________________________________________________ activation_110 (Activation) (None, 64) 0 _________________________________________________________________ dense_111 (Dense) (None, 1) 65 _________________________________________________________________ activation_111 (Activation) (None, 1) 0 ================================================================= Total params: 6,209 Trainable params: 6,209 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/50 298/298 [==============================] - 0s 516us/step - loss: 1.4422 - accuracy: 0.8221 - val_loss: 1.2509 - val_accuracy: 0.9100 Epoch 2/50 298/298 [==============================] - 0s 49us/step - loss: 1.1661 - accuracy: 0.9564 - val_loss: 1.1061 - val_accuracy: 0.9100 Epoch 3/50 298/298 [==============================] - 0s 44us/step - loss: 1.0287 - accuracy: 0.9564 - val_loss: 1.0066 - val_accuracy: 0.9100 Epoch 4/50 298/298 [==============================] - 0s 43us/step - loss: 0.9230 - accuracy: 0.9732 - val_loss: 0.9189 - val_accuracy: 0.9200 Epoch 5/50 298/298 [==============================] - 0s 49us/step - loss: 0.8302 - accuracy: 0.9698 - val_loss: 0.8398 - val_accuracy: 0.9200 Epoch 6/50 298/298 [==============================] - 0s 42us/step - loss: 0.7481 - accuracy: 0.9732 - val_loss: 0.7742 - val_accuracy: 0.9200 Epoch 7/50 298/298 [==============================] - 0s 44us/step - loss: 0.6736 - accuracy: 0.9765 - val_loss: 0.7079 - val_accuracy: 0.9300 Epoch 8/50 298/298 [==============================] - 0s 47us/step - loss: 0.6048 - accuracy: 0.9799 - val_loss: 0.6496 - val_accuracy: 0.9400 Epoch 9/50 298/298 [==============================] - 0s 42us/step - loss: 0.5429 - accuracy: 0.9799 - val_loss: 0.5893 - val_accuracy: 0.9300 Epoch 10/50 298/298 [==============================] - 0s 43us/step - loss: 0.4865 - accuracy: 0.9899 - val_loss: 0.5451 - val_accuracy: 0.9300 Epoch 11/50 298/298 [==============================] - 0s 44us/step - loss: 0.4364 - accuracy: 0.9866 - val_loss: 0.4992 - val_accuracy: 0.9400 Epoch 12/50 298/298 [==============================] - 0s 42us/step - loss: 0.3915 - accuracy: 0.9866 - val_loss: 0.4584 - val_accuracy: 0.9400 Epoch 13/50 298/298 [==============================] - 0s 41us/step - loss: 0.3509 - accuracy: 0.9899 - val_loss: 0.4269 - val_accuracy: 0.9400 Epoch 14/50 298/298 [==============================] - 0s 43us/step - loss: 0.3145 - accuracy: 0.9933 - val_loss: 0.3968 - val_accuracy: 0.9500 Epoch 15/50 298/298 [==============================] - 0s 41us/step - loss: 0.2821 - accuracy: 0.9933 - val_loss: 0.3682 - val_accuracy: 0.9400 Epoch 16/50 298/298 [==============================] - 0s 49us/step - loss: 0.2532 - accuracy: 0.9899 - val_loss: 0.3387 - val_accuracy: 0.9400 Epoch 17/50 298/298 [==============================] - 0s 44us/step - loss: 0.2283 - accuracy: 0.9966 - val_loss: 0.3202 - val_accuracy: 0.9400 Epoch 18/50 298/298 [==============================] - 0s 46us/step - loss: 0.2067 - accuracy: 0.9933 - val_loss: 0.2870 - val_accuracy: 0.9400 Epoch 19/50 298/298 [==============================] - 0s 45us/step - loss: 0.1896 - accuracy: 0.9933 - val_loss: 0.2759 - val_accuracy: 0.9400 Epoch 20/50 298/298 [==============================] - 0s 43us/step - loss: 0.1759 - accuracy: 0.9966 - val_loss: 0.2660 - val_accuracy: 0.9400 Epoch 21/50 298/298 [==============================] - 0s 45us/step - loss: 0.1636 - accuracy: 0.9933 - val_loss: 0.2648 - val_accuracy: 0.9400 Epoch 22/50 298/298 [==============================] - 0s 48us/step - loss: 0.1515 - accuracy: 0.9966 - val_loss: 0.2532 - val_accuracy: 0.9500 Epoch 23/50 298/298 [==============================] - 0s 43us/step - loss: 0.1442 - accuracy: 0.9933 - val_loss: 0.2472 - val_accuracy: 0.9400 Epoch 24/50 298/298 [==============================] - 0s 42us/step - loss: 0.1365 - accuracy: 0.9966 - val_loss: 0.2375 - val_accuracy: 0.9400 Epoch 25/50 298/298 [==============================] - 0s 43us/step - loss: 0.1285 - accuracy: 0.9966 - val_loss: 0.2382 - val_accuracy: 0.9400 Epoch 26/50 298/298 [==============================] - 0s 46us/step - loss: 0.1234 - accuracy: 0.9933 - val_loss: 0.2178 - val_accuracy: 0.9400 Epoch 27/50 298/298 [==============================] - 0s 45us/step - loss: 0.1179 - accuracy: 0.9966 - val_loss: 0.2176 - val_accuracy: 0.9400 Epoch 28/50 298/298 [==============================] - 0s 44us/step - loss: 0.1125 - accuracy: 0.9933 - val_loss: 0.2211 - val_accuracy: 0.9400 Epoch 29/50 298/298 [==============================] - 0s 45us/step - loss: 0.1095 - accuracy: 0.9966 - val_loss: 0.2244 - val_accuracy: 0.9400 Epoch 30/50 298/298 [==============================] - 0s 48us/step - loss: 0.1054 - accuracy: 0.9933 - val_loss: 0.2188 - val_accuracy: 0.9400 Epoch 31/50 298/298 [==============================] - 0s 46us/step - loss: 0.1033 - accuracy: 0.9933 - val_loss: 0.2113 - val_accuracy: 0.9400 Epoch 32/50 298/298 [==============================] - 0s 48us/step - loss: 0.0998 - accuracy: 0.9933 - val_loss: 0.2197 - val_accuracy: 0.9500 Epoch 33/50 298/298 [==============================] - 0s 44us/step - loss: 0.0972 - accuracy: 0.9899 - val_loss: 0.2110 - val_accuracy: 0.9500 Epoch 34/50 298/298 [==============================] - 0s 46us/step - loss: 0.0945 - accuracy: 0.9966 - val_loss: 0.1924 - val_accuracy: 0.9500 Epoch 35/50 298/298 [==============================] - 0s 46us/step - loss: 0.0926 - accuracy: 0.9933 - val_loss: 0.2062 - val_accuracy: 0.9400 Epoch 36/50 298/298 [==============================] - 0s 49us/step - loss: 0.0905 - accuracy: 0.9933 - val_loss: 0.2061 - val_accuracy: 0.9400 Epoch 37/50 298/298 [==============================] - 0s 44us/step - loss: 0.0899 - accuracy: 0.9966 - val_loss: 0.1934 - val_accuracy: 0.9500 Epoch 38/50 298/298 [==============================] - 0s 44us/step - loss: 0.0875 - accuracy: 0.9933 - val_loss: 0.1927 - val_accuracy: 0.9500 Epoch 39/50 298/298 [==============================] - 0s 45us/step - loss: 0.0861 - accuracy: 0.9933 - val_loss: 0.1994 - val_accuracy: 0.9400 Epoch 40/50 298/298 [==============================] - 0s 45us/step - loss: 0.0849 - accuracy: 0.9933 - val_loss: 0.1864 - val_accuracy: 0.9500 Epoch 41/50 298/298 [==============================] - 0s 46us/step - loss: 0.0833 - accuracy: 0.9966 - val_loss: 0.1852 - val_accuracy: 0.9500 Epoch 42/50 298/298 [==============================] - 0s 46us/step - loss: 0.0819 - accuracy: 0.9933 - val_loss: 0.1997 - val_accuracy: 0.9500 Epoch 43/50 298/298 [==============================] - 0s 46us/step - loss: 0.0802 - accuracy: 0.9933 - val_loss: 0.1948 - val_accuracy: 0.9500 Epoch 44/50 298/298 [==============================] - 0s 57us/step - loss: 0.0797 - accuracy: 0.9899 - val_loss: 0.1886 - val_accuracy: 0.9500 Epoch 45/50 298/298 [==============================] - 0s 48us/step - loss: 0.0775 - accuracy: 0.9966 - val_loss: 0.1913 - val_accuracy: 0.9500 Epoch 46/50 298/298 [==============================] - 0s 44us/step - loss: 0.0771 - accuracy: 0.9933 - val_loss: 0.1985 - val_accuracy: 0.9400 Epoch 47/50 298/298 [==============================] - 0s 42us/step - loss: 0.0767 - accuracy: 0.9933 - val_loss: 0.1966 - val_accuracy: 0.9400 Epoch 48/50 298/298 [==============================] - 0s 43us/step - loss: 0.0743 - accuracy: 0.9933 - val_loss: 0.1913 - val_accuracy: 0.9500 Epoch 49/50 298/298 [==============================] - 0s 44us/step - loss: 0.0742 - accuracy: 0.9966 - val_loss: 0.1939 - val_accuracy: 0.9500 Epoch 50/50 298/298 [==============================] - 0s 44us/step - loss: 0.0737 - accuracy: 0.9899 - val_loss: 0.1891 - val_accuracy: 0.9500 171/171 [==============================] - 0s 26us/step Optimizers: <keras.optimizers.RMSprop object at 0x10a1d9a50> Epoch Sizes: 50 Neurons or Units: 64 ['loss', 'accuracy'] [0.09528473019599915, 0.988304078578949] Test score: 0.09528473019599915 Test accuracy: 0.988304078578949 Model: "sequential_38" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_112 (Dense) (None, 128) 3968 _________________________________________________________________ activation_112 (Activation) (None, 128) 0 _________________________________________________________________ dense_113 (Dense) (None, 128) 16512 _________________________________________________________________ activation_113 (Activation) (None, 128) 0 _________________________________________________________________ dense_114 (Dense) (None, 1) 129 _________________________________________________________________ activation_114 (Activation) (None, 1) 0 ================================================================= Total params: 20,609 Trainable params: 20,609 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/50 298/298 [==============================] - 0s 530us/step - loss: 1.9541 - accuracy: 0.9262 - val_loss: 1.6830 - val_accuracy: 0.9200 Epoch 2/50 298/298 [==============================] - 0s 48us/step - loss: 1.5498 - accuracy: 0.9597 - val_loss: 1.4490 - val_accuracy: 0.9300 Epoch 3/50 298/298 [==============================] - 0s 47us/step - loss: 1.3251 - accuracy: 0.9698 - val_loss: 1.2575 - val_accuracy: 0.9200 Epoch 4/50 298/298 [==============================] - 0s 45us/step - loss: 1.1371 - accuracy: 0.9732 - val_loss: 1.0886 - val_accuracy: 0.9400 Epoch 5/50 298/298 [==============================] - 0s 44us/step - loss: 0.9719 - accuracy: 0.9765 - val_loss: 0.9431 - val_accuracy: 0.9400 Epoch 6/50 298/298 [==============================] - 0s 45us/step - loss: 0.8247 - accuracy: 0.9799 - val_loss: 0.8153 - val_accuracy: 0.9500 Epoch 7/50 298/298 [==============================] - 0s 45us/step - loss: 0.6966 - accuracy: 0.9832 - val_loss: 0.7072 - val_accuracy: 0.9500 Epoch 8/50 298/298 [==============================] - 0s 45us/step - loss: 0.5861 - accuracy: 0.9832 - val_loss: 0.5953 - val_accuracy: 0.9500 Epoch 9/50 298/298 [==============================] - 0s 80us/step - loss: 0.4916 - accuracy: 0.9933 - val_loss: 0.5163 - val_accuracy: 0.9500 Epoch 10/50 298/298 [==============================] - 0s 65us/step - loss: 0.4120 - accuracy: 0.9933 - val_loss: 0.4635 - val_accuracy: 0.9500 Epoch 11/50 298/298 [==============================] - 0s 48us/step - loss: 0.3466 - accuracy: 0.9899 - val_loss: 0.4022 - val_accuracy: 0.9300 Epoch 12/50 298/298 [==============================] - 0s 46us/step - loss: 0.2927 - accuracy: 0.9933 - val_loss: 0.3609 - val_accuracy: 0.9400 Epoch 13/50 298/298 [==============================] - 0s 45us/step - loss: 0.2490 - accuracy: 0.9966 - val_loss: 0.3259 - val_accuracy: 0.9500 Epoch 14/50 298/298 [==============================] - 0s 45us/step - loss: 0.2156 - accuracy: 0.9899 - val_loss: 0.2963 - val_accuracy: 0.9400 Epoch 15/50 298/298 [==============================] - 0s 44us/step - loss: 0.1864 - accuracy: 0.9933 - val_loss: 0.2615 - val_accuracy: 0.9400 Epoch 16/50 298/298 [==============================] - 0s 46us/step - loss: 0.1673 - accuracy: 0.9933 - val_loss: 0.2538 - val_accuracy: 0.9400 Epoch 17/50 298/298 [==============================] - 0s 45us/step - loss: 0.1503 - accuracy: 0.9966 - val_loss: 0.2438 - val_accuracy: 0.9400 Epoch 18/50 298/298 [==============================] - 0s 49us/step - loss: 0.1387 - accuracy: 0.9933 - val_loss: 0.2332 - val_accuracy: 0.9400 Epoch 19/50 298/298 [==============================] - 0s 50us/step - loss: 0.1303 - accuracy: 0.9933 - val_loss: 0.2245 - val_accuracy: 0.9400 Epoch 20/50 298/298 [==============================] - 0s 48us/step - loss: 0.1206 - accuracy: 0.9933 - val_loss: 0.2066 - val_accuracy: 0.9500 Epoch 21/50 298/298 [==============================] - 0s 49us/step - loss: 0.1151 - accuracy: 0.9933 - val_loss: 0.2116 - val_accuracy: 0.9400 Epoch 22/50 298/298 [==============================] - 0s 50us/step - loss: 0.1089 - accuracy: 0.9933 - val_loss: 0.2178 - val_accuracy: 0.9400 Epoch 23/50 298/298 [==============================] - 0s 48us/step - loss: 0.1034 - accuracy: 0.9933 - val_loss: 0.2055 - val_accuracy: 0.9500 Epoch 24/50 298/298 [==============================] - 0s 56us/step - loss: 0.1006 - accuracy: 0.9966 - val_loss: 0.2088 - val_accuracy: 0.9400 Epoch 25/50 298/298 [==============================] - 0s 55us/step - loss: 0.0992 - accuracy: 0.9933 - val_loss: 0.2258 - val_accuracy: 0.9400 Epoch 26/50 298/298 [==============================] - 0s 53us/step - loss: 0.0934 - accuracy: 0.9899 - val_loss: 0.2094 - val_accuracy: 0.9400 Epoch 27/50 298/298 [==============================] - 0s 53us/step - loss: 0.0935 - accuracy: 0.9899 - val_loss: 0.2145 - val_accuracy: 0.9400 Epoch 28/50 298/298 [==============================] - 0s 51us/step - loss: 0.0912 - accuracy: 0.9933 - val_loss: 0.2094 - val_accuracy: 0.9400 Epoch 29/50 298/298 [==============================] - 0s 48us/step - loss: 0.0896 - accuracy: 0.9899 - val_loss: 0.2001 - val_accuracy: 0.9400 Epoch 30/50 298/298 [==============================] - 0s 50us/step - loss: 0.0854 - accuracy: 0.9966 - val_loss: 0.2108 - val_accuracy: 0.9500 Epoch 31/50 298/298 [==============================] - 0s 47us/step - loss: 0.0836 - accuracy: 0.9933 - val_loss: 0.2102 - val_accuracy: 0.9400 Epoch 32/50 298/298 [==============================] - 0s 45us/step - loss: 0.0800 - accuracy: 0.9933 - val_loss: 0.2119 - val_accuracy: 0.9500 Epoch 33/50 298/298 [==============================] - 0s 45us/step - loss: 0.0823 - accuracy: 0.9899 - val_loss: 0.1930 - val_accuracy: 0.9400 Epoch 34/50 298/298 [==============================] - 0s 46us/step - loss: 0.0773 - accuracy: 0.9933 - val_loss: 0.1839 - val_accuracy: 0.9500 Epoch 35/50 298/298 [==============================] - 0s 45us/step - loss: 0.0768 - accuracy: 0.9933 - val_loss: 0.1970 - val_accuracy: 0.9400 Epoch 36/50 298/298 [==============================] - 0s 43us/step - loss: 0.0770 - accuracy: 0.9933 - val_loss: 0.2001 - val_accuracy: 0.9400 Epoch 37/50 298/298 [==============================] - 0s 44us/step - loss: 0.0752 - accuracy: 0.9933 - val_loss: 0.2032 - val_accuracy: 0.9400 Epoch 38/50 298/298 [==============================] - 0s 46us/step - loss: 0.0744 - accuracy: 0.9933 - val_loss: 0.1891 - val_accuracy: 0.9400 Epoch 39/50 298/298 [==============================] - 0s 50us/step - loss: 0.0721 - accuracy: 0.9966 - val_loss: 0.2153 - val_accuracy: 0.9500 Epoch 40/50 298/298 [==============================] - 0s 49us/step - loss: 0.0718 - accuracy: 0.9933 - val_loss: 0.2002 - val_accuracy: 0.9400 Epoch 41/50 298/298 [==============================] - 0s 52us/step - loss: 0.0691 - accuracy: 0.9966 - val_loss: 0.2149 - val_accuracy: 0.9200 Epoch 42/50 298/298 [==============================] - 0s 49us/step - loss: 0.0697 - accuracy: 0.9933 - val_loss: 0.1933 - val_accuracy: 0.9500 Epoch 43/50 298/298 [==============================] - 0s 49us/step - loss: 0.0710 - accuracy: 0.9899 - val_loss: 0.1853 - val_accuracy: 0.9600 Epoch 44/50 298/298 [==============================] - 0s 48us/step - loss: 0.0670 - accuracy: 0.9966 - val_loss: 0.1975 - val_accuracy: 0.9400 Epoch 45/50 298/298 [==============================] - 0s 46us/step - loss: 0.0678 - accuracy: 0.9933 - val_loss: 0.2106 - val_accuracy: 0.9400 Epoch 46/50 298/298 [==============================] - 0s 50us/step - loss: 0.0643 - accuracy: 0.9933 - val_loss: 0.2002 - val_accuracy: 0.9400 Epoch 47/50 298/298 [==============================] - 0s 47us/step - loss: 0.0641 - accuracy: 0.9966 - val_loss: 0.2116 - val_accuracy: 0.9400 Epoch 48/50 298/298 [==============================] - 0s 46us/step - loss: 0.0649 - accuracy: 0.9933 - val_loss: 0.2127 - val_accuracy: 0.9400 Epoch 49/50 298/298 [==============================] - 0s 46us/step - loss: 0.0659 - accuracy: 0.9933 - val_loss: 0.2243 - val_accuracy: 0.9400 Epoch 50/50 298/298 [==============================] - 0s 47us/step - loss: 0.0618 - accuracy: 0.9966 - val_loss: 0.2100 - val_accuracy: 0.9500 171/171 [==============================] - 0s 23us/step Optimizers: <keras.optimizers.RMSprop object at 0x10a1d9a50> Epoch Sizes: 50 Neurons or Units: 128 ['loss', 'accuracy'] [0.0941982003506164, 0.988304078578949] Test score: 0.0941982003506164 Test accuracy: 0.988304078578949 Model: "sequential_39" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_115 (Dense) (None, 256) 7936 _________________________________________________________________ activation_115 (Activation) (None, 256) 0 _________________________________________________________________ dense_116 (Dense) (None, 256) 65792 _________________________________________________________________ activation_116 (Activation) (None, 256) 0 _________________________________________________________________ dense_117 (Dense) (None, 1) 257 _________________________________________________________________ activation_117 (Activation) (None, 1) 0 ================================================================= Total params: 73,985 Trainable params: 73,985 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/50 298/298 [==============================] - 0s 596us/step - loss: 2.9963 - accuracy: 0.8993 - val_loss: 2.4705 - val_accuracy: 0.9100 Epoch 2/50 298/298 [==============================] - 0s 61us/step - loss: 2.1758 - accuracy: 0.9698 - val_loss: 1.9338 - val_accuracy: 0.9300 Epoch 3/50 298/298 [==============================] - 0s 72us/step - loss: 1.6893 - accuracy: 0.9799 - val_loss: 1.5164 - val_accuracy: 0.9400 Epoch 4/50 298/298 [==============================] - 0s 65us/step - loss: 1.3121 - accuracy: 0.9866 - val_loss: 1.2122 - val_accuracy: 0.9300 Epoch 5/50 298/298 [==============================] - 0s 63us/step - loss: 1.0038 - accuracy: 0.9832 - val_loss: 0.9321 - val_accuracy: 0.9500 Epoch 6/50 298/298 [==============================] - 0s 63us/step - loss: 0.7564 - accuracy: 0.9899 - val_loss: 0.7227 - val_accuracy: 0.9200 Epoch 7/50 298/298 [==============================] - 0s 67us/step - loss: 0.5646 - accuracy: 0.9899 - val_loss: 0.5936 - val_accuracy: 0.9400 Epoch 8/50 298/298 [==============================] - 0s 64us/step - loss: 0.4226 - accuracy: 0.9899 - val_loss: 0.4692 - val_accuracy: 0.9100 Epoch 9/50 298/298 [==============================] - 0s 63us/step - loss: 0.3181 - accuracy: 0.9933 - val_loss: 0.3444 - val_accuracy: 0.9600 Epoch 10/50 298/298 [==============================] - 0s 62us/step - loss: 0.2473 - accuracy: 0.9933 - val_loss: 0.3227 - val_accuracy: 0.9500 Epoch 11/50 298/298 [==============================] - 0s 66us/step - loss: 0.1993 - accuracy: 0.9933 - val_loss: 0.2820 - val_accuracy: 0.9500 Epoch 12/50 298/298 [==============================] - 0s 64us/step - loss: 0.1661 - accuracy: 0.9933 - val_loss: 0.2799 - val_accuracy: 0.9500 Epoch 13/50 298/298 [==============================] - 0s 63us/step - loss: 0.1476 - accuracy: 0.9933 - val_loss: 0.2222 - val_accuracy: 0.9600 Epoch 14/50 298/298 [==============================] - 0s 63us/step - loss: 0.1347 - accuracy: 0.9899 - val_loss: 0.2356 - val_accuracy: 0.9500 Epoch 15/50 298/298 [==============================] - 0s 60us/step - loss: 0.1237 - accuracy: 0.9933 - val_loss: 0.2159 - val_accuracy: 0.9400 Epoch 16/50 298/298 [==============================] - 0s 63us/step - loss: 0.1169 - accuracy: 0.9933 - val_loss: 0.2200 - val_accuracy: 0.9400 Epoch 17/50 298/298 [==============================] - 0s 65us/step - loss: 0.1107 - accuracy: 0.9899 - val_loss: 0.2144 - val_accuracy: 0.9400 Epoch 18/50 298/298 [==============================] - 0s 64us/step - loss: 0.1101 - accuracy: 0.9866 - val_loss: 0.2241 - val_accuracy: 0.9400 Epoch 19/50 298/298 [==============================] - 0s 64us/step - loss: 0.0996 - accuracy: 0.9933 - val_loss: 0.2103 - val_accuracy: 0.9400 Epoch 20/50 298/298 [==============================] - 0s 63us/step - loss: 0.0979 - accuracy: 0.9933 - val_loss: 0.2033 - val_accuracy: 0.9500 Epoch 21/50 298/298 [==============================] - 0s 64us/step - loss: 0.0940 - accuracy: 0.9933 - val_loss: 0.2013 - val_accuracy: 0.9400 Epoch 22/50 298/298 [==============================] - 0s 66us/step - loss: 0.0954 - accuracy: 0.9899 - val_loss: 0.2229 - val_accuracy: 0.9500 Epoch 23/50 298/298 [==============================] - 0s 66us/step - loss: 0.0897 - accuracy: 0.9899 - val_loss: 0.1995 - val_accuracy: 0.9400 Epoch 24/50 298/298 [==============================] - 0s 64us/step - loss: 0.0890 - accuracy: 0.9933 - val_loss: 0.1992 - val_accuracy: 0.9500 Epoch 25/50 298/298 [==============================] - 0s 64us/step - loss: 0.0850 - accuracy: 0.9899 - val_loss: 0.1983 - val_accuracy: 0.9500 Epoch 26/50 298/298 [==============================] - 0s 60us/step - loss: 0.0798 - accuracy: 0.9966 - val_loss: 0.1714 - val_accuracy: 0.9500 Epoch 27/50 298/298 [==============================] - 0s 61us/step - loss: 0.0841 - accuracy: 0.9899 - val_loss: 0.1625 - val_accuracy: 0.9600 Epoch 28/50 298/298 [==============================] - 0s 62us/step - loss: 0.0825 - accuracy: 0.9966 - val_loss: 0.2016 - val_accuracy: 0.9500 Epoch 29/50 298/298 [==============================] - 0s 62us/step - loss: 0.0760 - accuracy: 0.9966 - val_loss: 0.1662 - val_accuracy: 0.9600 Epoch 30/50 298/298 [==============================] - 0s 61us/step - loss: 0.0804 - accuracy: 0.9899 - val_loss: 0.1942 - val_accuracy: 0.9400 Epoch 31/50 298/298 [==============================] - 0s 64us/step - loss: 0.0756 - accuracy: 0.9899 - val_loss: 0.2114 - val_accuracy: 0.9500 Epoch 32/50 298/298 [==============================] - 0s 64us/step - loss: 0.0746 - accuracy: 0.9933 - val_loss: 0.1954 - val_accuracy: 0.9500 Epoch 33/50 298/298 [==============================] - 0s 65us/step - loss: 0.0736 - accuracy: 0.9933 - val_loss: 0.2228 - val_accuracy: 0.9500 Epoch 34/50 298/298 [==============================] - 0s 66us/step - loss: 0.0715 - accuracy: 0.9899 - val_loss: 0.1736 - val_accuracy: 0.9600 Epoch 35/50 298/298 [==============================] - 0s 65us/step - loss: 0.0700 - accuracy: 0.9899 - val_loss: 0.2051 - val_accuracy: 0.9500 Epoch 36/50 298/298 [==============================] - 0s 62us/step - loss: 0.0709 - accuracy: 0.9866 - val_loss: 0.1845 - val_accuracy: 0.9500 Epoch 37/50 298/298 [==============================] - 0s 61us/step - loss: 0.0656 - accuracy: 0.9966 - val_loss: 0.2217 - val_accuracy: 0.9500 Epoch 38/50 298/298 [==============================] - 0s 68us/step - loss: 0.0685 - accuracy: 0.9933 - val_loss: 0.1859 - val_accuracy: 0.9500 Epoch 39/50 298/298 [==============================] - 0s 64us/step - loss: 0.0668 - accuracy: 0.9899 - val_loss: 0.2397 - val_accuracy: 0.9400 Epoch 40/50 298/298 [==============================] - 0s 59us/step - loss: 0.0741 - accuracy: 0.9899 - val_loss: 0.1900 - val_accuracy: 0.9500 Epoch 41/50 298/298 [==============================] - 0s 60us/step - loss: 0.0611 - accuracy: 0.9933 - val_loss: 0.2208 - val_accuracy: 0.9400 Epoch 42/50 298/298 [==============================] - 0s 60us/step - loss: 0.0709 - accuracy: 0.9899 - val_loss: 0.1966 - val_accuracy: 0.9400 Epoch 43/50 298/298 [==============================] - 0s 63us/step - loss: 0.0617 - accuracy: 0.9933 - val_loss: 0.2359 - val_accuracy: 0.9500 Epoch 44/50 298/298 [==============================] - 0s 65us/step - loss: 0.0609 - accuracy: 0.9933 - val_loss: 0.2312 - val_accuracy: 0.9500 Epoch 45/50 298/298 [==============================] - 0s 66us/step - loss: 0.0661 - accuracy: 0.9933 - val_loss: 0.1772 - val_accuracy: 0.9600 Epoch 46/50 298/298 [==============================] - 0s 62us/step - loss: 0.0656 - accuracy: 0.9866 - val_loss: 0.2214 - val_accuracy: 0.9400 Epoch 47/50 298/298 [==============================] - 0s 62us/step - loss: 0.0588 - accuracy: 0.9966 - val_loss: 0.1812 - val_accuracy: 0.9500 Epoch 48/50 298/298 [==============================] - 0s 62us/step - loss: 0.0620 - accuracy: 0.9933 - val_loss: 0.1799 - val_accuracy: 0.9500 Epoch 49/50 298/298 [==============================] - 0s 64us/step - loss: 0.0588 - accuracy: 0.9966 - val_loss: 0.2308 - val_accuracy: 0.9400 Epoch 50/50 298/298 [==============================] - 0s 70us/step - loss: 0.0591 - accuracy: 0.9933 - val_loss: 0.2629 - val_accuracy: 0.9500 171/171 [==============================] - 0s 44us/step Optimizers: <keras.optimizers.RMSprop object at 0x10a1d9a50> Epoch Sizes: 50 Neurons or Units: 256 ['loss', 'accuracy'] [0.11992883978531374, 0.9707602262496948] Test score: 0.11992883978531374 Test accuracy: 0.9707602262496948 Model: "sequential_40" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_118 (Dense) (None, 64) 1984 _________________________________________________________________ activation_118 (Activation) (None, 64) 0 _________________________________________________________________ dense_119 (Dense) (None, 64) 4160 _________________________________________________________________ activation_119 (Activation) (None, 64) 0 _________________________________________________________________ dense_120 (Dense) (None, 1) 65 _________________________________________________________________ activation_120 (Activation) (None, 1) 0 ================================================================= Total params: 6,209 Trainable params: 6,209 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/100 298/298 [==============================] - 0s 572us/step - loss: 1.5404 - accuracy: 0.7886 - val_loss: 1.3267 - val_accuracy: 0.8900 Epoch 2/100 298/298 [==============================] - 0s 43us/step - loss: 1.2231 - accuracy: 0.9497 - val_loss: 1.1673 - val_accuracy: 0.8900 Epoch 3/100 298/298 [==============================] - 0s 45us/step - loss: 1.0752 - accuracy: 0.9698 - val_loss: 1.0601 - val_accuracy: 0.8900 Epoch 4/100 298/298 [==============================] - 0s 45us/step - loss: 0.9645 - accuracy: 0.9732 - val_loss: 0.9693 - val_accuracy: 0.9100 Epoch 5/100 298/298 [==============================] - 0s 45us/step - loss: 0.8711 - accuracy: 0.9765 - val_loss: 0.8838 - val_accuracy: 0.9100 Epoch 6/100 298/298 [==============================] - 0s 43us/step - loss: 0.7852 - accuracy: 0.9765 - val_loss: 0.8070 - val_accuracy: 0.9200 Epoch 7/100 298/298 [==============================] - 0s 45us/step - loss: 0.7069 - accuracy: 0.9765 - val_loss: 0.7354 - val_accuracy: 0.9300 Epoch 8/100 298/298 [==============================] - 0s 45us/step - loss: 0.6347 - accuracy: 0.9765 - val_loss: 0.6716 - val_accuracy: 0.9400 Epoch 9/100 298/298 [==============================] - 0s 45us/step - loss: 0.5696 - accuracy: 0.9765 - val_loss: 0.6032 - val_accuracy: 0.9500 Epoch 10/100 298/298 [==============================] - 0s 43us/step - loss: 0.5100 - accuracy: 0.9832 - val_loss: 0.5539 - val_accuracy: 0.9500 Epoch 11/100 298/298 [==============================] - 0s 43us/step - loss: 0.4569 - accuracy: 0.9799 - val_loss: 0.5011 - val_accuracy: 0.9600 Epoch 12/100 298/298 [==============================] - 0s 42us/step - loss: 0.4098 - accuracy: 0.9933 - val_loss: 0.4661 - val_accuracy: 0.9600 Epoch 13/100 298/298 [==============================] - 0s 43us/step - loss: 0.3682 - accuracy: 0.9933 - val_loss: 0.4296 - val_accuracy: 0.9500 Epoch 14/100 298/298 [==============================] - 0s 44us/step - loss: 0.3299 - accuracy: 0.9899 - val_loss: 0.3900 - val_accuracy: 0.9600 Epoch 15/100 298/298 [==============================] - 0s 44us/step - loss: 0.2975 - accuracy: 0.9933 - val_loss: 0.3653 - val_accuracy: 0.9600 Epoch 16/100 298/298 [==============================] - 0s 82us/step - loss: 0.2693 - accuracy: 0.9899 - val_loss: 0.3437 - val_accuracy: 0.9500 Epoch 17/100 298/298 [==============================] - 0s 54us/step - loss: 0.2447 - accuracy: 0.9966 - val_loss: 0.3284 - val_accuracy: 0.9500 Epoch 18/100 298/298 [==============================] - 0s 57us/step - loss: 0.2220 - accuracy: 0.9966 - val_loss: 0.2991 - val_accuracy: 0.9600 Epoch 19/100 298/298 [==============================] - 0s 44us/step - loss: 0.2025 - accuracy: 0.9966 - val_loss: 0.2888 - val_accuracy: 0.9500 Epoch 20/100 298/298 [==============================] - 0s 44us/step - loss: 0.1857 - accuracy: 0.9966 - val_loss: 0.2790 - val_accuracy: 0.9500 Epoch 21/100 298/298 [==============================] - 0s 45us/step - loss: 0.1721 - accuracy: 0.9933 - val_loss: 0.2704 - val_accuracy: 0.9500 Epoch 22/100 298/298 [==============================] - 0s 45us/step - loss: 0.1599 - accuracy: 0.9966 - val_loss: 0.2457 - val_accuracy: 0.9400 Epoch 23/100 298/298 [==============================] - 0s 44us/step - loss: 0.1479 - accuracy: 0.9966 - val_loss: 0.2457 - val_accuracy: 0.9500 Epoch 24/100 298/298 [==============================] - 0s 41us/step - loss: 0.1394 - accuracy: 0.9966 - val_loss: 0.2449 - val_accuracy: 0.9500 Epoch 25/100 298/298 [==============================] - 0s 45us/step - loss: 0.1319 - accuracy: 0.9933 - val_loss: 0.2334 - val_accuracy: 0.9500 Epoch 26/100 298/298 [==============================] - 0s 43us/step - loss: 0.1249 - accuracy: 0.9933 - val_loss: 0.2321 - val_accuracy: 0.9500 Epoch 27/100 298/298 [==============================] - 0s 44us/step - loss: 0.1193 - accuracy: 0.9966 - val_loss: 0.2248 - val_accuracy: 0.9500 Epoch 28/100 298/298 [==============================] - 0s 48us/step - loss: 0.1146 - accuracy: 0.9966 - val_loss: 0.2249 - val_accuracy: 0.9500 Epoch 29/100 298/298 [==============================] - 0s 47us/step - loss: 0.1107 - accuracy: 0.9933 - val_loss: 0.2141 - val_accuracy: 0.9400 Epoch 30/100 298/298 [==============================] - 0s 47us/step - loss: 0.1055 - accuracy: 0.9966 - val_loss: 0.2280 - val_accuracy: 0.9500 Epoch 31/100 298/298 [==============================] - 0s 45us/step - loss: 0.1038 - accuracy: 0.9933 - val_loss: 0.2194 - val_accuracy: 0.9500 Epoch 32/100 298/298 [==============================] - 0s 43us/step - loss: 0.1003 - accuracy: 0.9933 - val_loss: 0.2066 - val_accuracy: 0.9400 Epoch 33/100 298/298 [==============================] - 0s 44us/step - loss: 0.0969 - accuracy: 0.9966 - val_loss: 0.2126 - val_accuracy: 0.9400 Epoch 34/100 298/298 [==============================] - 0s 45us/step - loss: 0.0942 - accuracy: 0.9933 - val_loss: 0.1940 - val_accuracy: 0.9500 Epoch 35/100 298/298 [==============================] - 0s 43us/step - loss: 0.0931 - accuracy: 0.9966 - val_loss: 0.2130 - val_accuracy: 0.9400 Epoch 36/100 298/298 [==============================] - 0s 45us/step - loss: 0.0919 - accuracy: 0.9933 - val_loss: 0.2100 - val_accuracy: 0.9400 Epoch 37/100 298/298 [==============================] - 0s 46us/step - loss: 0.0885 - accuracy: 0.9933 - val_loss: 0.1931 - val_accuracy: 0.9400 Epoch 38/100 298/298 [==============================] - 0s 46us/step - loss: 0.0857 - accuracy: 0.9966 - val_loss: 0.1989 - val_accuracy: 0.9400 Epoch 39/100 298/298 [==============================] - 0s 46us/step - loss: 0.0861 - accuracy: 0.9966 - val_loss: 0.1965 - val_accuracy: 0.9400 Epoch 40/100 298/298 [==============================] - 0s 43us/step - loss: 0.0843 - accuracy: 0.9933 - val_loss: 0.1985 - val_accuracy: 0.9500 Epoch 41/100 298/298 [==============================] - 0s 43us/step - loss: 0.0827 - accuracy: 0.9933 - val_loss: 0.2081 - val_accuracy: 0.9500 Epoch 42/100 298/298 [==============================] - 0s 45us/step - loss: 0.0812 - accuracy: 0.9933 - val_loss: 0.1886 - val_accuracy: 0.9500 Epoch 43/100 298/298 [==============================] - 0s 45us/step - loss: 0.0806 - accuracy: 0.9966 - val_loss: 0.1966 - val_accuracy: 0.9400 Epoch 44/100 298/298 [==============================] - 0s 44us/step - loss: 0.0792 - accuracy: 0.9966 - val_loss: 0.1980 - val_accuracy: 0.9400 Epoch 45/100 298/298 [==============================] - 0s 46us/step - loss: 0.0783 - accuracy: 0.9933 - val_loss: 0.1982 - val_accuracy: 0.9400 Epoch 46/100 298/298 [==============================] - 0s 47us/step - loss: 0.0758 - accuracy: 0.9933 - val_loss: 0.2091 - val_accuracy: 0.9400 Epoch 47/100 298/298 [==============================] - 0s 46us/step - loss: 0.0746 - accuracy: 0.9966 - val_loss: 0.1947 - val_accuracy: 0.9400 Epoch 48/100 298/298 [==============================] - 0s 43us/step - loss: 0.0738 - accuracy: 0.9933 - val_loss: 0.2037 - val_accuracy: 0.9400 Epoch 49/100 298/298 [==============================] - 0s 44us/step - loss: 0.0739 - accuracy: 0.9966 - val_loss: 0.2046 - val_accuracy: 0.9400 Epoch 50/100 298/298 [==============================] - 0s 45us/step - loss: 0.0717 - accuracy: 0.9933 - val_loss: 0.1949 - val_accuracy: 0.9400 Epoch 51/100 298/298 [==============================] - 0s 45us/step - loss: 0.0716 - accuracy: 0.9933 - val_loss: 0.2047 - val_accuracy: 0.9400 Epoch 52/100 298/298 [==============================] - 0s 44us/step - loss: 0.0705 - accuracy: 0.9933 - val_loss: 0.1887 - val_accuracy: 0.9400 Epoch 53/100 298/298 [==============================] - 0s 47us/step - loss: 0.0688 - accuracy: 0.9966 - val_loss: 0.2146 - val_accuracy: 0.9400 Epoch 54/100 298/298 [==============================] - 0s 44us/step - loss: 0.0683 - accuracy: 0.9933 - val_loss: 0.2028 - val_accuracy: 0.9400 Epoch 55/100 298/298 [==============================] - 0s 45us/step - loss: 0.0661 - accuracy: 0.9966 - val_loss: 0.2061 - val_accuracy: 0.9400 Epoch 56/100 298/298 [==============================] - 0s 42us/step - loss: 0.0665 - accuracy: 0.9933 - val_loss: 0.1961 - val_accuracy: 0.9300 Epoch 57/100 298/298 [==============================] - 0s 44us/step - loss: 0.0675 - accuracy: 0.9899 - val_loss: 0.2006 - val_accuracy: 0.9400 Epoch 58/100 298/298 [==============================] - 0s 45us/step - loss: 0.0669 - accuracy: 0.9933 - val_loss: 0.2113 - val_accuracy: 0.9500 Epoch 59/100 298/298 [==============================] - 0s 43us/step - loss: 0.0645 - accuracy: 0.9966 - val_loss: 0.2031 - val_accuracy: 0.9400 Epoch 60/100 298/298 [==============================] - 0s 44us/step - loss: 0.0643 - accuracy: 0.9933 - val_loss: 0.2043 - val_accuracy: 0.9500 Epoch 61/100 298/298 [==============================] - 0s 46us/step - loss: 0.0637 - accuracy: 0.9966 - val_loss: 0.1952 - val_accuracy: 0.9400 Epoch 62/100 298/298 [==============================] - 0s 47us/step - loss: 0.0650 - accuracy: 0.9933 - val_loss: 0.1925 - val_accuracy: 0.9400 Epoch 63/100 298/298 [==============================] - 0s 47us/step - loss: 0.0626 - accuracy: 0.9933 - val_loss: 0.1824 - val_accuracy: 0.9600 Epoch 64/100 298/298 [==============================] - 0s 43us/step - loss: 0.0621 - accuracy: 0.9966 - val_loss: 0.1885 - val_accuracy: 0.9400 Epoch 65/100 298/298 [==============================] - 0s 47us/step - loss: 0.0623 - accuracy: 0.9899 - val_loss: 0.1959 - val_accuracy: 0.9400 Epoch 66/100 298/298 [==============================] - 0s 46us/step - loss: 0.0609 - accuracy: 0.9933 - val_loss: 0.1985 - val_accuracy: 0.9500 Epoch 67/100 298/298 [==============================] - 0s 44us/step - loss: 0.0614 - accuracy: 0.9933 - val_loss: 0.1771 - val_accuracy: 0.9500 Epoch 68/100 298/298 [==============================] - 0s 45us/step - loss: 0.0613 - accuracy: 0.9933 - val_loss: 0.1869 - val_accuracy: 0.9400 Epoch 69/100 298/298 [==============================] - 0s 47us/step - loss: 0.0603 - accuracy: 0.9933 - val_loss: 0.1881 - val_accuracy: 0.9400 Epoch 70/100 298/298 [==============================] - 0s 45us/step - loss: 0.0610 - accuracy: 0.9933 - val_loss: 0.1827 - val_accuracy: 0.9500 Epoch 71/100 298/298 [==============================] - 0s 47us/step - loss: 0.0603 - accuracy: 0.9933 - val_loss: 0.1765 - val_accuracy: 0.9500 Epoch 72/100 298/298 [==============================] - 0s 55us/step - loss: 0.0582 - accuracy: 0.9933 - val_loss: 0.1944 - val_accuracy: 0.9400 Epoch 73/100 298/298 [==============================] - 0s 49us/step - loss: 0.0578 - accuracy: 0.9933 - val_loss: 0.2090 - val_accuracy: 0.9300 Epoch 74/100 298/298 [==============================] - 0s 47us/step - loss: 0.0585 - accuracy: 0.9933 - val_loss: 0.2067 - val_accuracy: 0.9300 Epoch 75/100 298/298 [==============================] - 0s 45us/step - loss: 0.0591 - accuracy: 0.9933 - val_loss: 0.1766 - val_accuracy: 0.9600 Epoch 76/100 298/298 [==============================] - 0s 48us/step - loss: 0.0568 - accuracy: 0.9966 - val_loss: 0.1935 - val_accuracy: 0.9400 Epoch 77/100 298/298 [==============================] - 0s 48us/step - loss: 0.0567 - accuracy: 0.9933 - val_loss: 0.2086 - val_accuracy: 0.9400 Epoch 78/100 298/298 [==============================] - 0s 45us/step - loss: 0.0566 - accuracy: 0.9933 - val_loss: 0.1912 - val_accuracy: 0.9400 Epoch 79/100 298/298 [==============================] - 0s 45us/step - loss: 0.0570 - accuracy: 0.9966 - val_loss: 0.2071 - val_accuracy: 0.9300 Epoch 80/100 298/298 [==============================] - 0s 47us/step - loss: 0.0559 - accuracy: 0.9933 - val_loss: 0.2166 - val_accuracy: 0.9500 Epoch 81/100 298/298 [==============================] - 0s 46us/step - loss: 0.0553 - accuracy: 0.9933 - val_loss: 0.2027 - val_accuracy: 0.9300 Epoch 82/100 298/298 [==============================] - 0s 48us/step - loss: 0.0540 - accuracy: 0.9966 - val_loss: 0.2182 - val_accuracy: 0.9500 Epoch 83/100 298/298 [==============================] - 0s 51us/step - loss: 0.0555 - accuracy: 0.9933 - val_loss: 0.1963 - val_accuracy: 0.9400 Epoch 84/100 298/298 [==============================] - 0s 45us/step - loss: 0.0560 - accuracy: 0.9933 - val_loss: 0.1935 - val_accuracy: 0.9400 Epoch 85/100 298/298 [==============================] - 0s 47us/step - loss: 0.0547 - accuracy: 0.9933 - val_loss: 0.2085 - val_accuracy: 0.9500 Epoch 86/100 298/298 [==============================] - 0s 47us/step - loss: 0.0550 - accuracy: 0.9933 - val_loss: 0.2137 - val_accuracy: 0.9400 Epoch 87/100 298/298 [==============================] - 0s 42us/step - loss: 0.0533 - accuracy: 0.9966 - val_loss: 0.2002 - val_accuracy: 0.9300 Epoch 88/100 298/298 [==============================] - 0s 46us/step - loss: 0.0531 - accuracy: 0.9966 - val_loss: 0.2000 - val_accuracy: 0.9400 Epoch 89/100 298/298 [==============================] - 0s 46us/step - loss: 0.0528 - accuracy: 0.9933 - val_loss: 0.1908 - val_accuracy: 0.9500 Epoch 90/100 298/298 [==============================] - 0s 44us/step - loss: 0.0536 - accuracy: 0.9933 - val_loss: 0.1929 - val_accuracy: 0.9400 Epoch 91/100 298/298 [==============================] - 0s 45us/step - loss: 0.0540 - accuracy: 0.9933 - val_loss: 0.1930 - val_accuracy: 0.9500 Epoch 92/100 298/298 [==============================] - 0s 48us/step - loss: 0.0537 - accuracy: 0.9966 - val_loss: 0.1854 - val_accuracy: 0.9500 Epoch 93/100 298/298 [==============================] - 0s 44us/step - loss: 0.0518 - accuracy: 0.9933 - val_loss: 0.1974 - val_accuracy: 0.9500 Epoch 94/100 298/298 [==============================] - 0s 47us/step - loss: 0.0529 - accuracy: 0.9966 - val_loss: 0.2171 - val_accuracy: 0.9400 Epoch 95/100 298/298 [==============================] - 0s 44us/step - loss: 0.0534 - accuracy: 0.9966 - val_loss: 0.1952 - val_accuracy: 0.9400 Epoch 96/100 298/298 [==============================] - 0s 43us/step - loss: 0.0526 - accuracy: 0.9933 - val_loss: 0.1827 - val_accuracy: 0.9500 Epoch 97/100 298/298 [==============================] - 0s 43us/step - loss: 0.0505 - accuracy: 1.0000 - val_loss: 0.1992 - val_accuracy: 0.9400 Epoch 98/100 298/298 [==============================] - 0s 44us/step - loss: 0.0520 - accuracy: 0.9933 - val_loss: 0.1881 - val_accuracy: 0.9500 Epoch 99/100 298/298 [==============================] - 0s 44us/step - loss: 0.0501 - accuracy: 0.9966 - val_loss: 0.1923 - val_accuracy: 0.9400 Epoch 100/100 298/298 [==============================] - 0s 45us/step - loss: 0.0520 - accuracy: 0.9933 - val_loss: 0.1885 - val_accuracy: 0.9400 171/171 [==============================] - 0s 24us/step Optimizers: <keras.optimizers.RMSprop object at 0x10a1d9a50> Epoch Sizes: 100 Neurons or Units: 64 ['loss', 'accuracy'] [0.0825934979563568, 0.9941520690917969] Test score: 0.0825934979563568 Test accuracy: 0.9941520690917969 Model: "sequential_41" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_121 (Dense) (None, 128) 3968 _________________________________________________________________ activation_121 (Activation) (None, 128) 0 _________________________________________________________________ dense_122 (Dense) (None, 128) 16512 _________________________________________________________________ activation_122 (Activation) (None, 128) 0 _________________________________________________________________ dense_123 (Dense) (None, 1) 129 _________________________________________________________________ activation_123 (Activation) (None, 1) 0 ================================================================= Total params: 20,609 Trainable params: 20,609 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/100 298/298 [==============================] - 0s 545us/step - loss: 1.9808 - accuracy: 0.8859 - val_loss: 1.7220 - val_accuracy: 0.9100 Epoch 2/100 298/298 [==============================] - 0s 48us/step - loss: 1.5819 - accuracy: 0.9664 - val_loss: 1.4901 - val_accuracy: 0.9200 Epoch 3/100 298/298 [==============================] - 0s 47us/step - loss: 1.3575 - accuracy: 0.9732 - val_loss: 1.3068 - val_accuracy: 0.9200 Epoch 4/100 298/298 [==============================] - 0s 46us/step - loss: 1.1658 - accuracy: 0.9732 - val_loss: 1.1341 - val_accuracy: 0.9300 Epoch 5/100 298/298 [==============================] - 0s 46us/step - loss: 0.9963 - accuracy: 0.9765 - val_loss: 0.9780 - val_accuracy: 0.9400 Epoch 6/100 298/298 [==============================] - 0s 47us/step - loss: 0.8433 - accuracy: 0.9832 - val_loss: 0.8364 - val_accuracy: 0.9300 Epoch 7/100 298/298 [==============================] - 0s 46us/step - loss: 0.7121 - accuracy: 0.9832 - val_loss: 0.7172 - val_accuracy: 0.9400 Epoch 8/100 298/298 [==============================] - 0s 46us/step - loss: 0.5969 - accuracy: 0.9866 - val_loss: 0.6212 - val_accuracy: 0.9500 Epoch 9/100 298/298 [==============================] - 0s 46us/step - loss: 0.5011 - accuracy: 0.9933 - val_loss: 0.5457 - val_accuracy: 0.9500 Epoch 10/100 298/298 [==============================] - 0s 47us/step - loss: 0.4188 - accuracy: 0.9866 - val_loss: 0.4784 - val_accuracy: 0.9500 Epoch 11/100 298/298 [==============================] - 0s 47us/step - loss: 0.3502 - accuracy: 0.9899 - val_loss: 0.4122 - val_accuracy: 0.9500 Epoch 12/100 298/298 [==============================] - 0s 47us/step - loss: 0.2945 - accuracy: 0.9933 - val_loss: 0.3651 - val_accuracy: 0.9300 Epoch 13/100 298/298 [==============================] - 0s 47us/step - loss: 0.2489 - accuracy: 0.9899 - val_loss: 0.3332 - val_accuracy: 0.9400 Epoch 14/100 298/298 [==============================] - 0s 46us/step - loss: 0.2152 - accuracy: 0.9899 - val_loss: 0.3061 - val_accuracy: 0.9500 Epoch 15/100 298/298 [==============================] - 0s 44us/step - loss: 0.1876 - accuracy: 0.9899 - val_loss: 0.2750 - val_accuracy: 0.9400 Epoch 16/100 298/298 [==============================] - 0s 47us/step - loss: 0.1674 - accuracy: 0.9933 - val_loss: 0.2782 - val_accuracy: 0.9400 Epoch 17/100 298/298 [==============================] - 0s 46us/step - loss: 0.1526 - accuracy: 0.9933 - val_loss: 0.2558 - val_accuracy: 0.9500 Epoch 18/100 298/298 [==============================] - 0s 46us/step - loss: 0.1416 - accuracy: 0.9933 - val_loss: 0.2523 - val_accuracy: 0.9500 Epoch 19/100 298/298 [==============================] - 0s 46us/step - loss: 0.1288 - accuracy: 0.9933 - val_loss: 0.2260 - val_accuracy: 0.9400 Epoch 20/100 298/298 [==============================] - 0s 47us/step - loss: 0.1237 - accuracy: 0.9899 - val_loss: 0.2260 - val_accuracy: 0.9400 Epoch 21/100 298/298 [==============================] - 0s 47us/step - loss: 0.1155 - accuracy: 0.9899 - val_loss: 0.2304 - val_accuracy: 0.9400 Epoch 22/100 298/298 [==============================] - 0s 48us/step - loss: 0.1093 - accuracy: 0.9966 - val_loss: 0.2347 - val_accuracy: 0.9500 Epoch 23/100 298/298 [==============================] - 0s 47us/step - loss: 0.1082 - accuracy: 0.9933 - val_loss: 0.2236 - val_accuracy: 0.9400 Epoch 24/100 298/298 [==============================] - 0s 47us/step - loss: 0.1011 - accuracy: 0.9933 - val_loss: 0.2164 - val_accuracy: 0.9300 Epoch 25/100 298/298 [==============================] - 0s 45us/step - loss: 0.0984 - accuracy: 0.9966 - val_loss: 0.2218 - val_accuracy: 0.9400 Epoch 26/100 298/298 [==============================] - 0s 46us/step - loss: 0.0951 - accuracy: 0.9933 - val_loss: 0.2122 - val_accuracy: 0.9400 Epoch 27/100 298/298 [==============================] - 0s 45us/step - loss: 0.0922 - accuracy: 0.9933 - val_loss: 0.1919 - val_accuracy: 0.9500 Epoch 28/100 298/298 [==============================] - 0s 46us/step - loss: 0.0913 - accuracy: 0.9933 - val_loss: 0.2070 - val_accuracy: 0.9400 Epoch 29/100 298/298 [==============================] - 0s 46us/step - loss: 0.0870 - accuracy: 0.9933 - val_loss: 0.2148 - val_accuracy: 0.9400 Epoch 30/100 298/298 [==============================] - 0s 46us/step - loss: 0.0843 - accuracy: 0.9933 - val_loss: 0.2152 - val_accuracy: 0.9300 Epoch 31/100 298/298 [==============================] - 0s 48us/step - loss: 0.0858 - accuracy: 0.9933 - val_loss: 0.2006 - val_accuracy: 0.9400 Epoch 32/100 298/298 [==============================] - 0s 46us/step - loss: 0.0806 - accuracy: 0.9933 - val_loss: 0.2318 - val_accuracy: 0.9500 Epoch 33/100 298/298 [==============================] - 0s 46us/step - loss: 0.0836 - accuracy: 0.9933 - val_loss: 0.2122 - val_accuracy: 0.9500 Epoch 34/100 298/298 [==============================] - 0s 44us/step - loss: 0.0775 - accuracy: 0.9933 - val_loss: 0.2021 - val_accuracy: 0.9400 Epoch 35/100 298/298 [==============================] - 0s 45us/step - loss: 0.0783 - accuracy: 0.9933 - val_loss: 0.2259 - val_accuracy: 0.9500 Epoch 36/100 298/298 [==============================] - 0s 46us/step - loss: 0.0753 - accuracy: 0.9933 - val_loss: 0.2064 - val_accuracy: 0.9400 Epoch 37/100 298/298 [==============================] - 0s 48us/step - loss: 0.0738 - accuracy: 0.9966 - val_loss: 0.1964 - val_accuracy: 0.9500 Epoch 38/100 298/298 [==============================] - 0s 45us/step - loss: 0.0763 - accuracy: 0.9933 - val_loss: 0.2043 - val_accuracy: 0.9400 Epoch 39/100 298/298 [==============================] - 0s 44us/step - loss: 0.0730 - accuracy: 0.9933 - val_loss: 0.2324 - val_accuracy: 0.9400 Epoch 40/100 298/298 [==============================] - 0s 45us/step - loss: 0.0754 - accuracy: 0.9933 - val_loss: 0.2079 - val_accuracy: 0.9500 Epoch 41/100 298/298 [==============================] - 0s 47us/step - loss: 0.0713 - accuracy: 0.9899 - val_loss: 0.2048 - val_accuracy: 0.9500 Epoch 42/100 298/298 [==============================] - 0s 45us/step - loss: 0.0695 - accuracy: 0.9933 - val_loss: 0.2077 - val_accuracy: 0.9400 Epoch 43/100 298/298 [==============================] - 0s 43us/step - loss: 0.0680 - accuracy: 0.9966 - val_loss: 0.2073 - val_accuracy: 0.9400 Epoch 44/100 298/298 [==============================] - 0s 45us/step - loss: 0.0677 - accuracy: 0.9933 - val_loss: 0.2434 - val_accuracy: 0.9400 Epoch 45/100 298/298 [==============================] - 0s 45us/step - loss: 0.0660 - accuracy: 0.9966 - val_loss: 0.2357 - val_accuracy: 0.9500 Epoch 46/100 298/298 [==============================] - 0s 44us/step - loss: 0.0689 - accuracy: 0.9933 - val_loss: 0.2125 - val_accuracy: 0.9400 Epoch 47/100 298/298 [==============================] - 0s 45us/step - loss: 0.0669 - accuracy: 0.9899 - val_loss: 0.1904 - val_accuracy: 0.9500 Epoch 48/100 298/298 [==============================] - 0s 46us/step - loss: 0.0637 - accuracy: 0.9966 - val_loss: 0.1980 - val_accuracy: 0.9500 Epoch 49/100 298/298 [==============================] - 0s 49us/step - loss: 0.0660 - accuracy: 0.9933 - val_loss: 0.1685 - val_accuracy: 0.9600 Epoch 50/100 298/298 [==============================] - 0s 50us/step - loss: 0.0634 - accuracy: 0.9933 - val_loss: 0.2033 - val_accuracy: 0.9400 Epoch 51/100 298/298 [==============================] - 0s 48us/step - loss: 0.0639 - accuracy: 0.9933 - val_loss: 0.2019 - val_accuracy: 0.9400 Epoch 52/100 298/298 [==============================] - 0s 46us/step - loss: 0.0610 - accuracy: 0.9966 - val_loss: 0.1717 - val_accuracy: 0.9600 Epoch 53/100 298/298 [==============================] - 0s 46us/step - loss: 0.0601 - accuracy: 0.9966 - val_loss: 0.1962 - val_accuracy: 0.9500 Epoch 54/100 298/298 [==============================] - 0s 44us/step - loss: 0.0624 - accuracy: 0.9933 - val_loss: 0.2025 - val_accuracy: 0.9400 Epoch 55/100 298/298 [==============================] - 0s 45us/step - loss: 0.0607 - accuracy: 0.9933 - val_loss: 0.2266 - val_accuracy: 0.9500 Epoch 56/100 298/298 [==============================] - 0s 46us/step - loss: 0.0605 - accuracy: 0.9933 - val_loss: 0.2114 - val_accuracy: 0.9400 Epoch 57/100 298/298 [==============================] - 0s 46us/step - loss: 0.0616 - accuracy: 0.9933 - val_loss: 0.2246 - val_accuracy: 0.9500 Epoch 58/100 298/298 [==============================] - 0s 46us/step - loss: 0.0579 - accuracy: 0.9966 - val_loss: 0.1972 - val_accuracy: 0.9500 Epoch 59/100 298/298 [==============================] - 0s 47us/step - loss: 0.0572 - accuracy: 0.9966 - val_loss: 0.2001 - val_accuracy: 0.9500 Epoch 60/100 298/298 [==============================] - 0s 47us/step - loss: 0.0585 - accuracy: 0.9933 - val_loss: 0.1993 - val_accuracy: 0.9400 Epoch 61/100 298/298 [==============================] - 0s 47us/step - loss: 0.0576 - accuracy: 0.9933 - val_loss: 0.2084 - val_accuracy: 0.9400 Epoch 62/100 298/298 [==============================] - 0s 46us/step - loss: 0.0581 - accuracy: 0.9933 - val_loss: 0.2204 - val_accuracy: 0.9400 Epoch 63/100 298/298 [==============================] - 0s 44us/step - loss: 0.0592 - accuracy: 0.9899 - val_loss: 0.2028 - val_accuracy: 0.9500 Epoch 64/100 298/298 [==============================] - 0s 46us/step - loss: 0.0570 - accuracy: 0.9933 - val_loss: 0.1957 - val_accuracy: 0.9500 Epoch 65/100 298/298 [==============================] - 0s 46us/step - loss: 0.0563 - accuracy: 0.9933 - val_loss: 0.1934 - val_accuracy: 0.9500 Epoch 66/100 298/298 [==============================] - 0s 45us/step - loss: 0.0549 - accuracy: 0.9933 - val_loss: 0.2112 - val_accuracy: 0.9400 Epoch 67/100 298/298 [==============================] - 0s 44us/step - loss: 0.0563 - accuracy: 0.9933 - val_loss: 0.1701 - val_accuracy: 0.9600 Epoch 68/100 298/298 [==============================] - 0s 48us/step - loss: 0.0578 - accuracy: 0.9933 - val_loss: 0.1853 - val_accuracy: 0.9500 Epoch 69/100 298/298 [==============================] - 0s 47us/step - loss: 0.0533 - accuracy: 0.9933 - val_loss: 0.2037 - val_accuracy: 0.9400 Epoch 70/100 298/298 [==============================] - 0s 48us/step - loss: 0.0555 - accuracy: 0.9966 - val_loss: 0.2087 - val_accuracy: 0.9400 Epoch 71/100 298/298 [==============================] - 0s 45us/step - loss: 0.0548 - accuracy: 0.9899 - val_loss: 0.2141 - val_accuracy: 0.9400 Epoch 72/100 298/298 [==============================] - 0s 44us/step - loss: 0.0550 - accuracy: 0.9933 - val_loss: 0.1934 - val_accuracy: 0.9500 Epoch 73/100 298/298 [==============================] - 0s 45us/step - loss: 0.0518 - accuracy: 0.9933 - val_loss: 0.2204 - val_accuracy: 0.9400 Epoch 74/100 298/298 [==============================] - 0s 45us/step - loss: 0.0531 - accuracy: 0.9933 - val_loss: 0.1813 - val_accuracy: 0.9500 Epoch 75/100 298/298 [==============================] - 0s 44us/step - loss: 0.0511 - accuracy: 0.9966 - val_loss: 0.2244 - val_accuracy: 0.9400 Epoch 76/100 298/298 [==============================] - 0s 45us/step - loss: 0.0530 - accuracy: 0.9899 - val_loss: 0.2024 - val_accuracy: 0.9500 Epoch 77/100 298/298 [==============================] - 0s 46us/step - loss: 0.0521 - accuracy: 0.9933 - val_loss: 0.1708 - val_accuracy: 0.9600 Epoch 78/100 298/298 [==============================] - 0s 47us/step - loss: 0.0488 - accuracy: 0.9966 - val_loss: 0.2010 - val_accuracy: 0.9400 Epoch 79/100 298/298 [==============================] - 0s 49us/step - loss: 0.0505 - accuracy: 0.9966 - val_loss: 0.2215 - val_accuracy: 0.9400 Epoch 80/100 298/298 [==============================] - 0s 47us/step - loss: 0.0506 - accuracy: 0.9933 - val_loss: 0.1979 - val_accuracy: 0.9500 Epoch 81/100 298/298 [==============================] - 0s 44us/step - loss: 0.0507 - accuracy: 0.9966 - val_loss: 0.1983 - val_accuracy: 0.9500 Epoch 82/100 298/298 [==============================] - 0s 45us/step - loss: 0.0515 - accuracy: 0.9933 - val_loss: 0.1865 - val_accuracy: 0.9400 Epoch 83/100 298/298 [==============================] - 0s 45us/step - loss: 0.0499 - accuracy: 1.0000 - val_loss: 0.2185 - val_accuracy: 0.9500 Epoch 84/100 298/298 [==============================] - 0s 44us/step - loss: 0.0501 - accuracy: 0.9966 - val_loss: 0.1936 - val_accuracy: 0.9500 Epoch 85/100 298/298 [==============================] - 0s 44us/step - loss: 0.0496 - accuracy: 0.9966 - val_loss: 0.2149 - val_accuracy: 0.9500 Epoch 86/100 298/298 [==============================] - 0s 47us/step - loss: 0.0497 - accuracy: 0.9966 - val_loss: 0.2217 - val_accuracy: 0.9500 Epoch 87/100 298/298 [==============================] - 0s 46us/step - loss: 0.0507 - accuracy: 0.9933 - val_loss: 0.2078 - val_accuracy: 0.9300 Epoch 88/100 298/298 [==============================] - 0s 56us/step - loss: 0.0471 - accuracy: 0.9966 - val_loss: 0.2165 - val_accuracy: 0.9400 Epoch 89/100 298/298 [==============================] - 0s 48us/step - loss: 0.0515 - accuracy: 0.9933 - val_loss: 0.1953 - val_accuracy: 0.9500 Epoch 90/100 298/298 [==============================] - 0s 47us/step - loss: 0.0475 - accuracy: 0.9966 - val_loss: 0.2169 - val_accuracy: 0.9400 Epoch 91/100 298/298 [==============================] - 0s 48us/step - loss: 0.0463 - accuracy: 0.9966 - val_loss: 0.2449 - val_accuracy: 0.9400 Epoch 92/100 298/298 [==============================] - 0s 46us/step - loss: 0.0473 - accuracy: 0.9966 - val_loss: 0.2272 - val_accuracy: 0.9400 Epoch 93/100 298/298 [==============================] - 0s 46us/step - loss: 0.0504 - accuracy: 0.9933 - val_loss: 0.2194 - val_accuracy: 0.9400 Epoch 94/100 298/298 [==============================] - 0s 49us/step - loss: 0.0468 - accuracy: 0.9966 - val_loss: 0.2181 - val_accuracy: 0.9400 Epoch 95/100 298/298 [==============================] - 0s 52us/step - loss: 0.0485 - accuracy: 0.9933 - val_loss: 0.2082 - val_accuracy: 0.9500 Epoch 96/100 298/298 [==============================] - 0s 49us/step - loss: 0.0477 - accuracy: 0.9933 - val_loss: 0.1794 - val_accuracy: 0.9500 Epoch 97/100 298/298 [==============================] - 0s 48us/step - loss: 0.0463 - accuracy: 1.0000 - val_loss: 0.2200 - val_accuracy: 0.9400 Epoch 98/100 298/298 [==============================] - 0s 45us/step - loss: 0.0496 - accuracy: 0.9966 - val_loss: 0.2005 - val_accuracy: 0.9500 Epoch 99/100 298/298 [==============================] - 0s 48us/step - loss: 0.0453 - accuracy: 0.9933 - val_loss: 0.2012 - val_accuracy: 0.9500 Epoch 100/100 298/298 [==============================] - 0s 47us/step - loss: 0.0461 - accuracy: 0.9966 - val_loss: 0.1872 - val_accuracy: 0.9500 171/171 [==============================] - 0s 31us/step Optimizers: <keras.optimizers.RMSprop object at 0x10a1d9a50> Epoch Sizes: 100 Neurons or Units: 128 ['loss', 'accuracy'] [0.07920495294339476, 0.9824561476707458] Test score: 0.07920495294339476 Test accuracy: 0.9824561476707458 Model: "sequential_42" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_124 (Dense) (None, 256) 7936 _________________________________________________________________ activation_124 (Activation) (None, 256) 0 _________________________________________________________________ dense_125 (Dense) (None, 256) 65792 _________________________________________________________________ activation_125 (Activation) (None, 256) 0 _________________________________________________________________ dense_126 (Dense) (None, 1) 257 _________________________________________________________________ activation_126 (Activation) (None, 1) 0 ================================================================= Total params: 73,985 Trainable params: 73,985 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/100 298/298 [==============================] - 0s 551us/step - loss: 2.9825 - accuracy: 0.9060 - val_loss: 2.4901 - val_accuracy: 0.9000 Epoch 2/100 298/298 [==============================] - 0s 78us/step - loss: 2.1981 - accuracy: 0.9799 - val_loss: 1.9504 - val_accuracy: 0.9400 Epoch 3/100 298/298 [==============================] - 0s 150us/step - loss: 1.7120 - accuracy: 0.9832 - val_loss: 1.5663 - val_accuracy: 0.9100 Epoch 4/100 298/298 [==============================] - 0s 102us/step - loss: 1.3284 - accuracy: 0.9765 - val_loss: 1.2031 - val_accuracy: 0.9500 Epoch 5/100 298/298 [==============================] - 0s 75us/step - loss: 1.0154 - accuracy: 0.9832 - val_loss: 0.9472 - val_accuracy: 0.9500 Epoch 6/100 298/298 [==============================] - 0s 78us/step - loss: 0.7712 - accuracy: 0.9866 - val_loss: 0.7340 - val_accuracy: 0.9500 Epoch 7/100 298/298 [==============================] - 0s 75us/step - loss: 0.5798 - accuracy: 0.9866 - val_loss: 0.5853 - val_accuracy: 0.9500 Epoch 8/100 298/298 [==============================] - 0s 77us/step - loss: 0.4347 - accuracy: 0.9933 - val_loss: 0.4545 - val_accuracy: 0.9400 Epoch 9/100 298/298 [==============================] - 0s 74us/step - loss: 0.3346 - accuracy: 0.9966 - val_loss: 0.4003 - val_accuracy: 0.9300 Epoch 10/100 298/298 [==============================] - 0s 81us/step - loss: 0.2659 - accuracy: 0.9899 - val_loss: 0.3323 - val_accuracy: 0.9500 Epoch 11/100 298/298 [==============================] - 0s 69us/step - loss: 0.2123 - accuracy: 0.9933 - val_loss: 0.3103 - val_accuracy: 0.9500 Epoch 12/100 298/298 [==============================] - 0s 76us/step - loss: 0.1797 - accuracy: 0.9933 - val_loss: 0.2812 - val_accuracy: 0.9500 Epoch 13/100 298/298 [==============================] - 0s 78us/step - loss: 0.1554 - accuracy: 0.9933 - val_loss: 0.2538 - val_accuracy: 0.9500 Epoch 14/100 298/298 [==============================] - 0s 76us/step - loss: 0.1402 - accuracy: 0.9866 - val_loss: 0.2495 - val_accuracy: 0.9500 Epoch 15/100 298/298 [==============================] - 0s 68us/step - loss: 0.1294 - accuracy: 0.9899 - val_loss: 0.2057 - val_accuracy: 0.9600 Epoch 16/100 298/298 [==============================] - 0s 72us/step - loss: 0.1213 - accuracy: 0.9899 - val_loss: 0.2428 - val_accuracy: 0.9500 Epoch 17/100 298/298 [==============================] - 0s 75us/step - loss: 0.1127 - accuracy: 0.9933 - val_loss: 0.1972 - val_accuracy: 0.9600 Epoch 18/100 298/298 [==============================] - 0s 64us/step - loss: 0.1054 - accuracy: 0.9966 - val_loss: 0.2400 - val_accuracy: 0.9400 Epoch 19/100 298/298 [==============================] - 0s 70us/step - loss: 0.1037 - accuracy: 0.9899 - val_loss: 0.2233 - val_accuracy: 0.9500 Epoch 20/100 298/298 [==============================] - 0s 78us/step - loss: 0.0972 - accuracy: 0.9933 - val_loss: 0.2043 - val_accuracy: 0.9400 Epoch 21/100 298/298 [==============================] - 0s 67us/step - loss: 0.0968 - accuracy: 0.9899 - val_loss: 0.1901 - val_accuracy: 0.9600 Epoch 22/100 298/298 [==============================] - 0s 69us/step - loss: 0.0928 - accuracy: 0.9933 - val_loss: 0.1742 - val_accuracy: 0.9600 Epoch 23/100 298/298 [==============================] - 0s 68us/step - loss: 0.0907 - accuracy: 0.9933 - val_loss: 0.2066 - val_accuracy: 0.9400 Epoch 24/100 298/298 [==============================] - 0s 72us/step - loss: 0.0876 - accuracy: 0.9933 - val_loss: 0.1950 - val_accuracy: 0.9400 Epoch 25/100 298/298 [==============================] - 0s 68us/step - loss: 0.0846 - accuracy: 0.9933 - val_loss: 0.1977 - val_accuracy: 0.9400 Epoch 26/100 298/298 [==============================] - 0s 68us/step - loss: 0.0811 - accuracy: 0.9966 - val_loss: 0.1985 - val_accuracy: 0.9400 Epoch 27/100 298/298 [==============================] - 0s 65us/step - loss: 0.0820 - accuracy: 0.9933 - val_loss: 0.2107 - val_accuracy: 0.9400 Epoch 28/100 298/298 [==============================] - 0s 69us/step - loss: 0.0792 - accuracy: 0.9899 - val_loss: 0.2133 - val_accuracy: 0.9400 Epoch 29/100 298/298 [==============================] - 0s 75us/step - loss: 0.0806 - accuracy: 0.9899 - val_loss: 0.2155 - val_accuracy: 0.9400 Epoch 30/100 298/298 [==============================] - 0s 66us/step - loss: 0.0774 - accuracy: 0.9933 - val_loss: 0.1981 - val_accuracy: 0.9400 Epoch 31/100 298/298 [==============================] - 0s 69us/step - loss: 0.0768 - accuracy: 0.9933 - val_loss: 0.2029 - val_accuracy: 0.9400 Epoch 32/100 298/298 [==============================] - 0s 75us/step - loss: 0.0703 - accuracy: 0.9933 - val_loss: 0.1957 - val_accuracy: 0.9400 Epoch 33/100 298/298 [==============================] - 0s 66us/step - loss: 0.0790 - accuracy: 0.9899 - val_loss: 0.2243 - val_accuracy: 0.9400 Epoch 34/100 298/298 [==============================] - 0s 69us/step - loss: 0.0704 - accuracy: 0.9966 - val_loss: 0.2318 - val_accuracy: 0.9400 Epoch 35/100 298/298 [==============================] - 0s 78us/step - loss: 0.0712 - accuracy: 0.9899 - val_loss: 0.2043 - val_accuracy: 0.9400 Epoch 36/100 298/298 [==============================] - 0s 69us/step - loss: 0.0688 - accuracy: 0.9933 - val_loss: 0.2311 - val_accuracy: 0.9400 Epoch 37/100 298/298 [==============================] - 0s 72us/step - loss: 0.0685 - accuracy: 0.9933 - val_loss: 0.2009 - val_accuracy: 0.9300 Epoch 38/100 298/298 [==============================] - 0s 78us/step - loss: 0.0682 - accuracy: 0.9933 - val_loss: 0.2025 - val_accuracy: 0.9400 Epoch 39/100 298/298 [==============================] - 0s 69us/step - loss: 0.0655 - accuracy: 0.9933 - val_loss: 0.2082 - val_accuracy: 0.9400 Epoch 40/100 298/298 [==============================] - 0s 71us/step - loss: 0.0658 - accuracy: 0.9933 - val_loss: 0.2004 - val_accuracy: 0.9500 Epoch 41/100 298/298 [==============================] - 0s 74us/step - loss: 0.0654 - accuracy: 0.9933 - val_loss: 0.2131 - val_accuracy: 0.9400 Epoch 42/100 298/298 [==============================] - 0s 72us/step - loss: 0.0629 - accuracy: 0.9933 - val_loss: 0.1819 - val_accuracy: 0.9500 Epoch 43/100 298/298 [==============================] - 0s 62us/step - loss: 0.0637 - accuracy: 0.9933 - val_loss: 0.2464 - val_accuracy: 0.9400 Epoch 44/100 298/298 [==============================] - 0s 75us/step - loss: 0.0678 - accuracy: 0.9899 - val_loss: 0.2333 - val_accuracy: 0.9300 Epoch 45/100 298/298 [==============================] - 0s 72us/step - loss: 0.0605 - accuracy: 0.9933 - val_loss: 0.2051 - val_accuracy: 0.9300 Epoch 46/100 298/298 [==============================] - 0s 64us/step - loss: 0.0617 - accuracy: 0.9933 - val_loss: 0.2064 - val_accuracy: 0.9400 Epoch 47/100 298/298 [==============================] - 0s 76us/step - loss: 0.0602 - accuracy: 0.9933 - val_loss: 0.2223 - val_accuracy: 0.9300 Epoch 48/100 298/298 [==============================] - 0s 69us/step - loss: 0.0577 - accuracy: 0.9966 - val_loss: 0.2212 - val_accuracy: 0.9500 Epoch 49/100 298/298 [==============================] - 0s 72us/step - loss: 0.0624 - accuracy: 0.9933 - val_loss: 0.2098 - val_accuracy: 0.9400 Epoch 50/100 298/298 [==============================] - 0s 70us/step - loss: 0.0588 - accuracy: 0.9933 - val_loss: 0.2158 - val_accuracy: 0.9400 Epoch 51/100 298/298 [==============================] - 0s 71us/step - loss: 0.0555 - accuracy: 0.9933 - val_loss: 0.1928 - val_accuracy: 0.9500 Epoch 52/100 298/298 [==============================] - 0s 73us/step - loss: 0.0582 - accuracy: 0.9933 - val_loss: 0.1965 - val_accuracy: 0.9400 Epoch 53/100 298/298 [==============================] - 0s 75us/step - loss: 0.0543 - accuracy: 0.9966 - val_loss: 0.2145 - val_accuracy: 0.9400 Epoch 54/100 298/298 [==============================] - 0s 71us/step - loss: 0.0574 - accuracy: 0.9933 - val_loss: 0.2038 - val_accuracy: 0.9500 Epoch 55/100 298/298 [==============================] - 0s 65us/step - loss: 0.0581 - accuracy: 0.9933 - val_loss: 0.1898 - val_accuracy: 0.9500 Epoch 56/100 298/298 [==============================] - 0s 72us/step - loss: 0.0548 - accuracy: 0.9933 - val_loss: 0.2216 - val_accuracy: 0.9400 Epoch 57/100 298/298 [==============================] - 0s 75us/step - loss: 0.0588 - accuracy: 0.9899 - val_loss: 0.2154 - val_accuracy: 0.9300 Epoch 58/100 298/298 [==============================] - 0s 66us/step - loss: 0.0547 - accuracy: 0.9933 - val_loss: 0.1935 - val_accuracy: 0.9400 Epoch 59/100 298/298 [==============================] - 0s 72us/step - loss: 0.0523 - accuracy: 0.9966 - val_loss: 0.2395 - val_accuracy: 0.9400 Epoch 60/100 298/298 [==============================] - 0s 77us/step - loss: 0.0534 - accuracy: 0.9933 - val_loss: 0.2267 - val_accuracy: 0.9400 Epoch 61/100 298/298 [==============================] - 0s 71us/step - loss: 0.0562 - accuracy: 0.9933 - val_loss: 0.2123 - val_accuracy: 0.9400 Epoch 62/100 298/298 [==============================] - 0s 75us/step - loss: 0.0536 - accuracy: 0.9899 - val_loss: 0.2116 - val_accuracy: 0.9400 Epoch 63/100 298/298 [==============================] - 0s 79us/step - loss: 0.0546 - accuracy: 0.9933 - val_loss: 0.2099 - val_accuracy: 0.9400 Epoch 64/100 298/298 [==============================] - 0s 70us/step - loss: 0.0527 - accuracy: 0.9899 - val_loss: 0.2091 - val_accuracy: 0.9400 Epoch 65/100 298/298 [==============================] - 0s 64us/step - loss: 0.0509 - accuracy: 0.9966 - val_loss: 0.1997 - val_accuracy: 0.9400 Epoch 66/100 298/298 [==============================] - 0s 75us/step - loss: 0.0571 - accuracy: 0.9933 - val_loss: 0.1996 - val_accuracy: 0.9500 Epoch 67/100 298/298 [==============================] - 0s 77us/step - loss: 0.0508 - accuracy: 0.9966 - val_loss: 0.2152 - val_accuracy: 0.9400 Epoch 68/100 298/298 [==============================] - 0s 66us/step - loss: 0.0501 - accuracy: 0.9966 - val_loss: 0.2175 - val_accuracy: 0.9500 Epoch 69/100 298/298 [==============================] - 0s 70us/step - loss: 0.0487 - accuracy: 0.9966 - val_loss: 0.2307 - val_accuracy: 0.9400 Epoch 70/100 298/298 [==============================] - 0s 78us/step - loss: 0.0508 - accuracy: 0.9966 - val_loss: 0.1747 - val_accuracy: 0.9500 Epoch 71/100 298/298 [==============================] - 0s 65us/step - loss: 0.0474 - accuracy: 0.9966 - val_loss: 0.1952 - val_accuracy: 0.9400 Epoch 72/100 298/298 [==============================] - 0s 70us/step - loss: 0.0509 - accuracy: 0.9966 - val_loss: 0.2300 - val_accuracy: 0.9400 Epoch 73/100 298/298 [==============================] - 0s 75us/step - loss: 0.0502 - accuracy: 0.9966 - val_loss: 0.2389 - val_accuracy: 0.9400 Epoch 74/100 298/298 [==============================] - 0s 67us/step - loss: 0.0472 - accuracy: 0.9966 - val_loss: 0.1929 - val_accuracy: 0.9500 Epoch 75/100 298/298 [==============================] - 0s 71us/step - loss: 0.0492 - accuracy: 0.9933 - val_loss: 0.1993 - val_accuracy: 0.9400 Epoch 76/100 298/298 [==============================] - 0s 75us/step - loss: 0.0489 - accuracy: 0.9966 - val_loss: 0.2330 - val_accuracy: 0.9500 Epoch 77/100 298/298 [==============================] - 0s 64us/step - loss: 0.0488 - accuracy: 0.9933 - val_loss: 0.2181 - val_accuracy: 0.9400 Epoch 78/100 298/298 [==============================] - 0s 67us/step - loss: 0.0480 - accuracy: 0.9933 - val_loss: 0.1778 - val_accuracy: 0.9600 Epoch 79/100 298/298 [==============================] - 0s 74us/step - loss: 0.0494 - accuracy: 0.9933 - val_loss: 0.2420 - val_accuracy: 0.9400 Epoch 80/100 298/298 [==============================] - 0s 70us/step - loss: 0.0450 - accuracy: 0.9966 - val_loss: 0.2156 - val_accuracy: 0.9300 Epoch 81/100 298/298 [==============================] - 0s 70us/step - loss: 0.0465 - accuracy: 0.9966 - val_loss: 0.2146 - val_accuracy: 0.9400 Epoch 82/100 298/298 [==============================] - 0s 77us/step - loss: 0.0530 - accuracy: 0.9899 - val_loss: 0.2177 - val_accuracy: 0.9300 Epoch 83/100 298/298 [==============================] - 0s 64us/step - loss: 0.0460 - accuracy: 0.9933 - val_loss: 0.1727 - val_accuracy: 0.9600 Epoch 84/100 298/298 [==============================] - 0s 70us/step - loss: 0.0447 - accuracy: 1.0000 - val_loss: 0.2082 - val_accuracy: 0.9400 Epoch 85/100 298/298 [==============================] - 0s 79us/step - loss: 0.0442 - accuracy: 1.0000 - val_loss: 0.2228 - val_accuracy: 0.9400 Epoch 86/100 298/298 [==============================] - 0s 65us/step - loss: 0.0448 - accuracy: 1.0000 - val_loss: 0.1963 - val_accuracy: 0.9500 Epoch 87/100 298/298 [==============================] - 0s 68us/step - loss: 0.0462 - accuracy: 0.9966 - val_loss: 0.1676 - val_accuracy: 0.9600 Epoch 88/100 298/298 [==============================] - 0s 73us/step - loss: 0.0473 - accuracy: 0.9933 - val_loss: 0.1990 - val_accuracy: 0.9500 Epoch 89/100 298/298 [==============================] - 0s 67us/step - loss: 0.0441 - accuracy: 0.9933 - val_loss: 0.2017 - val_accuracy: 0.9500 Epoch 90/100 298/298 [==============================] - 0s 73us/step - loss: 0.0436 - accuracy: 0.9966 - val_loss: 0.1781 - val_accuracy: 0.9600 Epoch 91/100 298/298 [==============================] - 0s 75us/step - loss: 0.0442 - accuracy: 1.0000 - val_loss: 0.2197 - val_accuracy: 0.9300 Epoch 92/100 298/298 [==============================] - 0s 69us/step - loss: 0.0467 - accuracy: 0.9966 - val_loss: 0.2382 - val_accuracy: 0.9400 Epoch 93/100 298/298 [==============================] - 0s 63us/step - loss: 0.0450 - accuracy: 0.9899 - val_loss: 0.1861 - val_accuracy: 0.9500 Epoch 94/100 298/298 [==============================] - 0s 73us/step - loss: 0.0444 - accuracy: 0.9966 - val_loss: 0.2267 - val_accuracy: 0.9400 Epoch 95/100 298/298 [==============================] - 0s 65us/step - loss: 0.0448 - accuracy: 0.9966 - val_loss: 0.1989 - val_accuracy: 0.9500 Epoch 96/100 298/298 [==============================] - 0s 71us/step - loss: 0.0419 - accuracy: 1.0000 - val_loss: 0.2057 - val_accuracy: 0.9400 Epoch 97/100 298/298 [==============================] - 0s 75us/step - loss: 0.0499 - accuracy: 0.9933 - val_loss: 0.2009 - val_accuracy: 0.9400 Epoch 98/100 298/298 [==============================] - 0s 67us/step - loss: 0.0432 - accuracy: 1.0000 - val_loss: 0.1986 - val_accuracy: 0.9500 Epoch 99/100 298/298 [==============================] - 0s 71us/step - loss: 0.0405 - accuracy: 1.0000 - val_loss: 0.2368 - val_accuracy: 0.9300 Epoch 100/100 298/298 [==============================] - 0s 80us/step - loss: 0.0461 - accuracy: 0.9966 - val_loss: 0.1988 - val_accuracy: 0.9500 171/171 [==============================] - 0s 28us/step Optimizers: <keras.optimizers.RMSprop object at 0x10a1d9a50> Epoch Sizes: 100 Neurons or Units: 256 ['loss', 'accuracy'] [0.08117412688613634, 0.988304078578949] Test score: 0.08117412688613634 Test accuracy: 0.988304078578949 Model: "sequential_43" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_127 (Dense) (None, 64) 1984 _________________________________________________________________ activation_127 (Activation) (None, 64) 0 _________________________________________________________________ dense_128 (Dense) (None, 64) 4160 _________________________________________________________________ activation_128 (Activation) (None, 64) 0 _________________________________________________________________ dense_129 (Dense) (None, 1) 65 _________________________________________________________________ activation_129 (Activation) (None, 1) 0 ================================================================= Total params: 6,209 Trainable params: 6,209 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/200 298/298 [==============================] - 0s 548us/step - loss: 1.5018 - accuracy: 0.8221 - val_loss: 1.3010 - val_accuracy: 0.9100 Epoch 2/200 298/298 [==============================] - 0s 47us/step - loss: 1.2169 - accuracy: 0.9396 - val_loss: 1.1499 - val_accuracy: 0.9100 Epoch 3/200 298/298 [==============================] - 0s 51us/step - loss: 1.0769 - accuracy: 0.9664 - val_loss: 1.0444 - val_accuracy: 0.9100 Epoch 4/200 298/298 [==============================] - 0s 48us/step - loss: 0.9685 - accuracy: 0.9732 - val_loss: 0.9558 - val_accuracy: 0.9100 Epoch 5/200 298/298 [==============================] - 0s 50us/step - loss: 0.8753 - accuracy: 0.9732 - val_loss: 0.8793 - val_accuracy: 0.9100 Epoch 6/200 298/298 [==============================] - 0s 43us/step - loss: 0.7923 - accuracy: 0.9799 - val_loss: 0.8063 - val_accuracy: 0.9100 Epoch 7/200 298/298 [==============================] - 0s 47us/step - loss: 0.7172 - accuracy: 0.9765 - val_loss: 0.7447 - val_accuracy: 0.9100 Epoch 8/200 298/298 [==============================] - 0s 45us/step - loss: 0.6483 - accuracy: 0.9765 - val_loss: 0.6845 - val_accuracy: 0.9100 Epoch 9/200 298/298 [==============================] - 0s 52us/step - loss: 0.5831 - accuracy: 0.9799 - val_loss: 0.6258 - val_accuracy: 0.9200 Epoch 10/200 298/298 [==============================] - 0s 44us/step - loss: 0.5241 - accuracy: 0.9765 - val_loss: 0.5732 - val_accuracy: 0.9300 Epoch 11/200 298/298 [==============================] - 0s 47us/step - loss: 0.4708 - accuracy: 0.9832 - val_loss: 0.5169 - val_accuracy: 0.9400 Epoch 12/200 298/298 [==============================] - 0s 43us/step - loss: 0.4230 - accuracy: 0.9899 - val_loss: 0.4763 - val_accuracy: 0.9500 Epoch 13/200 298/298 [==============================] - 0s 52us/step - loss: 0.3802 - accuracy: 0.9899 - val_loss: 0.4393 - val_accuracy: 0.9500 Epoch 14/200 298/298 [==============================] - 0s 45us/step - loss: 0.3413 - accuracy: 0.9899 - val_loss: 0.3998 - val_accuracy: 0.9500 Epoch 15/200 298/298 [==============================] - 0s 48us/step - loss: 0.3060 - accuracy: 0.9933 - val_loss: 0.3777 - val_accuracy: 0.9500 Epoch 16/200 298/298 [==============================] - 0s 49us/step - loss: 0.2775 - accuracy: 0.9899 - val_loss: 0.3531 - val_accuracy: 0.9500 Epoch 17/200 298/298 [==============================] - 0s 46us/step - loss: 0.2515 - accuracy: 0.9899 - val_loss: 0.3358 - val_accuracy: 0.9500 Epoch 18/200 298/298 [==============================] - 0s 49us/step - loss: 0.2295 - accuracy: 0.9899 - val_loss: 0.3148 - val_accuracy: 0.9500 Epoch 19/200 298/298 [==============================] - 0s 46us/step - loss: 0.2095 - accuracy: 0.9899 - val_loss: 0.2910 - val_accuracy: 0.9400 Epoch 20/200 298/298 [==============================] - 0s 52us/step - loss: 0.1920 - accuracy: 0.9933 - val_loss: 0.2807 - val_accuracy: 0.9500 Epoch 21/200 298/298 [==============================] - 0s 43us/step - loss: 0.1777 - accuracy: 0.9899 - val_loss: 0.2673 - val_accuracy: 0.9400 Epoch 22/200 298/298 [==============================] - 0s 50us/step - loss: 0.1646 - accuracy: 0.9966 - val_loss: 0.2582 - val_accuracy: 0.9400 Epoch 23/200 298/298 [==============================] - 0s 43us/step - loss: 0.1536 - accuracy: 0.9933 - val_loss: 0.2469 - val_accuracy: 0.9400 Epoch 24/200 298/298 [==============================] - 0s 52us/step - loss: 0.1434 - accuracy: 0.9966 - val_loss: 0.2446 - val_accuracy: 0.9300 Epoch 25/200 298/298 [==============================] - 0s 44us/step - loss: 0.1371 - accuracy: 0.9933 - val_loss: 0.2291 - val_accuracy: 0.9400 Epoch 26/200 298/298 [==============================] - 0s 47us/step - loss: 0.1303 - accuracy: 0.9933 - val_loss: 0.2304 - val_accuracy: 0.9400 Epoch 27/200 298/298 [==============================] - 0s 45us/step - loss: 0.1249 - accuracy: 0.9933 - val_loss: 0.2197 - val_accuracy: 0.9400 Epoch 28/200 298/298 [==============================] - 0s 55us/step - loss: 0.1197 - accuracy: 0.9966 - val_loss: 0.2212 - val_accuracy: 0.9400 Epoch 29/200 298/298 [==============================] - 0s 48us/step - loss: 0.1150 - accuracy: 0.9966 - val_loss: 0.2144 - val_accuracy: 0.9400 Epoch 30/200 298/298 [==============================] - 0s 44us/step - loss: 0.1114 - accuracy: 0.9966 - val_loss: 0.2186 - val_accuracy: 0.9400 Epoch 31/200 298/298 [==============================] - 0s 50us/step - loss: 0.1088 - accuracy: 0.9933 - val_loss: 0.2219 - val_accuracy: 0.9400 Epoch 32/200 298/298 [==============================] - 0s 49us/step - loss: 0.1043 - accuracy: 0.9933 - val_loss: 0.2252 - val_accuracy: 0.9500 Epoch 33/200 298/298 [==============================] - 0s 51us/step - loss: 0.1025 - accuracy: 0.9933 - val_loss: 0.2238 - val_accuracy: 0.9400 Epoch 34/200 298/298 [==============================] - 0s 51us/step - loss: 0.0994 - accuracy: 0.9933 - val_loss: 0.2239 - val_accuracy: 0.9500 Epoch 35/200 298/298 [==============================] - 0s 49us/step - loss: 0.0974 - accuracy: 0.9899 - val_loss: 0.2125 - val_accuracy: 0.9400 Epoch 36/200 298/298 [==============================] - 0s 46us/step - loss: 0.0941 - accuracy: 0.9933 - val_loss: 0.2143 - val_accuracy: 0.9400 Epoch 37/200 298/298 [==============================] - 0s 50us/step - loss: 0.0932 - accuracy: 0.9933 - val_loss: 0.2122 - val_accuracy: 0.9500 Epoch 38/200 298/298 [==============================] - 0s 46us/step - loss: 0.0914 - accuracy: 0.9966 - val_loss: 0.2103 - val_accuracy: 0.9500 Epoch 39/200 298/298 [==============================] - 0s 45us/step - loss: 0.0895 - accuracy: 0.9933 - val_loss: 0.2043 - val_accuracy: 0.9400 Epoch 40/200 298/298 [==============================] - 0s 44us/step - loss: 0.0870 - accuracy: 0.9933 - val_loss: 0.2060 - val_accuracy: 0.9400 Epoch 41/200 298/298 [==============================] - 0s 54us/step - loss: 0.0867 - accuracy: 0.9933 - val_loss: 0.1851 - val_accuracy: 0.9600 Epoch 42/200 298/298 [==============================] - 0s 44us/step - loss: 0.0853 - accuracy: 0.9933 - val_loss: 0.1950 - val_accuracy: 0.9500 Epoch 43/200 298/298 [==============================] - 0s 50us/step - loss: 0.0838 - accuracy: 0.9933 - val_loss: 0.1963 - val_accuracy: 0.9400 Epoch 44/200 298/298 [==============================] - 0s 44us/step - loss: 0.0834 - accuracy: 0.9933 - val_loss: 0.1904 - val_accuracy: 0.9500 Epoch 45/200 298/298 [==============================] - 0s 55us/step - loss: 0.0806 - accuracy: 0.9933 - val_loss: 0.1997 - val_accuracy: 0.9400 Epoch 46/200 298/298 [==============================] - 0s 43us/step - loss: 0.0786 - accuracy: 0.9933 - val_loss: 0.1940 - val_accuracy: 0.9500 Epoch 47/200 298/298 [==============================] - 0s 47us/step - loss: 0.0780 - accuracy: 0.9966 - val_loss: 0.2044 - val_accuracy: 0.9400 Epoch 48/200 298/298 [==============================] - 0s 53us/step - loss: 0.0763 - accuracy: 0.9966 - val_loss: 0.1893 - val_accuracy: 0.9500 Epoch 49/200 298/298 [==============================] - 0s 48us/step - loss: 0.0758 - accuracy: 0.9966 - val_loss: 0.2011 - val_accuracy: 0.9400 Epoch 50/200 298/298 [==============================] - 0s 51us/step - loss: 0.0750 - accuracy: 0.9933 - val_loss: 0.1859 - val_accuracy: 0.9500 Epoch 51/200 298/298 [==============================] - 0s 45us/step - loss: 0.0739 - accuracy: 0.9933 - val_loss: 0.2099 - val_accuracy: 0.9500 Epoch 52/200 298/298 [==============================] - 0s 51us/step - loss: 0.0728 - accuracy: 0.9933 - val_loss: 0.2009 - val_accuracy: 0.9400 Epoch 53/200 298/298 [==============================] - 0s 46us/step - loss: 0.0722 - accuracy: 0.9933 - val_loss: 0.1895 - val_accuracy: 0.9500 Epoch 54/200 298/298 [==============================] - 0s 48us/step - loss: 0.0696 - accuracy: 0.9966 - val_loss: 0.1789 - val_accuracy: 0.9500 Epoch 55/200 298/298 [==============================] - 0s 42us/step - loss: 0.0707 - accuracy: 0.9933 - val_loss: 0.1805 - val_accuracy: 0.9600 Epoch 56/200 298/298 [==============================] - 0s 50us/step - loss: 0.0724 - accuracy: 0.9899 - val_loss: 0.1911 - val_accuracy: 0.9500 Epoch 57/200 298/298 [==============================] - 0s 43us/step - loss: 0.0688 - accuracy: 0.9933 - val_loss: 0.1969 - val_accuracy: 0.9400 Epoch 58/200 298/298 [==============================] - 0s 52us/step - loss: 0.0687 - accuracy: 0.9899 - val_loss: 0.1844 - val_accuracy: 0.9500 Epoch 59/200 298/298 [==============================] - 0s 42us/step - loss: 0.0677 - accuracy: 0.9899 - val_loss: 0.1824 - val_accuracy: 0.9400 Epoch 60/200 298/298 [==============================] - 0s 48us/step - loss: 0.0693 - accuracy: 0.9933 - val_loss: 0.1840 - val_accuracy: 0.9500 Epoch 61/200 298/298 [==============================] - 0s 49us/step - loss: 0.0668 - accuracy: 0.9933 - val_loss: 0.1906 - val_accuracy: 0.9500 Epoch 62/200 298/298 [==============================] - 0s 53us/step - loss: 0.0642 - accuracy: 0.9966 - val_loss: 0.2181 - val_accuracy: 0.9400 Epoch 63/200 298/298 [==============================] - 0s 46us/step - loss: 0.0665 - accuracy: 0.9899 - val_loss: 0.1957 - val_accuracy: 0.9500 Epoch 64/200 298/298 [==============================] - 0s 51us/step - loss: 0.0635 - accuracy: 0.9933 - val_loss: 0.2007 - val_accuracy: 0.9400 Epoch 65/200 298/298 [==============================] - 0s 53us/step - loss: 0.0633 - accuracy: 0.9933 - val_loss: 0.2047 - val_accuracy: 0.9500 Epoch 66/200 298/298 [==============================] - 0s 50us/step - loss: 0.0649 - accuracy: 0.9933 - val_loss: 0.1903 - val_accuracy: 0.9500 Epoch 67/200 298/298 [==============================] - 0s 46us/step - loss: 0.0626 - accuracy: 0.9933 - val_loss: 0.1873 - val_accuracy: 0.9400 Epoch 68/200 298/298 [==============================] - 0s 48us/step - loss: 0.0629 - accuracy: 0.9933 - val_loss: 0.1735 - val_accuracy: 0.9600 Epoch 69/200 298/298 [==============================] - 0s 50us/step - loss: 0.0626 - accuracy: 0.9933 - val_loss: 0.1878 - val_accuracy: 0.9500 Epoch 70/200 298/298 [==============================] - 0s 45us/step - loss: 0.0618 - accuracy: 0.9933 - val_loss: 0.1866 - val_accuracy: 0.9500 Epoch 71/200 298/298 [==============================] - 0s 48us/step - loss: 0.0611 - accuracy: 0.9966 - val_loss: 0.1940 - val_accuracy: 0.9500 Epoch 72/200 298/298 [==============================] - 0s 45us/step - loss: 0.0618 - accuracy: 0.9933 - val_loss: 0.1879 - val_accuracy: 0.9500 Epoch 73/200 298/298 [==============================] - 0s 51us/step - loss: 0.0584 - accuracy: 0.9966 - val_loss: 0.2022 - val_accuracy: 0.9400 Epoch 74/200 298/298 [==============================] - 0s 45us/step - loss: 0.0621 - accuracy: 0.9933 - val_loss: 0.1932 - val_accuracy: 0.9400 Epoch 75/200 298/298 [==============================] - 0s 52us/step - loss: 0.0573 - accuracy: 0.9933 - val_loss: 0.1909 - val_accuracy: 0.9400 Epoch 76/200 298/298 [==============================] - 0s 43us/step - loss: 0.0577 - accuracy: 0.9966 - val_loss: 0.1954 - val_accuracy: 0.9400 Epoch 77/200 298/298 [==============================] - 0s 48us/step - loss: 0.0576 - accuracy: 0.9933 - val_loss: 0.2033 - val_accuracy: 0.9400 Epoch 78/200 298/298 [==============================] - 0s 49us/step - loss: 0.0587 - accuracy: 0.9933 - val_loss: 0.2082 - val_accuracy: 0.9400 Epoch 79/200 298/298 [==============================] - 0s 48us/step - loss: 0.0568 - accuracy: 0.9933 - val_loss: 0.2094 - val_accuracy: 0.9500 Epoch 80/200 298/298 [==============================] - 0s 46us/step - loss: 0.0571 - accuracy: 0.9933 - val_loss: 0.1925 - val_accuracy: 0.9500 Epoch 81/200 298/298 [==============================] - 0s 47us/step - loss: 0.0574 - accuracy: 0.9933 - val_loss: 0.1961 - val_accuracy: 0.9400 Epoch 82/200 298/298 [==============================] - 0s 52us/step - loss: 0.0564 - accuracy: 0.9933 - val_loss: 0.1989 - val_accuracy: 0.9400 Epoch 83/200 298/298 [==============================] - 0s 48us/step - loss: 0.0562 - accuracy: 0.9933 - val_loss: 0.1859 - val_accuracy: 0.9500 Epoch 84/200 298/298 [==============================] - 0s 48us/step - loss: 0.0563 - accuracy: 0.9966 - val_loss: 0.1970 - val_accuracy: 0.9300 Epoch 85/200 298/298 [==============================] - 0s 45us/step - loss: 0.0557 - accuracy: 0.9966 - val_loss: 0.1977 - val_accuracy: 0.9400 Epoch 86/200 298/298 [==============================] - 0s 48us/step - loss: 0.0544 - accuracy: 0.9933 - val_loss: 0.2010 - val_accuracy: 0.9500 Epoch 87/200 298/298 [==============================] - 0s 43us/step - loss: 0.0561 - accuracy: 0.9933 - val_loss: 0.1936 - val_accuracy: 0.9500 Epoch 88/200 298/298 [==============================] - 0s 49us/step - loss: 0.0538 - accuracy: 0.9899 - val_loss: 0.1815 - val_accuracy: 0.9500 Epoch 89/200 298/298 [==============================] - 0s 50us/step - loss: 0.0531 - accuracy: 0.9933 - val_loss: 0.1701 - val_accuracy: 0.9600 Epoch 90/200 298/298 [==============================] - 0s 52us/step - loss: 0.0553 - accuracy: 0.9933 - val_loss: 0.1816 - val_accuracy: 0.9500 Epoch 91/200 298/298 [==============================] - 0s 47us/step - loss: 0.0542 - accuracy: 0.9933 - val_loss: 0.1921 - val_accuracy: 0.9500 Epoch 92/200 298/298 [==============================] - 0s 68us/step - loss: 0.0528 - accuracy: 0.9933 - val_loss: 0.1977 - val_accuracy: 0.9500 Epoch 93/200 298/298 [==============================] - 0s 45us/step - loss: 0.0520 - accuracy: 0.9966 - val_loss: 0.1980 - val_accuracy: 0.9500 Epoch 94/200 298/298 [==============================] - 0s 50us/step - loss: 0.0513 - accuracy: 0.9966 - val_loss: 0.2066 - val_accuracy: 0.9400 Epoch 95/200 298/298 [==============================] - 0s 43us/step - loss: 0.0529 - accuracy: 0.9933 - val_loss: 0.1988 - val_accuracy: 0.9500 Epoch 96/200 298/298 [==============================] - 0s 52us/step - loss: 0.0537 - accuracy: 0.9933 - val_loss: 0.2042 - val_accuracy: 0.9400 Epoch 97/200 298/298 [==============================] - 0s 44us/step - loss: 0.0503 - accuracy: 1.0000 - val_loss: 0.2189 - val_accuracy: 0.9500 Epoch 98/200 298/298 [==============================] - 0s 51us/step - loss: 0.0518 - accuracy: 0.9933 - val_loss: 0.1988 - val_accuracy: 0.9500 Epoch 99/200 298/298 [==============================] - 0s 46us/step - loss: 0.0514 - accuracy: 0.9933 - val_loss: 0.2105 - val_accuracy: 0.9400 Epoch 100/200 298/298 [==============================] - 0s 52us/step - loss: 0.0510 - accuracy: 0.9966 - val_loss: 0.2152 - val_accuracy: 0.9400 Epoch 101/200 298/298 [==============================] - 0s 56us/step - loss: 0.0487 - accuracy: 0.9966 - val_loss: 0.2078 - val_accuracy: 0.9500 Epoch 102/200 298/298 [==============================] - 0s 61us/step - loss: 0.0551 - accuracy: 0.9933 - val_loss: 0.2029 - val_accuracy: 0.9300 Epoch 103/200 298/298 [==============================] - 0s 48us/step - loss: 0.0486 - accuracy: 0.9966 - val_loss: 0.2069 - val_accuracy: 0.9400 Epoch 104/200 298/298 [==============================] - 0s 57us/step - loss: 0.0507 - accuracy: 0.9933 - val_loss: 0.2115 - val_accuracy: 0.9400 Epoch 105/200 298/298 [==============================] - 0s 46us/step - loss: 0.0506 - accuracy: 0.9933 - val_loss: 0.2008 - val_accuracy: 0.9500 Epoch 106/200 298/298 [==============================] - 0s 58us/step - loss: 0.0497 - accuracy: 0.9966 - val_loss: 0.2006 - val_accuracy: 0.9400 Epoch 107/200 298/298 [==============================] - 0s 46us/step - loss: 0.0486 - accuracy: 0.9966 - val_loss: 0.2079 - val_accuracy: 0.9400 Epoch 108/200 298/298 [==============================] - 0s 52us/step - loss: 0.0511 - accuracy: 0.9899 - val_loss: 0.1901 - val_accuracy: 0.9500 Epoch 109/200 298/298 [==============================] - 0s 44us/step - loss: 0.0489 - accuracy: 0.9966 - val_loss: 0.2248 - val_accuracy: 0.9300 Epoch 110/200 298/298 [==============================] - 0s 48us/step - loss: 0.0489 - accuracy: 0.9933 - val_loss: 0.2072 - val_accuracy: 0.9400 Epoch 111/200 298/298 [==============================] - 0s 54us/step - loss: 0.0489 - accuracy: 0.9933 - val_loss: 0.1738 - val_accuracy: 0.9600 Epoch 112/200 298/298 [==============================] - 0s 50us/step - loss: 0.0487 - accuracy: 0.9966 - val_loss: 0.1920 - val_accuracy: 0.9500 Epoch 113/200 298/298 [==============================] - 0s 47us/step - loss: 0.0475 - accuracy: 0.9933 - val_loss: 0.1993 - val_accuracy: 0.9400 Epoch 114/200 298/298 [==============================] - 0s 53us/step - loss: 0.0474 - accuracy: 0.9933 - val_loss: 0.2043 - val_accuracy: 0.9400 Epoch 115/200 298/298 [==============================] - 0s 52us/step - loss: 0.0469 - accuracy: 0.9933 - val_loss: 0.1788 - val_accuracy: 0.9500 Epoch 116/200 298/298 [==============================] - 0s 51us/step - loss: 0.0512 - accuracy: 0.9899 - val_loss: 0.1906 - val_accuracy: 0.9500 Epoch 117/200 298/298 [==============================] - 0s 46us/step - loss: 0.0466 - accuracy: 0.9933 - val_loss: 0.2025 - val_accuracy: 0.9500 Epoch 118/200 298/298 [==============================] - 0s 49us/step - loss: 0.0468 - accuracy: 0.9966 - val_loss: 0.2089 - val_accuracy: 0.9400 Epoch 119/200 298/298 [==============================] - 0s 51us/step - loss: 0.0465 - accuracy: 0.9933 - val_loss: 0.1988 - val_accuracy: 0.9500 Epoch 120/200 298/298 [==============================] - 0s 48us/step - loss: 0.0463 - accuracy: 0.9966 - val_loss: 0.1955 - val_accuracy: 0.9500 Epoch 121/200 298/298 [==============================] - 0s 51us/step - loss: 0.0470 - accuracy: 0.9966 - val_loss: 0.2120 - val_accuracy: 0.9400 Epoch 122/200 298/298 [==============================] - 0s 47us/step - loss: 0.0468 - accuracy: 0.9966 - val_loss: 0.2085 - val_accuracy: 0.9500 Epoch 123/200 298/298 [==============================] - 0s 51us/step - loss: 0.0473 - accuracy: 0.9933 - val_loss: 0.2061 - val_accuracy: 0.9400 Epoch 124/200 298/298 [==============================] - 0s 46us/step - loss: 0.0456 - accuracy: 0.9966 - val_loss: 0.2057 - val_accuracy: 0.9400 Epoch 125/200 298/298 [==============================] - 0s 48us/step - loss: 0.0470 - accuracy: 0.9933 - val_loss: 0.2215 - val_accuracy: 0.9500 Epoch 126/200 298/298 [==============================] - 0s 44us/step - loss: 0.0483 - accuracy: 0.9966 - val_loss: 0.1948 - val_accuracy: 0.9400 Epoch 127/200 298/298 [==============================] - 0s 50us/step - loss: 0.0450 - accuracy: 0.9966 - val_loss: 0.1987 - val_accuracy: 0.9500 Epoch 128/200 298/298 [==============================] - 0s 45us/step - loss: 0.0463 - accuracy: 0.9933 - val_loss: 0.2063 - val_accuracy: 0.9400 Epoch 129/200 298/298 [==============================] - 0s 51us/step - loss: 0.0457 - accuracy: 0.9966 - val_loss: 0.2167 - val_accuracy: 0.9400 Epoch 130/200 298/298 [==============================] - 0s 45us/step - loss: 0.0459 - accuracy: 0.9933 - val_loss: 0.2093 - val_accuracy: 0.9400 Epoch 131/200 298/298 [==============================] - 0s 49us/step - loss: 0.0442 - accuracy: 0.9933 - val_loss: 0.1750 - val_accuracy: 0.9500 Epoch 132/200 298/298 [==============================] - 0s 46us/step - loss: 0.0434 - accuracy: 0.9966 - val_loss: 0.2061 - val_accuracy: 0.9400 Epoch 133/200 298/298 [==============================] - 0s 52us/step - loss: 0.0481 - accuracy: 0.9933 - val_loss: 0.1951 - val_accuracy: 0.9500 Epoch 134/200 298/298 [==============================] - 0s 46us/step - loss: 0.0443 - accuracy: 0.9933 - val_loss: 0.1938 - val_accuracy: 0.9400 Epoch 135/200 298/298 [==============================] - 0s 50us/step - loss: 0.0443 - accuracy: 1.0000 - val_loss: 0.2145 - val_accuracy: 0.9400 Epoch 136/200 298/298 [==============================] - 0s 52us/step - loss: 0.0449 - accuracy: 0.9933 - val_loss: 0.2139 - val_accuracy: 0.9400 Epoch 137/200 298/298 [==============================] - 0s 54us/step - loss: 0.0436 - accuracy: 0.9933 - val_loss: 0.2010 - val_accuracy: 0.9400 Epoch 138/200 298/298 [==============================] - 0s 46us/step - loss: 0.0454 - accuracy: 0.9933 - val_loss: 0.1975 - val_accuracy: 0.9500 Epoch 139/200 298/298 [==============================] - 0s 50us/step - loss: 0.0446 - accuracy: 0.9966 - val_loss: 0.2156 - val_accuracy: 0.9400 Epoch 140/200 298/298 [==============================] - 0s 51us/step - loss: 0.0430 - accuracy: 0.9966 - val_loss: 0.2028 - val_accuracy: 0.9500 Epoch 141/200 298/298 [==============================] - 0s 48us/step - loss: 0.0460 - accuracy: 0.9933 - val_loss: 0.1964 - val_accuracy: 0.9400 Epoch 142/200 298/298 [==============================] - 0s 49us/step - loss: 0.0434 - accuracy: 1.0000 - val_loss: 0.2063 - val_accuracy: 0.9300 Epoch 143/200 298/298 [==============================] - 0s 47us/step - loss: 0.0429 - accuracy: 0.9966 - val_loss: 0.2066 - val_accuracy: 0.9500 Epoch 144/200 298/298 [==============================] - 0s 49us/step - loss: 0.0441 - accuracy: 0.9966 - val_loss: 0.1881 - val_accuracy: 0.9500 Epoch 145/200 298/298 [==============================] - 0s 44us/step - loss: 0.0441 - accuracy: 0.9966 - val_loss: 0.1922 - val_accuracy: 0.9400 Epoch 146/200 298/298 [==============================] - 0s 47us/step - loss: 0.0447 - accuracy: 0.9933 - val_loss: 0.2063 - val_accuracy: 0.9400 Epoch 147/200 298/298 [==============================] - 0s 44us/step - loss: 0.0428 - accuracy: 1.0000 - val_loss: 0.2002 - val_accuracy: 0.9500 Epoch 148/200 298/298 [==============================] - 0s 53us/step - loss: 0.0429 - accuracy: 0.9966 - val_loss: 0.1907 - val_accuracy: 0.9500 Epoch 149/200 298/298 [==============================] - 0s 43us/step - loss: 0.0424 - accuracy: 1.0000 - val_loss: 0.1969 - val_accuracy: 0.9500 Epoch 150/200 298/298 [==============================] - 0s 51us/step - loss: 0.0436 - accuracy: 0.9899 - val_loss: 0.1879 - val_accuracy: 0.9500 Epoch 151/200 298/298 [==============================] - 0s 43us/step - loss: 0.0410 - accuracy: 1.0000 - val_loss: 0.1863 - val_accuracy: 0.9500 Epoch 152/200 298/298 [==============================] - 0s 48us/step - loss: 0.0424 - accuracy: 1.0000 - val_loss: 0.2148 - val_accuracy: 0.9400 Epoch 153/200 298/298 [==============================] - 0s 51us/step - loss: 0.0430 - accuracy: 0.9966 - val_loss: 0.1882 - val_accuracy: 0.9500 Epoch 154/200 298/298 [==============================] - 0s 61us/step - loss: 0.0425 - accuracy: 0.9966 - val_loss: 0.2064 - val_accuracy: 0.9500 Epoch 155/200 298/298 [==============================] - 0s 44us/step - loss: 0.0422 - accuracy: 0.9966 - val_loss: 0.1898 - val_accuracy: 0.9500 Epoch 156/200 298/298 [==============================] - 0s 50us/step - loss: 0.0410 - accuracy: 0.9966 - val_loss: 0.1800 - val_accuracy: 0.9500 Epoch 157/200 298/298 [==============================] - 0s 47us/step - loss: 0.0431 - accuracy: 1.0000 - val_loss: 0.2064 - val_accuracy: 0.9500 Epoch 158/200 298/298 [==============================] - 0s 52us/step - loss: 0.0424 - accuracy: 0.9966 - val_loss: 0.2092 - val_accuracy: 0.9400 Epoch 159/200 298/298 [==============================] - 0s 49us/step - loss: 0.0401 - accuracy: 1.0000 - val_loss: 0.2030 - val_accuracy: 0.9500 Epoch 160/200 298/298 [==============================] - 0s 50us/step - loss: 0.0400 - accuracy: 1.0000 - val_loss: 0.2323 - val_accuracy: 0.9400 Epoch 161/200 298/298 [==============================] - 0s 55us/step - loss: 0.0448 - accuracy: 0.9966 - val_loss: 0.2172 - val_accuracy: 0.9300 Epoch 162/200 298/298 [==============================] - 0s 53us/step - loss: 0.0403 - accuracy: 1.0000 - val_loss: 0.2205 - val_accuracy: 0.9400 Epoch 163/200 298/298 [==============================] - 0s 54us/step - loss: 0.0437 - accuracy: 0.9966 - val_loss: 0.2112 - val_accuracy: 0.9400 Epoch 164/200 298/298 [==============================] - 0s 56us/step - loss: 0.0413 - accuracy: 0.9966 - val_loss: 0.1956 - val_accuracy: 0.9500 Epoch 165/200 298/298 [==============================] - 0s 52us/step - loss: 0.0410 - accuracy: 0.9966 - val_loss: 0.2055 - val_accuracy: 0.9400 Epoch 166/200 298/298 [==============================] - 0s 66us/step - loss: 0.0392 - accuracy: 1.0000 - val_loss: 0.1925 - val_accuracy: 0.9500 Epoch 167/200 298/298 [==============================] - 0s 45us/step - loss: 0.0433 - accuracy: 0.9966 - val_loss: 0.2035 - val_accuracy: 0.9400 Epoch 168/200 298/298 [==============================] - 0s 48us/step - loss: 0.0423 - accuracy: 0.9966 - val_loss: 0.1898 - val_accuracy: 0.9500 Epoch 169/200 298/298 [==============================] - 0s 48us/step - loss: 0.0390 - accuracy: 1.0000 - val_loss: 0.2158 - val_accuracy: 0.9500 Epoch 170/200 298/298 [==============================] - 0s 58us/step - loss: 0.0431 - accuracy: 0.9966 - val_loss: 0.2145 - val_accuracy: 0.9400 Epoch 171/200 298/298 [==============================] - 0s 43us/step - loss: 0.0413 - accuracy: 0.9966 - val_loss: 0.1982 - val_accuracy: 0.9400 Epoch 172/200 298/298 [==============================] - 0s 49us/step - loss: 0.0408 - accuracy: 0.9966 - val_loss: 0.2186 - val_accuracy: 0.9300 Epoch 173/200 298/298 [==============================] - 0s 51us/step - loss: 0.0415 - accuracy: 0.9966 - val_loss: 0.2166 - val_accuracy: 0.9400 Epoch 174/200 298/298 [==============================] - 0s 55us/step - loss: 0.0410 - accuracy: 0.9966 - val_loss: 0.1924 - val_accuracy: 0.9500 Epoch 175/200 298/298 [==============================] - 0s 47us/step - loss: 0.0396 - accuracy: 1.0000 - val_loss: 0.2182 - val_accuracy: 0.9500 Epoch 176/200 298/298 [==============================] - 0s 51us/step - loss: 0.0406 - accuracy: 0.9966 - val_loss: 0.1998 - val_accuracy: 0.9500 Epoch 177/200 298/298 [==============================] - 0s 54us/step - loss: 0.0407 - accuracy: 0.9933 - val_loss: 0.2054 - val_accuracy: 0.9500 Epoch 178/200 298/298 [==============================] - 0s 56us/step - loss: 0.0406 - accuracy: 1.0000 - val_loss: 0.2199 - val_accuracy: 0.9400 Epoch 179/200 298/298 [==============================] - 0s 45us/step - loss: 0.0391 - accuracy: 1.0000 - val_loss: 0.2074 - val_accuracy: 0.9400 Epoch 180/200 298/298 [==============================] - 0s 85us/step - loss: 0.0411 - accuracy: 0.9966 - val_loss: 0.2027 - val_accuracy: 0.9500 Epoch 181/200 298/298 [==============================] - 0s 46us/step - loss: 0.0399 - accuracy: 1.0000 - val_loss: 0.1995 - val_accuracy: 0.9500 Epoch 182/200 298/298 [==============================] - 0s 50us/step - loss: 0.0394 - accuracy: 0.9966 - val_loss: 0.1985 - val_accuracy: 0.9400 Epoch 183/200 298/298 [==============================] - 0s 44us/step - loss: 0.0393 - accuracy: 1.0000 - val_loss: 0.1985 - val_accuracy: 0.9500 Epoch 184/200 298/298 [==============================] - 0s 55us/step - loss: 0.0389 - accuracy: 1.0000 - val_loss: 0.2253 - val_accuracy: 0.9500 Epoch 185/200 298/298 [==============================] - 0s 47us/step - loss: 0.0403 - accuracy: 1.0000 - val_loss: 0.1766 - val_accuracy: 0.9600 Epoch 186/200 298/298 [==============================] - 0s 55us/step - loss: 0.0390 - accuracy: 0.9966 - val_loss: 0.1969 - val_accuracy: 0.9400 Epoch 187/200 298/298 [==============================] - 0s 46us/step - loss: 0.0401 - accuracy: 1.0000 - val_loss: 0.2019 - val_accuracy: 0.9500 Epoch 188/200 298/298 [==============================] - 0s 52us/step - loss: 0.0381 - accuracy: 0.9966 - val_loss: 0.2089 - val_accuracy: 0.9400 Epoch 189/200 298/298 [==============================] - 0s 53us/step - loss: 0.0406 - accuracy: 0.9966 - val_loss: 0.1984 - val_accuracy: 0.9500 Epoch 190/200 298/298 [==============================] - 0s 55us/step - loss: 0.0389 - accuracy: 1.0000 - val_loss: 0.2028 - val_accuracy: 0.9500 Epoch 191/200 298/298 [==============================] - 0s 51us/step - loss: 0.0386 - accuracy: 0.9966 - val_loss: 0.2177 - val_accuracy: 0.9400 Epoch 192/200 298/298 [==============================] - 0s 51us/step - loss: 0.0401 - accuracy: 0.9966 - val_loss: 0.2046 - val_accuracy: 0.9400 Epoch 193/200 298/298 [==============================] - 0s 61us/step - loss: 0.0384 - accuracy: 1.0000 - val_loss: 0.2145 - val_accuracy: 0.9500 Epoch 194/200 298/298 [==============================] - 0s 49us/step - loss: 0.0383 - accuracy: 1.0000 - val_loss: 0.2054 - val_accuracy: 0.9500 Epoch 195/200 298/298 [==============================] - 0s 50us/step - loss: 0.0394 - accuracy: 0.9966 - val_loss: 0.1943 - val_accuracy: 0.9500 Epoch 196/200 298/298 [==============================] - 0s 57us/step - loss: 0.0379 - accuracy: 1.0000 - val_loss: 0.1902 - val_accuracy: 0.9500 Epoch 197/200 298/298 [==============================] - 0s 58us/step - loss: 0.0391 - accuracy: 1.0000 - val_loss: 0.2065 - val_accuracy: 0.9400 Epoch 198/200 298/298 [==============================] - 0s 44us/step - loss: 0.0373 - accuracy: 1.0000 - val_loss: 0.2149 - val_accuracy: 0.9400 Epoch 199/200 298/298 [==============================] - 0s 50us/step - loss: 0.0401 - accuracy: 0.9966 - val_loss: 0.2012 - val_accuracy: 0.9500 Epoch 200/200 298/298 [==============================] - 0s 47us/step - loss: 0.0388 - accuracy: 0.9966 - val_loss: 0.2096 - val_accuracy: 0.9400 171/171 [==============================] - 0s 30us/step Optimizers: <keras.optimizers.RMSprop object at 0x10a1d9a50> Epoch Sizes: 200 Neurons or Units: 64 ['loss', 'accuracy'] [0.08095977228810215, 0.988304078578949] Test score: 0.08095977228810215 Test accuracy: 0.988304078578949 Model: "sequential_44" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_130 (Dense) (None, 128) 3968 _________________________________________________________________ activation_130 (Activation) (None, 128) 0 _________________________________________________________________ dense_131 (Dense) (None, 128) 16512 _________________________________________________________________ activation_131 (Activation) (None, 128) 0 _________________________________________________________________ dense_132 (Dense) (None, 1) 129 _________________________________________________________________ activation_132 (Activation) (None, 1) 0 ================================================================= Total params: 20,609 Trainable params: 20,609 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/200 298/298 [==============================] - 0s 564us/step - loss: 2.0068 - accuracy: 0.8624 - val_loss: 1.7408 - val_accuracy: 0.9000 Epoch 2/200 298/298 [==============================] - 0s 46us/step - loss: 1.5810 - accuracy: 0.9631 - val_loss: 1.4980 - val_accuracy: 0.9100 Epoch 3/200 298/298 [==============================] - 0s 50us/step - loss: 1.3489 - accuracy: 0.9732 - val_loss: 1.3102 - val_accuracy: 0.9100 Epoch 4/200 298/298 [==============================] - 0s 49us/step - loss: 1.1558 - accuracy: 0.9765 - val_loss: 1.1254 - val_accuracy: 0.9300 Epoch 5/200 298/298 [==============================] - 0s 51us/step - loss: 0.9880 - accuracy: 0.9765 - val_loss: 0.9772 - val_accuracy: 0.9300 Epoch 6/200 298/298 [==============================] - 0s 51us/step - loss: 0.8391 - accuracy: 0.9832 - val_loss: 0.8478 - val_accuracy: 0.9200 Epoch 7/200 298/298 [==============================] - 0s 50us/step - loss: 0.7083 - accuracy: 0.9832 - val_loss: 0.7169 - val_accuracy: 0.9500 Epoch 8/200 298/298 [==============================] - 0s 46us/step - loss: 0.5946 - accuracy: 0.9899 - val_loss: 0.6219 - val_accuracy: 0.9500 Epoch 9/200 298/298 [==============================] - 0s 50us/step - loss: 0.4974 - accuracy: 0.9899 - val_loss: 0.5387 - val_accuracy: 0.9500 Epoch 10/200 298/298 [==============================] - 0s 48us/step - loss: 0.4141 - accuracy: 0.9933 - val_loss: 0.4698 - val_accuracy: 0.9500 Epoch 11/200 298/298 [==============================] - 0s 52us/step - loss: 0.3465 - accuracy: 0.9933 - val_loss: 0.4185 - val_accuracy: 0.9500 Epoch 12/200 298/298 [==============================] - 0s 47us/step - loss: 0.2899 - accuracy: 0.9933 - val_loss: 0.3746 - val_accuracy: 0.9500 Epoch 13/200 298/298 [==============================] - 0s 50us/step - loss: 0.2469 - accuracy: 0.9899 - val_loss: 0.3201 - val_accuracy: 0.9400 Epoch 14/200 298/298 [==============================] - 0s 49us/step - loss: 0.2133 - accuracy: 0.9899 - val_loss: 0.3007 - val_accuracy: 0.9500 Epoch 15/200 298/298 [==============================] - 0s 47us/step - loss: 0.1848 - accuracy: 0.9933 - val_loss: 0.2916 - val_accuracy: 0.9500 Epoch 16/200 298/298 [==============================] - 0s 55us/step - loss: 0.1668 - accuracy: 0.9899 - val_loss: 0.2776 - val_accuracy: 0.9500 Epoch 17/200 298/298 [==============================] - 0s 50us/step - loss: 0.1506 - accuracy: 0.9933 - val_loss: 0.2504 - val_accuracy: 0.9400 Epoch 18/200 298/298 [==============================] - 0s 50us/step - loss: 0.1390 - accuracy: 0.9899 - val_loss: 0.2402 - val_accuracy: 0.9400 Epoch 19/200 298/298 [==============================] - 0s 61us/step - loss: 0.1287 - accuracy: 0.9966 - val_loss: 0.2353 - val_accuracy: 0.9400 Epoch 20/200 298/298 [==============================] - 0s 61us/step - loss: 0.1233 - accuracy: 0.9899 - val_loss: 0.2244 - val_accuracy: 0.9400 Epoch 21/200 298/298 [==============================] - 0s 57us/step - loss: 0.1168 - accuracy: 0.9966 - val_loss: 0.2250 - val_accuracy: 0.9400 Epoch 22/200 298/298 [==============================] - 0s 44us/step - loss: 0.1107 - accuracy: 0.9966 - val_loss: 0.2254 - val_accuracy: 0.9400 Epoch 23/200 298/298 [==============================] - 0s 55us/step - loss: 0.1063 - accuracy: 0.9933 - val_loss: 0.2277 - val_accuracy: 0.9400 Epoch 24/200 298/298 [==============================] - 0s 46us/step - loss: 0.1020 - accuracy: 0.9966 - val_loss: 0.2153 - val_accuracy: 0.9400 Epoch 25/200 298/298 [==============================] - 0s 49us/step - loss: 0.0963 - accuracy: 0.9933 - val_loss: 0.2196 - val_accuracy: 0.9400 Epoch 26/200 298/298 [==============================] - 0s 47us/step - loss: 0.0952 - accuracy: 0.9966 - val_loss: 0.2210 - val_accuracy: 0.9400 Epoch 27/200 298/298 [==============================] - 0s 52us/step - loss: 0.0947 - accuracy: 0.9899 - val_loss: 0.2368 - val_accuracy: 0.9500 Epoch 28/200 298/298 [==============================] - 0s 47us/step - loss: 0.0889 - accuracy: 0.9933 - val_loss: 0.2224 - val_accuracy: 0.9400 Epoch 29/200 298/298 [==============================] - 0s 52us/step - loss: 0.0869 - accuracy: 0.9899 - val_loss: 0.2178 - val_accuracy: 0.9400 Epoch 30/200 298/298 [==============================] - 0s 52us/step - loss: 0.0850 - accuracy: 0.9866 - val_loss: 0.1967 - val_accuracy: 0.9400 Epoch 31/200 298/298 [==============================] - 0s 51us/step - loss: 0.0841 - accuracy: 0.9899 - val_loss: 0.2206 - val_accuracy: 0.9500 Epoch 32/200 298/298 [==============================] - 0s 57us/step - loss: 0.0823 - accuracy: 0.9933 - val_loss: 0.2339 - val_accuracy: 0.9500 Epoch 33/200 298/298 [==============================] - 0s 61us/step - loss: 0.0766 - accuracy: 0.9933 - val_loss: 0.1972 - val_accuracy: 0.9600 Epoch 34/200 298/298 [==============================] - 0s 46us/step - loss: 0.0783 - accuracy: 0.9933 - val_loss: 0.1751 - val_accuracy: 0.9600 Epoch 35/200 298/298 [==============================] - 0s 58us/step - loss: 0.0822 - accuracy: 0.9899 - val_loss: 0.2040 - val_accuracy: 0.9400 Epoch 36/200 298/298 [==============================] - 0s 48us/step - loss: 0.0736 - accuracy: 0.9933 - val_loss: 0.2098 - val_accuracy: 0.9500 Epoch 37/200 298/298 [==============================] - 0s 58us/step - loss: 0.0729 - accuracy: 0.9966 - val_loss: 0.1941 - val_accuracy: 0.9400 Epoch 38/200 298/298 [==============================] - 0s 44us/step - loss: 0.0723 - accuracy: 0.9933 - val_loss: 0.2129 - val_accuracy: 0.9500 Epoch 39/200 298/298 [==============================] - 0s 56us/step - loss: 0.0710 - accuracy: 0.9966 - val_loss: 0.1749 - val_accuracy: 0.9600 Epoch 40/200 298/298 [==============================] - 0s 48us/step - loss: 0.0694 - accuracy: 0.9966 - val_loss: 0.2073 - val_accuracy: 0.9400 Epoch 41/200 298/298 [==============================] - 0s 49us/step - loss: 0.0694 - accuracy: 0.9933 - val_loss: 0.2067 - val_accuracy: 0.9400 Epoch 42/200 298/298 [==============================] - 0s 50us/step - loss: 0.0666 - accuracy: 0.9933 - val_loss: 0.1985 - val_accuracy: 0.9400 Epoch 43/200 298/298 [==============================] - 0s 52us/step - loss: 0.0719 - accuracy: 0.9933 - val_loss: 0.2018 - val_accuracy: 0.9500 Epoch 44/200 298/298 [==============================] - 0s 48us/step - loss: 0.0681 - accuracy: 0.9933 - val_loss: 0.2011 - val_accuracy: 0.9500 Epoch 45/200 298/298 [==============================] - 0s 56us/step - loss: 0.0655 - accuracy: 0.9933 - val_loss: 0.1915 - val_accuracy: 0.9400 Epoch 46/200 298/298 [==============================] - 0s 49us/step - loss: 0.0663 - accuracy: 0.9933 - val_loss: 0.2017 - val_accuracy: 0.9400 Epoch 47/200 298/298 [==============================] - 0s 54us/step - loss: 0.0653 - accuracy: 0.9933 - val_loss: 0.1899 - val_accuracy: 0.9400 Epoch 48/200 298/298 [==============================] - 0s 47us/step - loss: 0.0643 - accuracy: 0.9933 - val_loss: 0.2019 - val_accuracy: 0.9500 Epoch 49/200 298/298 [==============================] - 0s 49us/step - loss: 0.0659 - accuracy: 0.9933 - val_loss: 0.2023 - val_accuracy: 0.9400 Epoch 50/200 298/298 [==============================] - 0s 50us/step - loss: 0.0614 - accuracy: 0.9966 - val_loss: 0.2014 - val_accuracy: 0.9400 Epoch 51/200 298/298 [==============================] - 0s 47us/step - loss: 0.0615 - accuracy: 0.9933 - val_loss: 0.2343 - val_accuracy: 0.9400 Epoch 52/200 298/298 [==============================] - 0s 59us/step - loss: 0.0646 - accuracy: 0.9933 - val_loss: 0.2058 - val_accuracy: 0.9400 Epoch 53/200 298/298 [==============================] - 0s 54us/step - loss: 0.0599 - accuracy: 0.9933 - val_loss: 0.2143 - val_accuracy: 0.9500 Epoch 54/200 298/298 [==============================] - 0s 51us/step - loss: 0.0625 - accuracy: 0.9933 - val_loss: 0.1880 - val_accuracy: 0.9500 Epoch 55/200 298/298 [==============================] - 0s 56us/step - loss: 0.0607 - accuracy: 0.9933 - val_loss: 0.1870 - val_accuracy: 0.9400 Epoch 56/200 298/298 [==============================] - 0s 50us/step - loss: 0.0589 - accuracy: 0.9966 - val_loss: 0.1984 - val_accuracy: 0.9400 Epoch 57/200 298/298 [==============================] - 0s 55us/step - loss: 0.0598 - accuracy: 0.9933 - val_loss: 0.1886 - val_accuracy: 0.9500 Epoch 58/200 298/298 [==============================] - 0s 53us/step - loss: 0.0571 - accuracy: 0.9933 - val_loss: 0.2083 - val_accuracy: 0.9400 Epoch 59/200 298/298 [==============================] - 0s 54us/step - loss: 0.0592 - accuracy: 0.9966 - val_loss: 0.2147 - val_accuracy: 0.9400 Epoch 60/200 298/298 [==============================] - 0s 47us/step - loss: 0.0572 - accuracy: 0.9933 - val_loss: 0.2077 - val_accuracy: 0.9400 Epoch 61/200 298/298 [==============================] - 0s 45us/step - loss: 0.0570 - accuracy: 0.9933 - val_loss: 0.1938 - val_accuracy: 0.9400 Epoch 62/200 298/298 [==============================] - 0s 57us/step - loss: 0.0571 - accuracy: 0.9933 - val_loss: 0.1856 - val_accuracy: 0.9500 Epoch 63/200 298/298 [==============================] - 0s 53us/step - loss: 0.0541 - accuracy: 0.9966 - val_loss: 0.1898 - val_accuracy: 0.9500 Epoch 64/200 298/298 [==============================] - 0s 47us/step - loss: 0.0570 - accuracy: 0.9933 - val_loss: 0.1739 - val_accuracy: 0.9600 Epoch 65/200 298/298 [==============================] - 0s 54us/step - loss: 0.0564 - accuracy: 0.9966 - val_loss: 0.1979 - val_accuracy: 0.9500 Epoch 66/200 298/298 [==============================] - 0s 50us/step - loss: 0.0551 - accuracy: 0.9933 - val_loss: 0.1967 - val_accuracy: 0.9500 Epoch 67/200 298/298 [==============================] - 0s 57us/step - loss: 0.0541 - accuracy: 0.9966 - val_loss: 0.1744 - val_accuracy: 0.9600 Epoch 68/200 298/298 [==============================] - 0s 47us/step - loss: 0.0524 - accuracy: 0.9966 - val_loss: 0.2148 - val_accuracy: 0.9400 Epoch 69/200 298/298 [==============================] - 0s 51us/step - loss: 0.0540 - accuracy: 0.9933 - val_loss: 0.1809 - val_accuracy: 0.9500 Epoch 70/200 298/298 [==============================] - 0s 51us/step - loss: 0.0517 - accuracy: 0.9966 - val_loss: 0.2028 - val_accuracy: 0.9500 Epoch 71/200 298/298 [==============================] - 0s 50us/step - loss: 0.0534 - accuracy: 0.9966 - val_loss: 0.1840 - val_accuracy: 0.9600 Epoch 72/200 298/298 [==============================] - 0s 55us/step - loss: 0.0522 - accuracy: 0.9933 - val_loss: 0.1818 - val_accuracy: 0.9500 Epoch 73/200 298/298 [==============================] - 0s 57us/step - loss: 0.0549 - accuracy: 0.9933 - val_loss: 0.2135 - val_accuracy: 0.9400 Epoch 74/200 298/298 [==============================] - 0s 45us/step - loss: 0.0514 - accuracy: 0.9966 - val_loss: 0.2041 - val_accuracy: 0.9400 Epoch 75/200 298/298 [==============================] - 0s 57us/step - loss: 0.0532 - accuracy: 0.9933 - val_loss: 0.1709 - val_accuracy: 0.9600 Epoch 76/200 298/298 [==============================] - 0s 48us/step - loss: 0.0520 - accuracy: 0.9933 - val_loss: 0.1878 - val_accuracy: 0.9500 Epoch 77/200 298/298 [==============================] - 0s 53us/step - loss: 0.0494 - accuracy: 0.9933 - val_loss: 0.1997 - val_accuracy: 0.9500 Epoch 78/200 298/298 [==============================] - 0s 49us/step - loss: 0.0500 - accuracy: 0.9966 - val_loss: 0.1788 - val_accuracy: 0.9600 Epoch 79/200 298/298 [==============================] - 0s 52us/step - loss: 0.0509 - accuracy: 0.9933 - val_loss: 0.2035 - val_accuracy: 0.9500 Epoch 80/200 298/298 [==============================] - 0s 50us/step - loss: 0.0494 - accuracy: 0.9933 - val_loss: 0.2289 - val_accuracy: 0.9500 Epoch 81/200 298/298 [==============================] - 0s 53us/step - loss: 0.0487 - accuracy: 0.9966 - val_loss: 0.1973 - val_accuracy: 0.9500 Epoch 82/200 298/298 [==============================] - 0s 58us/step - loss: 0.0475 - accuracy: 0.9966 - val_loss: 0.2092 - val_accuracy: 0.9400 Epoch 83/200 298/298 [==============================] - 0s 56us/step - loss: 0.0483 - accuracy: 0.9933 - val_loss: 0.2092 - val_accuracy: 0.9500 Epoch 84/200 298/298 [==============================] - 0s 44us/step - loss: 0.0482 - accuracy: 0.9933 - val_loss: 0.2108 - val_accuracy: 0.9300 Epoch 85/200 298/298 [==============================] - 0s 57us/step - loss: 0.0512 - accuracy: 0.9899 - val_loss: 0.2025 - val_accuracy: 0.9400 Epoch 86/200 298/298 [==============================] - 0s 47us/step - loss: 0.0470 - accuracy: 0.9966 - val_loss: 0.2051 - val_accuracy: 0.9400 Epoch 87/200 298/298 [==============================] - 0s 56us/step - loss: 0.0490 - accuracy: 0.9966 - val_loss: 0.1937 - val_accuracy: 0.9500 Epoch 88/200 298/298 [==============================] - 0s 49us/step - loss: 0.0468 - accuracy: 0.9966 - val_loss: 0.1962 - val_accuracy: 0.9500 Epoch 89/200 298/298 [==============================] - 0s 53us/step - loss: 0.0510 - accuracy: 0.9933 - val_loss: 0.2067 - val_accuracy: 0.9400 Epoch 90/200 298/298 [==============================] - 0s 49us/step - loss: 0.0462 - accuracy: 0.9966 - val_loss: 0.2054 - val_accuracy: 0.9400 Epoch 91/200 298/298 [==============================] - 0s 52us/step - loss: 0.0500 - accuracy: 0.9933 - val_loss: 0.2245 - val_accuracy: 0.9400 Epoch 92/200 298/298 [==============================] - 0s 52us/step - loss: 0.0471 - accuracy: 0.9933 - val_loss: 0.2041 - val_accuracy: 0.9400 Epoch 93/200 298/298 [==============================] - 0s 55us/step - loss: 0.0461 - accuracy: 0.9933 - val_loss: 0.2153 - val_accuracy: 0.9400 Epoch 94/200 298/298 [==============================] - 0s 48us/step - loss: 0.0468 - accuracy: 0.9966 - val_loss: 0.1937 - val_accuracy: 0.9500 Epoch 95/200 298/298 [==============================] - 0s 61us/step - loss: 0.0480 - accuracy: 0.9933 - val_loss: 0.2115 - val_accuracy: 0.9400 Epoch 96/200 298/298 [==============================] - 0s 48us/step - loss: 0.0450 - accuracy: 1.0000 - val_loss: 0.2094 - val_accuracy: 0.9400 Epoch 97/200 298/298 [==============================] - 0s 58us/step - loss: 0.0468 - accuracy: 0.9966 - val_loss: 0.1904 - val_accuracy: 0.9500 Epoch 98/200 298/298 [==============================] - 0s 46us/step - loss: 0.0445 - accuracy: 0.9966 - val_loss: 0.2139 - val_accuracy: 0.9400 Epoch 99/200 298/298 [==============================] - 0s 51us/step - loss: 0.0456 - accuracy: 0.9933 - val_loss: 0.1893 - val_accuracy: 0.9500 Epoch 100/200 298/298 [==============================] - 0s 47us/step - loss: 0.0461 - accuracy: 0.9966 - val_loss: 0.2104 - val_accuracy: 0.9400 Epoch 101/200 298/298 [==============================] - 0s 51us/step - loss: 0.0431 - accuracy: 0.9966 - val_loss: 0.1849 - val_accuracy: 0.9500 Epoch 102/200 298/298 [==============================] - 0s 50us/step - loss: 0.0435 - accuracy: 0.9966 - val_loss: 0.2441 - val_accuracy: 0.9500 Epoch 103/200 298/298 [==============================] - 0s 58us/step - loss: 0.0473 - accuracy: 0.9933 - val_loss: 0.1746 - val_accuracy: 0.9600 Epoch 104/200 298/298 [==============================] - 0s 47us/step - loss: 0.0441 - accuracy: 0.9933 - val_loss: 0.1930 - val_accuracy: 0.9500 Epoch 105/200 298/298 [==============================] - 0s 55us/step - loss: 0.0435 - accuracy: 0.9933 - val_loss: 0.1782 - val_accuracy: 0.9600 Epoch 106/200 298/298 [==============================] - 0s 48us/step - loss: 0.0426 - accuracy: 0.9966 - val_loss: 0.1803 - val_accuracy: 0.9500 Epoch 107/200 298/298 [==============================] - 0s 54us/step - loss: 0.0454 - accuracy: 0.9933 - val_loss: 0.1890 - val_accuracy: 0.9500 Epoch 108/200 298/298 [==============================] - 0s 46us/step - loss: 0.0451 - accuracy: 0.9933 - val_loss: 0.2210 - val_accuracy: 0.9400 Epoch 109/200 298/298 [==============================] - 0s 53us/step - loss: 0.0441 - accuracy: 0.9966 - val_loss: 0.2234 - val_accuracy: 0.9400 Epoch 110/200 298/298 [==============================] - 0s 46us/step - loss: 0.0430 - accuracy: 0.9933 - val_loss: 0.1828 - val_accuracy: 0.9400 Epoch 111/200 298/298 [==============================] - 0s 46us/step - loss: 0.0451 - accuracy: 0.9966 - val_loss: 0.2388 - val_accuracy: 0.9400 Epoch 112/200 298/298 [==============================] - 0s 57us/step - loss: 0.0459 - accuracy: 0.9966 - val_loss: 0.2005 - val_accuracy: 0.9500 Epoch 113/200 298/298 [==============================] - 0s 59us/step - loss: 0.0439 - accuracy: 0.9933 - val_loss: 0.1882 - val_accuracy: 0.9500 Epoch 114/200 298/298 [==============================] - 0s 46us/step - loss: 0.0414 - accuracy: 1.0000 - val_loss: 0.1941 - val_accuracy: 0.9500 Epoch 115/200 298/298 [==============================] - 0s 55us/step - loss: 0.0428 - accuracy: 0.9966 - val_loss: 0.1924 - val_accuracy: 0.9500 Epoch 116/200 298/298 [==============================] - 0s 47us/step - loss: 0.0419 - accuracy: 0.9966 - val_loss: 0.2206 - val_accuracy: 0.9500 Epoch 117/200 298/298 [==============================] - 0s 51us/step - loss: 0.0434 - accuracy: 0.9966 - val_loss: 0.2024 - val_accuracy: 0.9500 Epoch 118/200 298/298 [==============================] - 0s 46us/step - loss: 0.0420 - accuracy: 1.0000 - val_loss: 0.2201 - val_accuracy: 0.9400 Epoch 119/200 298/298 [==============================] - 0s 50us/step - loss: 0.0437 - accuracy: 0.9933 - val_loss: 0.2199 - val_accuracy: 0.9400 Epoch 120/200 298/298 [==============================] - 0s 50us/step - loss: 0.0452 - accuracy: 0.9966 - val_loss: 0.2160 - val_accuracy: 0.9400 Epoch 121/200 298/298 [==============================] - 0s 47us/step - loss: 0.0412 - accuracy: 0.9966 - val_loss: 0.2131 - val_accuracy: 0.9400 Epoch 122/200 298/298 [==============================] - 0s 55us/step - loss: 0.0432 - accuracy: 0.9966 - val_loss: 0.2029 - val_accuracy: 0.9400 Epoch 123/200 298/298 [==============================] - 0s 55us/step - loss: 0.0433 - accuracy: 0.9933 - val_loss: 0.2192 - val_accuracy: 0.9400 Epoch 124/200 298/298 [==============================] - 0s 48us/step - loss: 0.0434 - accuracy: 0.9966 - val_loss: 0.1959 - val_accuracy: 0.9500 Epoch 125/200 298/298 [==============================] - 0s 53us/step - loss: 0.0408 - accuracy: 0.9966 - val_loss: 0.1946 - val_accuracy: 0.9500 Epoch 126/200 298/298 [==============================] - 0s 46us/step - loss: 0.0397 - accuracy: 1.0000 - val_loss: 0.1798 - val_accuracy: 0.9600 Epoch 127/200 298/298 [==============================] - 0s 47us/step - loss: 0.0411 - accuracy: 0.9966 - val_loss: 0.1791 - val_accuracy: 0.9600 Epoch 128/200 298/298 [==============================] - 0s 48us/step - loss: 0.0409 - accuracy: 0.9966 - val_loss: 0.2270 - val_accuracy: 0.9400 Epoch 129/200 298/298 [==============================] - 0s 46us/step - loss: 0.0415 - accuracy: 0.9966 - val_loss: 0.2065 - val_accuracy: 0.9500 Epoch 130/200 298/298 [==============================] - 0s 55us/step - loss: 0.0417 - accuracy: 1.0000 - val_loss: 0.2246 - val_accuracy: 0.9500 Epoch 131/200 298/298 [==============================] - 0s 49us/step - loss: 0.0404 - accuracy: 0.9966 - val_loss: 0.1981 - val_accuracy: 0.9500 Epoch 132/200 298/298 [==============================] - 0s 54us/step - loss: 0.0389 - accuracy: 1.0000 - val_loss: 0.2070 - val_accuracy: 0.9400 Epoch 133/200 298/298 [==============================] - 0s 49us/step - loss: 0.0390 - accuracy: 1.0000 - val_loss: 0.1657 - val_accuracy: 0.9600 Epoch 134/200 298/298 [==============================] - 0s 50us/step - loss: 0.0434 - accuracy: 0.9933 - val_loss: 0.1911 - val_accuracy: 0.9500 Epoch 135/200 298/298 [==============================] - 0s 47us/step - loss: 0.0397 - accuracy: 0.9966 - val_loss: 0.2268 - val_accuracy: 0.9300 Epoch 136/200 298/298 [==============================] - 0s 54us/step - loss: 0.0403 - accuracy: 0.9933 - val_loss: 0.2120 - val_accuracy: 0.9400 Epoch 137/200 298/298 [==============================] - 0s 46us/step - loss: 0.0422 - accuracy: 0.9966 - val_loss: 0.2031 - val_accuracy: 0.9500 Epoch 138/200 298/298 [==============================] - 0s 54us/step - loss: 0.0398 - accuracy: 0.9933 - val_loss: 0.1991 - val_accuracy: 0.9500 Epoch 139/200 298/298 [==============================] - 0s 50us/step - loss: 0.0404 - accuracy: 0.9966 - val_loss: 0.2053 - val_accuracy: 0.9400 Epoch 140/200 298/298 [==============================] - 0s 50us/step - loss: 0.0404 - accuracy: 0.9933 - val_loss: 0.2162 - val_accuracy: 0.9400 Epoch 141/200 298/298 [==============================] - 0s 46us/step - loss: 0.0415 - accuracy: 0.9933 - val_loss: 0.1951 - val_accuracy: 0.9500 Epoch 142/200 298/298 [==============================] - 0s 53us/step - loss: 0.0372 - accuracy: 1.0000 - val_loss: 0.1879 - val_accuracy: 0.9500 Epoch 143/200 298/298 [==============================] - 0s 46us/step - loss: 0.0393 - accuracy: 0.9966 - val_loss: 0.2049 - val_accuracy: 0.9400 Epoch 144/200 298/298 [==============================] - 0s 57us/step - loss: 0.0385 - accuracy: 0.9966 - val_loss: 0.2307 - val_accuracy: 0.9400 Epoch 145/200 298/298 [==============================] - 0s 45us/step - loss: 0.0425 - accuracy: 0.9933 - val_loss: 0.2008 - val_accuracy: 0.9500 Epoch 146/200 298/298 [==============================] - 0s 54us/step - loss: 0.0386 - accuracy: 0.9966 - val_loss: 0.2224 - val_accuracy: 0.9400 Epoch 147/200 298/298 [==============================] - 0s 46us/step - loss: 0.0397 - accuracy: 0.9966 - val_loss: 0.1884 - val_accuracy: 0.9500 Epoch 148/200 298/298 [==============================] - 0s 50us/step - loss: 0.0407 - accuracy: 0.9966 - val_loss: 0.2101 - val_accuracy: 0.9400 Epoch 149/200 298/298 [==============================] - 0s 52us/step - loss: 0.0403 - accuracy: 0.9966 - val_loss: 0.2209 - val_accuracy: 0.9400 Epoch 150/200 298/298 [==============================] - 0s 54us/step - loss: 0.0387 - accuracy: 0.9966 - val_loss: 0.1999 - val_accuracy: 0.9500 Epoch 151/200 298/298 [==============================] - 0s 47us/step - loss: 0.0389 - accuracy: 0.9966 - val_loss: 0.2139 - val_accuracy: 0.9400 Epoch 152/200 298/298 [==============================] - 0s 55us/step - loss: 0.0381 - accuracy: 0.9966 - val_loss: 0.2416 - val_accuracy: 0.9400 Epoch 153/200 298/298 [==============================] - 0s 48us/step - loss: 0.0411 - accuracy: 0.9966 - val_loss: 0.1645 - val_accuracy: 0.9600 Epoch 154/200 298/298 [==============================] - 0s 55us/step - loss: 0.0381 - accuracy: 1.0000 - val_loss: 0.1903 - val_accuracy: 0.9500 Epoch 155/200 298/298 [==============================] - 0s 46us/step - loss: 0.0392 - accuracy: 0.9966 - val_loss: 0.1944 - val_accuracy: 0.9500 Epoch 156/200 298/298 [==============================] - 0s 50us/step - loss: 0.0404 - accuracy: 0.9966 - val_loss: 0.1887 - val_accuracy: 0.9500 Epoch 157/200 298/298 [==============================] - 0s 47us/step - loss: 0.0369 - accuracy: 1.0000 - val_loss: 0.2393 - val_accuracy: 0.9400 Epoch 158/200 298/298 [==============================] - 0s 45us/step - loss: 0.0393 - accuracy: 0.9933 - val_loss: 0.2388 - val_accuracy: 0.9400 Epoch 159/200 298/298 [==============================] - 0s 56us/step - loss: 0.0373 - accuracy: 1.0000 - val_loss: 0.2122 - val_accuracy: 0.9400 Epoch 160/200 298/298 [==============================] - 0s 52us/step - loss: 0.0396 - accuracy: 0.9933 - val_loss: 0.2145 - val_accuracy: 0.9400 Epoch 161/200 298/298 [==============================] - 0s 47us/step - loss: 0.0402 - accuracy: 0.9966 - val_loss: 0.2044 - val_accuracy: 0.9400 Epoch 162/200 298/298 [==============================] - 0s 49us/step - loss: 0.0366 - accuracy: 0.9966 - val_loss: 0.1982 - val_accuracy: 0.9500 Epoch 163/200 298/298 [==============================] - 0s 47us/step - loss: 0.0348 - accuracy: 1.0000 - val_loss: 0.2298 - val_accuracy: 0.9500 Epoch 164/200 298/298 [==============================] - 0s 46us/step - loss: 0.0385 - accuracy: 0.9966 - val_loss: 0.1794 - val_accuracy: 0.9500 Epoch 165/200 298/298 [==============================] - 0s 56us/step - loss: 0.0362 - accuracy: 1.0000 - val_loss: 0.1952 - val_accuracy: 0.9500 Epoch 166/200 298/298 [==============================] - 0s 47us/step - loss: 0.0376 - accuracy: 1.0000 - val_loss: 0.2157 - val_accuracy: 0.9500 Epoch 167/200 298/298 [==============================] - 0s 54us/step - loss: 0.0387 - accuracy: 1.0000 - val_loss: 0.2137 - val_accuracy: 0.9400 Epoch 168/200 298/298 [==============================] - 0s 47us/step - loss: 0.0362 - accuracy: 1.0000 - val_loss: 0.2079 - val_accuracy: 0.9500 Epoch 169/200 298/298 [==============================] - 0s 56us/step - loss: 0.0363 - accuracy: 1.0000 - val_loss: 0.2033 - val_accuracy: 0.9500 Epoch 170/200 298/298 [==============================] - 0s 48us/step - loss: 0.0391 - accuracy: 0.9933 - val_loss: 0.1904 - val_accuracy: 0.9500 Epoch 171/200 298/298 [==============================] - 0s 49us/step - loss: 0.0356 - accuracy: 1.0000 - val_loss: 0.2017 - val_accuracy: 0.9500 Epoch 172/200 298/298 [==============================] - 0s 46us/step - loss: 0.0381 - accuracy: 1.0000 - val_loss: 0.2540 - val_accuracy: 0.9400 Epoch 173/200 298/298 [==============================] - 0s 55us/step - loss: 0.0405 - accuracy: 0.9933 - val_loss: 0.1969 - val_accuracy: 0.9500 Epoch 174/200 298/298 [==============================] - 0s 46us/step - loss: 0.0351 - accuracy: 1.0000 - val_loss: 0.1814 - val_accuracy: 0.9500 Epoch 175/200 298/298 [==============================] - 0s 54us/step - loss: 0.0373 - accuracy: 0.9966 - val_loss: 0.2044 - val_accuracy: 0.9400 Epoch 176/200 298/298 [==============================] - 0s 46us/step - loss: 0.0386 - accuracy: 0.9966 - val_loss: 0.2149 - val_accuracy: 0.9400 Epoch 177/200 298/298 [==============================] - 0s 52us/step - loss: 0.0358 - accuracy: 1.0000 - val_loss: 0.2152 - val_accuracy: 0.9500 Epoch 178/200 298/298 [==============================] - 0s 46us/step - loss: 0.0382 - accuracy: 0.9933 - val_loss: 0.1941 - val_accuracy: 0.9500 Epoch 179/200 298/298 [==============================] - 0s 53us/step - loss: 0.0372 - accuracy: 0.9966 - val_loss: 0.2002 - val_accuracy: 0.9500 Epoch 180/200 298/298 [==============================] - 0s 46us/step - loss: 0.0353 - accuracy: 1.0000 - val_loss: 0.2036 - val_accuracy: 0.9500 Epoch 181/200 298/298 [==============================] - 0s 54us/step - loss: 0.0361 - accuracy: 0.9966 - val_loss: 0.2045 - val_accuracy: 0.9500 Epoch 182/200 298/298 [==============================] - 0s 47us/step - loss: 0.0346 - accuracy: 1.0000 - val_loss: 0.2181 - val_accuracy: 0.9500 Epoch 183/200 298/298 [==============================] - 0s 53us/step - loss: 0.0382 - accuracy: 0.9966 - val_loss: 0.2192 - val_accuracy: 0.9400 Epoch 184/200 298/298 [==============================] - 0s 47us/step - loss: 0.0374 - accuracy: 0.9966 - val_loss: 0.2177 - val_accuracy: 0.9400 Epoch 185/200 298/298 [==============================] - 0s 49us/step - loss: 0.0359 - accuracy: 1.0000 - val_loss: 0.2210 - val_accuracy: 0.9400 Epoch 186/200 298/298 [==============================] - 0s 49us/step - loss: 0.0389 - accuracy: 0.9966 - val_loss: 0.2292 - val_accuracy: 0.9300 Epoch 187/200 298/298 [==============================] - 0s 52us/step - loss: 0.0361 - accuracy: 0.9966 - val_loss: 0.1921 - val_accuracy: 0.9500 Epoch 188/200 298/298 [==============================] - 0s 47us/step - loss: 0.0369 - accuracy: 0.9966 - val_loss: 0.2497 - val_accuracy: 0.9500 Epoch 189/200 298/298 [==============================] - 0s 54us/step - loss: 0.0355 - accuracy: 1.0000 - val_loss: 0.2436 - val_accuracy: 0.9400 Epoch 190/200 298/298 [==============================] - 0s 47us/step - loss: 0.0347 - accuracy: 1.0000 - val_loss: 0.2287 - val_accuracy: 0.9400 Epoch 191/200 298/298 [==============================] - 0s 51us/step - loss: 0.0372 - accuracy: 1.0000 - val_loss: 0.2187 - val_accuracy: 0.9400 Epoch 192/200 298/298 [==============================] - 0s 49us/step - loss: 0.0362 - accuracy: 0.9966 - val_loss: 0.2060 - val_accuracy: 0.9500 Epoch 193/200 298/298 [==============================] - 0s 46us/step - loss: 0.0361 - accuracy: 0.9966 - val_loss: 0.1763 - val_accuracy: 0.9600 Epoch 194/200 298/298 [==============================] - 0s 51us/step - loss: 0.0350 - accuracy: 1.0000 - val_loss: 0.2101 - val_accuracy: 0.9500 Epoch 195/200 298/298 [==============================] - 0s 54us/step - loss: 0.0350 - accuracy: 1.0000 - val_loss: 0.2176 - val_accuracy: 0.9400 Epoch 196/200 298/298 [==============================] - 0s 46us/step - loss: 0.0349 - accuracy: 0.9966 - val_loss: 0.2152 - val_accuracy: 0.9300 Epoch 197/200 298/298 [==============================] - 0s 51us/step - loss: 0.0363 - accuracy: 1.0000 - val_loss: 0.1916 - val_accuracy: 0.9500 Epoch 198/200 298/298 [==============================] - 0s 47us/step - loss: 0.0338 - accuracy: 1.0000 - val_loss: 0.2126 - val_accuracy: 0.9400 Epoch 199/200 298/298 [==============================] - 0s 50us/step - loss: 0.0359 - accuracy: 0.9966 - val_loss: 0.2136 - val_accuracy: 0.9400 Epoch 200/200 298/298 [==============================] - 0s 50us/step - loss: 0.0345 - accuracy: 0.9966 - val_loss: 0.1938 - val_accuracy: 0.9500 171/171 [==============================] - 0s 28us/step Optimizers: <keras.optimizers.RMSprop object at 0x10a1d9a50> Epoch Sizes: 200 Neurons or Units: 128 ['loss', 'accuracy'] [0.08279129296366931, 0.9824561476707458] Test score: 0.08279129296366931 Test accuracy: 0.9824561476707458 Model: "sequential_45" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_133 (Dense) (None, 256) 7936 _________________________________________________________________ activation_133 (Activation) (None, 256) 0 _________________________________________________________________ dense_134 (Dense) (None, 256) 65792 _________________________________________________________________ activation_134 (Activation) (None, 256) 0 _________________________________________________________________ dense_135 (Dense) (None, 1) 257 _________________________________________________________________ activation_135 (Activation) (None, 1) 0 ================================================================= Total params: 73,985 Trainable params: 73,985 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/200 298/298 [==============================] - 0s 571us/step - loss: 3.0020 - accuracy: 0.8926 - val_loss: 2.4974 - val_accuracy: 0.9300 Epoch 2/200 298/298 [==============================] - 0s 65us/step - loss: 2.2239 - accuracy: 0.9732 - val_loss: 1.9949 - val_accuracy: 0.9200 Epoch 3/200 298/298 [==============================] - 0s 65us/step - loss: 1.7458 - accuracy: 0.9765 - val_loss: 1.5730 - val_accuracy: 0.9300 Epoch 4/200 298/298 [==============================] - 0s 74us/step - loss: 1.3566 - accuracy: 0.9832 - val_loss: 1.2235 - val_accuracy: 0.9400 Epoch 5/200 298/298 [==============================] - 0s 64us/step - loss: 1.0392 - accuracy: 0.9899 - val_loss: 0.9532 - val_accuracy: 0.9500 Epoch 6/200 298/298 [==============================] - 0s 73us/step - loss: 0.7847 - accuracy: 0.9866 - val_loss: 0.7630 - val_accuracy: 0.9300 Epoch 7/200 298/298 [==============================] - 0s 73us/step - loss: 0.5899 - accuracy: 0.9832 - val_loss: 0.5969 - val_accuracy: 0.9300 Epoch 8/200 298/298 [==============================] - 0s 66us/step - loss: 0.4413 - accuracy: 0.9899 - val_loss: 0.4579 - val_accuracy: 0.9400 Epoch 9/200 298/298 [==============================] - 0s 66us/step - loss: 0.3478 - accuracy: 0.9832 - val_loss: 0.4097 - val_accuracy: 0.9500 Epoch 10/200 298/298 [==============================] - 0s 72us/step - loss: 0.2735 - accuracy: 0.9899 - val_loss: 0.3491 - val_accuracy: 0.9500 Epoch 11/200 298/298 [==============================] - 0s 67us/step - loss: 0.2251 - accuracy: 0.9899 - val_loss: 0.3161 - val_accuracy: 0.9500 Epoch 12/200 298/298 [==============================] - 0s 79us/step - loss: 0.1878 - accuracy: 0.9933 - val_loss: 0.2825 - val_accuracy: 0.9400 Epoch 13/200 298/298 [==============================] - 0s 72us/step - loss: 0.1654 - accuracy: 0.9899 - val_loss: 0.2716 - val_accuracy: 0.9400 Epoch 14/200 298/298 [==============================] - 0s 73us/step - loss: 0.1451 - accuracy: 0.9899 - val_loss: 0.2427 - val_accuracy: 0.9500 Epoch 15/200 298/298 [==============================] - 0s 62us/step - loss: 0.1327 - accuracy: 0.9899 - val_loss: 0.2096 - val_accuracy: 0.9500 Epoch 16/200 298/298 [==============================] - 0s 76us/step - loss: 0.1256 - accuracy: 0.9933 - val_loss: 0.2268 - val_accuracy: 0.9400 Epoch 17/200 298/298 [==============================] - 0s 63us/step - loss: 0.1173 - accuracy: 0.9933 - val_loss: 0.2195 - val_accuracy: 0.9300 Epoch 18/200 298/298 [==============================] - 0s 74us/step - loss: 0.1107 - accuracy: 0.9933 - val_loss: 0.2175 - val_accuracy: 0.9400 Epoch 19/200 298/298 [==============================] - 0s 73us/step - loss: 0.1077 - accuracy: 0.9966 - val_loss: 0.2350 - val_accuracy: 0.9300 Epoch 20/200 298/298 [==============================] - 0s 73us/step - loss: 0.1041 - accuracy: 0.9899 - val_loss: 0.1856 - val_accuracy: 0.9600 Epoch 21/200 298/298 [==============================] - 0s 65us/step - loss: 0.0977 - accuracy: 0.9933 - val_loss: 0.2181 - val_accuracy: 0.9200 Epoch 22/200 298/298 [==============================] - 0s 78us/step - loss: 0.0957 - accuracy: 0.9899 - val_loss: 0.2042 - val_accuracy: 0.9400 Epoch 23/200 298/298 [==============================] - 0s 73us/step - loss: 0.0982 - accuracy: 0.9933 - val_loss: 0.2091 - val_accuracy: 0.9500 Epoch 24/200 298/298 [==============================] - 0s 68us/step - loss: 0.0885 - accuracy: 0.9933 - val_loss: 0.2351 - val_accuracy: 0.9500 Epoch 25/200 298/298 [==============================] - 0s 74us/step - loss: 0.0902 - accuracy: 0.9933 - val_loss: 0.2232 - val_accuracy: 0.9400 Epoch 26/200 298/298 [==============================] - 0s 70us/step - loss: 0.0863 - accuracy: 0.9933 - val_loss: 0.1973 - val_accuracy: 0.9500 Epoch 27/200 298/298 [==============================] - 0s 66us/step - loss: 0.0860 - accuracy: 0.9966 - val_loss: 0.1839 - val_accuracy: 0.9500 Epoch 28/200 298/298 [==============================] - 0s 73us/step - loss: 0.0850 - accuracy: 0.9866 - val_loss: 0.1795 - val_accuracy: 0.9500 Epoch 29/200 298/298 [==============================] - 0s 66us/step - loss: 0.0767 - accuracy: 0.9966 - val_loss: 0.2280 - val_accuracy: 0.9500 Epoch 30/200 298/298 [==============================] - 0s 66us/step - loss: 0.0803 - accuracy: 0.9933 - val_loss: 0.2197 - val_accuracy: 0.9400 Epoch 31/200 298/298 [==============================] - 0s 81us/step - loss: 0.0826 - accuracy: 0.9866 - val_loss: 0.1969 - val_accuracy: 0.9500 Epoch 32/200 298/298 [==============================] - 0s 67us/step - loss: 0.0767 - accuracy: 0.9866 - val_loss: 0.1938 - val_accuracy: 0.9500 Epoch 33/200 298/298 [==============================] - 0s 71us/step - loss: 0.0759 - accuracy: 0.9866 - val_loss: 0.1921 - val_accuracy: 0.9400 Epoch 34/200 298/298 [==============================] - 0s 70us/step - loss: 0.0750 - accuracy: 0.9933 - val_loss: 0.2005 - val_accuracy: 0.9500 Epoch 35/200 298/298 [==============================] - 0s 73us/step - loss: 0.0745 - accuracy: 0.9899 - val_loss: 0.1701 - val_accuracy: 0.9600 Epoch 36/200 298/298 [==============================] - 0s 64us/step - loss: 0.0722 - accuracy: 0.9866 - val_loss: 0.1933 - val_accuracy: 0.9500 Epoch 37/200 298/298 [==============================] - 0s 75us/step - loss: 0.0719 - accuracy: 0.9899 - val_loss: 0.1988 - val_accuracy: 0.9500 Epoch 38/200 298/298 [==============================] - 0s 73us/step - loss: 0.0682 - accuracy: 0.9933 - val_loss: 0.2554 - val_accuracy: 0.9400 Epoch 39/200 298/298 [==============================] - 0s 70us/step - loss: 0.0767 - accuracy: 0.9866 - val_loss: 0.2185 - val_accuracy: 0.9300 Epoch 40/200 298/298 [==============================] - 0s 67us/step - loss: 0.0665 - accuracy: 0.9933 - val_loss: 0.2051 - val_accuracy: 0.9500 Epoch 41/200 298/298 [==============================] - 0s 73us/step - loss: 0.0651 - accuracy: 0.9933 - val_loss: 0.1798 - val_accuracy: 0.9500 Epoch 42/200 298/298 [==============================] - 0s 65us/step - loss: 0.0664 - accuracy: 0.9933 - val_loss: 0.1792 - val_accuracy: 0.9500 Epoch 43/200 298/298 [==============================] - 0s 73us/step - loss: 0.0688 - accuracy: 0.9899 - val_loss: 0.2379 - val_accuracy: 0.9500 Epoch 44/200 298/298 [==============================] - 0s 69us/step - loss: 0.0643 - accuracy: 0.9899 - val_loss: 0.2056 - val_accuracy: 0.9400 Epoch 45/200 298/298 [==============================] - 0s 68us/step - loss: 0.0661 - accuracy: 0.9933 - val_loss: 0.2086 - val_accuracy: 0.9300 Epoch 46/200 298/298 [==============================] - 0s 71us/step - loss: 0.0651 - accuracy: 0.9933 - val_loss: 0.2249 - val_accuracy: 0.9400 Epoch 47/200 298/298 [==============================] - 0s 76us/step - loss: 0.0664 - accuracy: 0.9866 - val_loss: 0.2389 - val_accuracy: 0.9200 Epoch 48/200 298/298 [==============================] - 0s 68us/step - loss: 0.0601 - accuracy: 0.9933 - val_loss: 0.2850 - val_accuracy: 0.9500 Epoch 49/200 298/298 [==============================] - 0s 74us/step - loss: 0.0659 - accuracy: 0.9899 - val_loss: 0.2092 - val_accuracy: 0.9400 Epoch 50/200 298/298 [==============================] - 0s 71us/step - loss: 0.0597 - accuracy: 0.9966 - val_loss: 0.2270 - val_accuracy: 0.9500 Epoch 51/200 298/298 [==============================] - 0s 70us/step - loss: 0.0582 - accuracy: 0.9933 - val_loss: 0.2472 - val_accuracy: 0.9400 Epoch 52/200 298/298 [==============================] - 0s 62us/step - loss: 0.0698 - accuracy: 0.9866 - val_loss: 0.2090 - val_accuracy: 0.9500 Epoch 53/200 298/298 [==============================] - 0s 75us/step - loss: 0.0584 - accuracy: 0.9933 - val_loss: 0.2077 - val_accuracy: 0.9400 Epoch 54/200 298/298 [==============================] - 0s 65us/step - loss: 0.0626 - accuracy: 0.9933 - val_loss: 0.1864 - val_accuracy: 0.9600 Epoch 55/200 298/298 [==============================] - 0s 81us/step - loss: 0.0574 - accuracy: 0.9966 - val_loss: 0.1814 - val_accuracy: 0.9500 Epoch 56/200 298/298 [==============================] - 0s 74us/step - loss: 0.0570 - accuracy: 0.9933 - val_loss: 0.2223 - val_accuracy: 0.9500 Epoch 57/200 298/298 [==============================] - 0s 72us/step - loss: 0.0574 - accuracy: 0.9933 - val_loss: 0.2453 - val_accuracy: 0.9500 Epoch 58/200 298/298 [==============================] - 0s 64us/step - loss: 0.0682 - accuracy: 0.9866 - val_loss: 0.1934 - val_accuracy: 0.9400 Epoch 59/200 298/298 [==============================] - 0s 72us/step - loss: 0.0563 - accuracy: 0.9899 - val_loss: 0.1934 - val_accuracy: 0.9400 Epoch 60/200 298/298 [==============================] - 0s 74us/step - loss: 0.0537 - accuracy: 0.9966 - val_loss: 0.1861 - val_accuracy: 0.9500 Epoch 61/200 298/298 [==============================] - 0s 63us/step - loss: 0.0537 - accuracy: 0.9933 - val_loss: 0.1816 - val_accuracy: 0.9500 Epoch 62/200 298/298 [==============================] - 0s 80us/step - loss: 0.0582 - accuracy: 0.9933 - val_loss: 0.2201 - val_accuracy: 0.9400 Epoch 63/200 298/298 [==============================] - 0s 71us/step - loss: 0.0597 - accuracy: 0.9832 - val_loss: 0.1767 - val_accuracy: 0.9600 Epoch 64/200 298/298 [==============================] - 0s 70us/step - loss: 0.0532 - accuracy: 0.9933 - val_loss: 0.1797 - val_accuracy: 0.9500 Epoch 65/200 298/298 [==============================] - 0s 66us/step - loss: 0.0582 - accuracy: 0.9933 - val_loss: 0.1749 - val_accuracy: 0.9600 Epoch 66/200 298/298 [==============================] - 0s 74us/step - loss: 0.0542 - accuracy: 0.9933 - val_loss: 0.1967 - val_accuracy: 0.9500 Epoch 67/200 298/298 [==============================] - 0s 64us/step - loss: 0.0540 - accuracy: 0.9933 - val_loss: 0.1965 - val_accuracy: 0.9400 Epoch 68/200 298/298 [==============================] - 0s 75us/step - loss: 0.0538 - accuracy: 0.9899 - val_loss: 0.1596 - val_accuracy: 0.9600 Epoch 69/200 298/298 [==============================] - 0s 65us/step - loss: 0.0589 - accuracy: 0.9933 - val_loss: 0.2329 - val_accuracy: 0.9500 Epoch 70/200 298/298 [==============================] - 0s 67us/step - loss: 0.0518 - accuracy: 0.9933 - val_loss: 0.2212 - val_accuracy: 0.9400 Epoch 71/200 298/298 [==============================] - 0s 74us/step - loss: 0.0525 - accuracy: 0.9933 - val_loss: 0.2008 - val_accuracy: 0.9400 Epoch 72/200 298/298 [==============================] - 0s 74us/step - loss: 0.0493 - accuracy: 0.9933 - val_loss: 0.1628 - val_accuracy: 0.9600 Epoch 73/200 298/298 [==============================] - 0s 66us/step - loss: 0.0514 - accuracy: 0.9933 - val_loss: 0.1674 - val_accuracy: 0.9500 Epoch 74/200 298/298 [==============================] - 0s 75us/step - loss: 0.0538 - accuracy: 0.9899 - val_loss: 0.2068 - val_accuracy: 0.9500 Epoch 75/200 298/298 [==============================] - 0s 75us/step - loss: 0.0519 - accuracy: 0.9933 - val_loss: 0.2674 - val_accuracy: 0.9500 Epoch 76/200 298/298 [==============================] - 0s 65us/step - loss: 0.0530 - accuracy: 0.9933 - val_loss: 0.1847 - val_accuracy: 0.9400 Epoch 77/200 298/298 [==============================] - 0s 72us/step - loss: 0.0513 - accuracy: 0.9966 - val_loss: 0.2071 - val_accuracy: 0.9500 Epoch 78/200 298/298 [==============================] - 0s 73us/step - loss: 0.0467 - accuracy: 1.0000 - val_loss: 0.2479 - val_accuracy: 0.9500 Epoch 79/200 298/298 [==============================] - 0s 69us/step - loss: 0.0553 - accuracy: 0.9933 - val_loss: 0.1897 - val_accuracy: 0.9500 Epoch 80/200 298/298 [==============================] - 0s 67us/step - loss: 0.0482 - accuracy: 0.9933 - val_loss: 0.2245 - val_accuracy: 0.9400 Epoch 81/200 298/298 [==============================] - 0s 75us/step - loss: 0.0532 - accuracy: 0.9966 - val_loss: 0.2402 - val_accuracy: 0.9500 Epoch 82/200 298/298 [==============================] - 0s 66us/step - loss: 0.0467 - accuracy: 0.9966 - val_loss: 0.2607 - val_accuracy: 0.9500 Epoch 83/200 298/298 [==============================] - 0s 75us/step - loss: 0.0548 - accuracy: 0.9933 - val_loss: 0.1892 - val_accuracy: 0.9500 Epoch 84/200 298/298 [==============================] - 0s 72us/step - loss: 0.0475 - accuracy: 0.9933 - val_loss: 0.2165 - val_accuracy: 0.9500 Epoch 85/200 298/298 [==============================] - 0s 66us/step - loss: 0.0453 - accuracy: 0.9966 - val_loss: 0.2494 - val_accuracy: 0.9400 Epoch 86/200 298/298 [==============================] - 0s 74us/step - loss: 0.0516 - accuracy: 0.9899 - val_loss: 0.2311 - val_accuracy: 0.9500 Epoch 87/200 298/298 [==============================] - 0s 76us/step - loss: 0.0504 - accuracy: 0.9966 - val_loss: 0.1842 - val_accuracy: 0.9600 Epoch 88/200 298/298 [==============================] - 0s 66us/step - loss: 0.0493 - accuracy: 0.9933 - val_loss: 0.2331 - val_accuracy: 0.9500 Epoch 89/200 298/298 [==============================] - 0s 65us/step - loss: 0.0469 - accuracy: 0.9933 - val_loss: 0.2277 - val_accuracy: 0.9500 Epoch 90/200 298/298 [==============================] - 0s 72us/step - loss: 0.0498 - accuracy: 0.9933 - val_loss: 0.2044 - val_accuracy: 0.9500 Epoch 91/200 298/298 [==============================] - 0s 69us/step - loss: 0.0458 - accuracy: 0.9966 - val_loss: 0.2066 - val_accuracy: 0.9400 Epoch 92/200 298/298 [==============================] - 0s 79us/step - loss: 0.0450 - accuracy: 1.0000 - val_loss: 0.1985 - val_accuracy: 0.9400 Epoch 93/200 298/298 [==============================] - 0s 73us/step - loss: 0.0498 - accuracy: 0.9899 - val_loss: 0.1492 - val_accuracy: 0.9600 Epoch 94/200 298/298 [==============================] - 0s 70us/step - loss: 0.0438 - accuracy: 1.0000 - val_loss: 0.2347 - val_accuracy: 0.9400 Epoch 95/200 298/298 [==============================] - 0s 65us/step - loss: 0.0442 - accuracy: 0.9966 - val_loss: 0.1672 - val_accuracy: 0.9500 Epoch 96/200 298/298 [==============================] - 0s 76us/step - loss: 0.0497 - accuracy: 0.9899 - val_loss: 0.1796 - val_accuracy: 0.9600 Epoch 97/200 298/298 [==============================] - 0s 64us/step - loss: 0.0494 - accuracy: 0.9933 - val_loss: 0.2161 - val_accuracy: 0.9300 Epoch 98/200 298/298 [==============================] - 0s 72us/step - loss: 0.0432 - accuracy: 0.9966 - val_loss: 0.2461 - val_accuracy: 0.9400 Epoch 99/200 298/298 [==============================] - 0s 73us/step - loss: 0.0464 - accuracy: 0.9899 - val_loss: 0.2005 - val_accuracy: 0.9500 Epoch 100/200 298/298 [==============================] - 0s 72us/step - loss: 0.0474 - accuracy: 0.9933 - val_loss: 0.1909 - val_accuracy: 0.9500 Epoch 101/200 298/298 [==============================] - 0s 68us/step - loss: 0.0465 - accuracy: 0.9966 - val_loss: 0.1927 - val_accuracy: 0.9500 Epoch 102/200 298/298 [==============================] - 0s 77us/step - loss: 0.0435 - accuracy: 0.9966 - val_loss: 0.2127 - val_accuracy: 0.9500 Epoch 103/200 298/298 [==============================] - 0s 71us/step - loss: 0.0451 - accuracy: 0.9966 - val_loss: 0.2555 - val_accuracy: 0.9400 Epoch 104/200 298/298 [==============================] - 0s 65us/step - loss: 0.0555 - accuracy: 0.9866 - val_loss: 0.1944 - val_accuracy: 0.9400 Epoch 105/200 298/298 [==============================] - 0s 73us/step - loss: 0.0431 - accuracy: 0.9966 - val_loss: 0.2174 - val_accuracy: 0.9400 Epoch 106/200 298/298 [==============================] - 0s 71us/step - loss: 0.0454 - accuracy: 0.9933 - val_loss: 0.2349 - val_accuracy: 0.9400 Epoch 107/200 298/298 [==============================] - 0s 66us/step - loss: 0.0447 - accuracy: 0.9966 - val_loss: 0.2208 - val_accuracy: 0.9400 Epoch 108/200 298/298 [==============================] - 0s 70us/step - loss: 0.0444 - accuracy: 0.9933 - val_loss: 0.2172 - val_accuracy: 0.9400 Epoch 109/200 298/298 [==============================] - 0s 75us/step - loss: 0.0444 - accuracy: 0.9933 - val_loss: 0.1872 - val_accuracy: 0.9500 Epoch 110/200 298/298 [==============================] - 0s 70us/step - loss: 0.0465 - accuracy: 0.9966 - val_loss: 0.2202 - val_accuracy: 0.9400 Epoch 111/200 298/298 [==============================] - 0s 75us/step - loss: 0.0410 - accuracy: 1.0000 - val_loss: 0.2036 - val_accuracy: 0.9400 Epoch 112/200 298/298 [==============================] - 0s 69us/step - loss: 0.0436 - accuracy: 0.9933 - val_loss: 0.2136 - val_accuracy: 0.9600 Epoch 113/200 298/298 [==============================] - 0s 66us/step - loss: 0.0485 - accuracy: 0.9899 - val_loss: 0.1897 - val_accuracy: 0.9500 Epoch 114/200 298/298 [==============================] - 0s 71us/step - loss: 0.0431 - accuracy: 1.0000 - val_loss: 0.2108 - val_accuracy: 0.9400 Epoch 115/200 298/298 [==============================] - 0s 69us/step - loss: 0.0447 - accuracy: 0.9966 - val_loss: 0.2031 - val_accuracy: 0.9400 Epoch 116/200 298/298 [==============================] - 0s 66us/step - loss: 0.0403 - accuracy: 0.9966 - val_loss: 0.1863 - val_accuracy: 0.9500 Epoch 117/200 298/298 [==============================] - 0s 70us/step - loss: 0.0425 - accuracy: 0.9966 - val_loss: 0.1849 - val_accuracy: 0.9500 Epoch 118/200 298/298 [==============================] - 0s 70us/step - loss: 0.0420 - accuracy: 1.0000 - val_loss: 0.2072 - val_accuracy: 0.9500 Epoch 119/200 298/298 [==============================] - 0s 70us/step - loss: 0.0463 - accuracy: 0.9933 - val_loss: 0.2109 - val_accuracy: 0.9400 Epoch 120/200 298/298 [==============================] - 0s 74us/step - loss: 0.0425 - accuracy: 0.9966 - val_loss: 0.2111 - val_accuracy: 0.9400 Epoch 121/200 298/298 [==============================] - 0s 74us/step - loss: 0.0433 - accuracy: 0.9966 - val_loss: 0.2423 - val_accuracy: 0.9300 Epoch 122/200 298/298 [==============================] - 0s 70us/step - loss: 0.0449 - accuracy: 0.9966 - val_loss: 0.2294 - val_accuracy: 0.9400 Epoch 123/200 298/298 [==============================] - 0s 71us/step - loss: 0.0389 - accuracy: 1.0000 - val_loss: 0.1979 - val_accuracy: 0.9500 Epoch 124/200 298/298 [==============================] - 0s 68us/step - loss: 0.0398 - accuracy: 1.0000 - val_loss: 0.2633 - val_accuracy: 0.9400 Epoch 125/200 298/298 [==============================] - 0s 68us/step - loss: 0.0426 - accuracy: 1.0000 - val_loss: 0.2700 - val_accuracy: 0.9400 Epoch 126/200 298/298 [==============================] - 0s 67us/step - loss: 0.0436 - accuracy: 0.9933 - val_loss: 0.1971 - val_accuracy: 0.9500 Epoch 127/200 298/298 [==============================] - 0s 72us/step - loss: 0.0476 - accuracy: 0.9966 - val_loss: 0.2238 - val_accuracy: 0.9500 Epoch 128/200 298/298 [==============================] - 0s 68us/step - loss: 0.0413 - accuracy: 0.9966 - val_loss: 0.2188 - val_accuracy: 0.9400 Epoch 129/200 298/298 [==============================] - 0s 80us/step - loss: 0.0379 - accuracy: 1.0000 - val_loss: 0.1835 - val_accuracy: 0.9500 Epoch 130/200 298/298 [==============================] - 0s 72us/step - loss: 0.0417 - accuracy: 0.9966 - val_loss: 0.2201 - val_accuracy: 0.9500 Epoch 131/200 298/298 [==============================] - 0s 71us/step - loss: 0.0383 - accuracy: 1.0000 - val_loss: 0.2244 - val_accuracy: 0.9400 Epoch 132/200 298/298 [==============================] - 0s 63us/step - loss: 0.0429 - accuracy: 0.9899 - val_loss: 0.2255 - val_accuracy: 0.9200 Epoch 133/200 298/298 [==============================] - 0s 71us/step - loss: 0.0404 - accuracy: 1.0000 - val_loss: 0.2427 - val_accuracy: 0.9400 Epoch 134/200 298/298 [==============================] - 0s 68us/step - loss: 0.0382 - accuracy: 0.9966 - val_loss: 0.2295 - val_accuracy: 0.9400 Epoch 135/200 298/298 [==============================] - 0s 70us/step - loss: 0.0432 - accuracy: 0.9933 - val_loss: 0.2149 - val_accuracy: 0.9300 Epoch 136/200 298/298 [==============================] - 0s 73us/step - loss: 0.0418 - accuracy: 0.9966 - val_loss: 0.1863 - val_accuracy: 0.9500 Epoch 137/200 298/298 [==============================] - 0s 69us/step - loss: 0.0414 - accuracy: 0.9966 - val_loss: 0.2010 - val_accuracy: 0.9500 Epoch 138/200 298/298 [==============================] - 0s 73us/step - loss: 0.0426 - accuracy: 0.9933 - val_loss: 0.1892 - val_accuracy: 0.9500 Epoch 139/200 298/298 [==============================] - 0s 77us/step - loss: 0.0404 - accuracy: 0.9966 - val_loss: 0.2277 - val_accuracy: 0.9500 Epoch 140/200 298/298 [==============================] - 0s 71us/step - loss: 0.0432 - accuracy: 0.9966 - val_loss: 0.1956 - val_accuracy: 0.9500 Epoch 141/200 298/298 [==============================] - 0s 64us/step - loss: 0.0411 - accuracy: 0.9966 - val_loss: 0.2072 - val_accuracy: 0.9400 Epoch 142/200 298/298 [==============================] - 0s 72us/step - loss: 0.0380 - accuracy: 1.0000 - val_loss: 0.1682 - val_accuracy: 0.9600 Epoch 143/200 298/298 [==============================] - 0s 63us/step - loss: 0.0422 - accuracy: 0.9966 - val_loss: 0.1868 - val_accuracy: 0.9600 Epoch 144/200 298/298 [==============================] - 0s 75us/step - loss: 0.0377 - accuracy: 1.0000 - val_loss: 0.2265 - val_accuracy: 0.9500 Epoch 145/200 298/298 [==============================] - 0s 71us/step - loss: 0.0457 - accuracy: 0.9933 - val_loss: 0.1880 - val_accuracy: 0.9500 Epoch 146/200 298/298 [==============================] - 0s 74us/step - loss: 0.0366 - accuracy: 1.0000 - val_loss: 0.2266 - val_accuracy: 0.9400 Epoch 147/200 298/298 [==============================] - 0s 65us/step - loss: 0.0386 - accuracy: 0.9966 - val_loss: 0.2289 - val_accuracy: 0.9400 Epoch 148/200 298/298 [==============================] - 0s 74us/step - loss: 0.0409 - accuracy: 0.9966 - val_loss: 0.2232 - val_accuracy: 0.9300 Epoch 149/200 298/298 [==============================] - 0s 75us/step - loss: 0.0392 - accuracy: 0.9966 - val_loss: 0.2524 - val_accuracy: 0.9400 Epoch 150/200 298/298 [==============================] - 0s 65us/step - loss: 0.0410 - accuracy: 0.9966 - val_loss: 0.1861 - val_accuracy: 0.9600 Epoch 151/200 298/298 [==============================] - 0s 73us/step - loss: 0.0363 - accuracy: 0.9966 - val_loss: 0.2684 - val_accuracy: 0.9500 Epoch 152/200 298/298 [==============================] - 0s 72us/step - loss: 0.0401 - accuracy: 0.9966 - val_loss: 0.2081 - val_accuracy: 0.9500 Epoch 153/200 298/298 [==============================] - 0s 68us/step - loss: 0.0367 - accuracy: 1.0000 - val_loss: 0.2001 - val_accuracy: 0.9500 Epoch 154/200 298/298 [==============================] - 0s 70us/step - loss: 0.0390 - accuracy: 0.9933 - val_loss: 0.2082 - val_accuracy: 0.9400 Epoch 155/200 298/298 [==============================] - 0s 74us/step - loss: 0.0429 - accuracy: 0.9966 - val_loss: 0.2204 - val_accuracy: 0.9400 Epoch 156/200 298/298 [==============================] - 0s 66us/step - loss: 0.0368 - accuracy: 1.0000 - val_loss: 0.1950 - val_accuracy: 0.9500 Epoch 157/200 298/298 [==============================] - 0s 77us/step - loss: 0.0384 - accuracy: 0.9966 - val_loss: 0.2509 - val_accuracy: 0.9400 Epoch 158/200 298/298 [==============================] - 0s 74us/step - loss: 0.0384 - accuracy: 0.9966 - val_loss: 0.1955 - val_accuracy: 0.9500 Epoch 159/200 298/298 [==============================] - 0s 74us/step - loss: 0.0406 - accuracy: 0.9933 - val_loss: 0.1945 - val_accuracy: 0.9500 Epoch 160/200 298/298 [==============================] - 0s 61us/step - loss: 0.0360 - accuracy: 1.0000 - val_loss: 0.1941 - val_accuracy: 0.9500 Epoch 161/200 298/298 [==============================] - 0s 72us/step - loss: 0.0383 - accuracy: 0.9966 - val_loss: 0.2387 - val_accuracy: 0.9300 Epoch 162/200 298/298 [==============================] - 0s 64us/step - loss: 0.0434 - accuracy: 1.0000 - val_loss: 0.2061 - val_accuracy: 0.9500 Epoch 163/200 298/298 [==============================] - 0s 76us/step - loss: 0.0360 - accuracy: 1.0000 - val_loss: 0.1863 - val_accuracy: 0.9500 Epoch 164/200 298/298 [==============================] - 0s 73us/step - loss: 0.0397 - accuracy: 0.9933 - val_loss: 0.1975 - val_accuracy: 0.9400 Epoch 165/200 298/298 [==============================] - 0s 65us/step - loss: 0.0380 - accuracy: 0.9933 - val_loss: 0.1840 - val_accuracy: 0.9500 Epoch 166/200 298/298 [==============================] - 0s 76us/step - loss: 0.0382 - accuracy: 0.9966 - val_loss: 0.2095 - val_accuracy: 0.9600 Epoch 167/200 298/298 [==============================] - 0s 73us/step - loss: 0.0391 - accuracy: 0.9933 - val_loss: 0.2140 - val_accuracy: 0.9500 Epoch 168/200 298/298 [==============================] - 0s 71us/step - loss: 0.0357 - accuracy: 1.0000 - val_loss: 0.1928 - val_accuracy: 0.9600 Epoch 169/200 298/298 [==============================] - 0s 63us/step - loss: 0.0359 - accuracy: 0.9966 - val_loss: 0.1877 - val_accuracy: 0.9500 Epoch 170/200 298/298 [==============================] - 0s 73us/step - loss: 0.0358 - accuracy: 1.0000 - val_loss: 0.1767 - val_accuracy: 0.9600 Epoch 171/200 298/298 [==============================] - 0s 64us/step - loss: 0.0356 - accuracy: 1.0000 - val_loss: 0.2195 - val_accuracy: 0.9300 Epoch 172/200 298/298 [==============================] - 0s 72us/step - loss: 0.0401 - accuracy: 0.9933 - val_loss: 0.2136 - val_accuracy: 0.9400 Epoch 173/200 298/298 [==============================] - 0s 76us/step - loss: 0.0349 - accuracy: 0.9966 - val_loss: 0.2194 - val_accuracy: 0.9400 Epoch 174/200 298/298 [==============================] - 0s 71us/step - loss: 0.0384 - accuracy: 0.9966 - val_loss: 0.1785 - val_accuracy: 0.9600 Epoch 175/200 298/298 [==============================] - 0s 61us/step - loss: 0.0371 - accuracy: 0.9966 - val_loss: 0.2484 - val_accuracy: 0.9400 Epoch 176/200 298/298 [==============================] - 0s 79us/step - loss: 0.0397 - accuracy: 0.9966 - val_loss: 0.2357 - val_accuracy: 0.9500 Epoch 177/200 298/298 [==============================] - 0s 69us/step - loss: 0.0356 - accuracy: 0.9966 - val_loss: 0.2089 - val_accuracy: 0.9300 Epoch 178/200 298/298 [==============================] - 0s 73us/step - loss: 0.0327 - accuracy: 1.0000 - val_loss: 0.2463 - val_accuracy: 0.9300 Epoch 179/200 298/298 [==============================] - 0s 76us/step - loss: 0.0367 - accuracy: 1.0000 - val_loss: 0.1817 - val_accuracy: 0.9500 Epoch 180/200 298/298 [==============================] - 0s 72us/step - loss: 0.0411 - accuracy: 0.9933 - val_loss: 0.1918 - val_accuracy: 0.9400 Epoch 181/200 298/298 [==============================] - 0s 65us/step - loss: 0.0400 - accuracy: 0.9966 - val_loss: 0.1969 - val_accuracy: 0.9500 Epoch 182/200 298/298 [==============================] - 0s 69us/step - loss: 0.0335 - accuracy: 1.0000 - val_loss: 0.1734 - val_accuracy: 0.9500 Epoch 183/200 298/298 [==============================] - 0s 74us/step - loss: 0.0372 - accuracy: 0.9966 - val_loss: 0.1980 - val_accuracy: 0.9500 Epoch 184/200 298/298 [==============================] - 0s 65us/step - loss: 0.0381 - accuracy: 0.9966 - val_loss: 0.2259 - val_accuracy: 0.9400 Epoch 185/200 298/298 [==============================] - 0s 75us/step - loss: 0.0368 - accuracy: 0.9966 - val_loss: 0.2168 - val_accuracy: 0.9400 Epoch 186/200 298/298 [==============================] - 0s 69us/step - loss: 0.0342 - accuracy: 0.9966 - val_loss: 0.1815 - val_accuracy: 0.9500 Epoch 187/200 298/298 [==============================] - 0s 71us/step - loss: 0.0349 - accuracy: 0.9966 - val_loss: 0.1991 - val_accuracy: 0.9500 Epoch 188/200 298/298 [==============================] - 0s 68us/step - loss: 0.0321 - accuracy: 1.0000 - val_loss: 0.2525 - val_accuracy: 0.9500 Epoch 189/200 298/298 [==============================] - 0s 73us/step - loss: 0.0401 - accuracy: 0.9933 - val_loss: 0.2203 - val_accuracy: 0.9400 Epoch 190/200 298/298 [==============================] - 0s 70us/step - loss: 0.0368 - accuracy: 0.9966 - val_loss: 0.2678 - val_accuracy: 0.9400 Epoch 191/200 298/298 [==============================] - 0s 71us/step - loss: 0.0364 - accuracy: 1.0000 - val_loss: 0.2271 - val_accuracy: 0.9400 Epoch 192/200 298/298 [==============================] - 0s 74us/step - loss: 0.0350 - accuracy: 1.0000 - val_loss: 0.1926 - val_accuracy: 0.9500 Epoch 193/200 298/298 [==============================] - 0s 65us/step - loss: 0.0347 - accuracy: 1.0000 - val_loss: 0.2115 - val_accuracy: 0.9400 Epoch 194/200 298/298 [==============================] - 0s 72us/step - loss: 0.0397 - accuracy: 0.9966 - val_loss: 0.2138 - val_accuracy: 0.9400 Epoch 195/200 298/298 [==============================] - 0s 75us/step - loss: 0.0342 - accuracy: 1.0000 - val_loss: 0.2162 - val_accuracy: 0.9300 Epoch 196/200 298/298 [==============================] - 0s 68us/step - loss: 0.0338 - accuracy: 1.0000 - val_loss: 0.1984 - val_accuracy: 0.9600 Epoch 197/200 298/298 [==============================] - 0s 67us/step - loss: 0.0356 - accuracy: 0.9966 - val_loss: 0.2154 - val_accuracy: 0.9400 Epoch 198/200 298/298 [==============================] - 0s 74us/step - loss: 0.0374 - accuracy: 0.9933 - val_loss: 0.2443 - val_accuracy: 0.9400 Epoch 199/200 298/298 [==============================] - 0s 65us/step - loss: 0.0377 - accuracy: 0.9966 - val_loss: 0.2039 - val_accuracy: 0.9400 Epoch 200/200 298/298 [==============================] - 0s 74us/step - loss: 0.0347 - accuracy: 0.9966 - val_loss: 0.1893 - val_accuracy: 0.9500 171/171 [==============================] - 0s 49us/step Optimizers: <keras.optimizers.RMSprop object at 0x10a1d9a50> Epoch Sizes: 200 Neurons or Units: 256 ['loss', 'accuracy'] [0.08162528058590247, 0.9824561476707458] Test score: 0.08162528058590247 Test accuracy: 0.9824561476707458 Model: "sequential_46" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_136 (Dense) (None, 64) 1984 _________________________________________________________________ activation_136 (Activation) (None, 64) 0 _________________________________________________________________ dense_137 (Dense) (None, 64) 4160 _________________________________________________________________ activation_137 (Activation) (None, 64) 0 _________________________________________________________________ dense_138 (Dense) (None, 1) 65 _________________________________________________________________ activation_138 (Activation) (None, 1) 0 ================================================================= Total params: 6,209 Trainable params: 6,209 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/50 298/298 [==============================] - 0s 662us/step - loss: 1.5406 - accuracy: 0.8289 - val_loss: 1.3821 - val_accuracy: 0.9300 Epoch 2/50 298/298 [==============================] - 0s 53us/step - loss: 1.2987 - accuracy: 0.9631 - val_loss: 1.2185 - val_accuracy: 0.9100 Epoch 3/50 298/298 [==============================] - 0s 50us/step - loss: 1.1386 - accuracy: 0.9631 - val_loss: 1.1050 - val_accuracy: 0.9100 Epoch 4/50 298/298 [==============================] - 0s 55us/step - loss: 1.0232 - accuracy: 0.9631 - val_loss: 1.0204 - val_accuracy: 0.9100 Epoch 5/50 298/298 [==============================] - 0s 58us/step - loss: 0.9322 - accuracy: 0.9664 - val_loss: 0.9485 - val_accuracy: 0.9100 Epoch 6/50 298/298 [==============================] - 0s 51us/step - loss: 0.8542 - accuracy: 0.9698 - val_loss: 0.8827 - val_accuracy: 0.9100 Epoch 7/50 298/298 [==============================] - 0s 57us/step - loss: 0.7844 - accuracy: 0.9698 - val_loss: 0.8200 - val_accuracy: 0.9100 Epoch 8/50 298/298 [==============================] - 0s 50us/step - loss: 0.7211 - accuracy: 0.9732 - val_loss: 0.7668 - val_accuracy: 0.9100 Epoch 9/50 298/298 [==============================] - 0s 56us/step - loss: 0.6635 - accuracy: 0.9732 - val_loss: 0.7120 - val_accuracy: 0.9300 Epoch 10/50 298/298 [==============================] - 0s 50us/step - loss: 0.6107 - accuracy: 0.9732 - val_loss: 0.6662 - val_accuracy: 0.9400 Epoch 11/50 298/298 [==============================] - 0s 56us/step - loss: 0.5627 - accuracy: 0.9765 - val_loss: 0.6239 - val_accuracy: 0.9400 Epoch 12/50 298/298 [==============================] - 0s 51us/step - loss: 0.5182 - accuracy: 0.9832 - val_loss: 0.5825 - val_accuracy: 0.9400 Epoch 13/50 298/298 [==============================] - 0s 51us/step - loss: 0.4784 - accuracy: 0.9832 - val_loss: 0.5473 - val_accuracy: 0.9400 Epoch 14/50 298/298 [==============================] - 0s 49us/step - loss: 0.4418 - accuracy: 0.9866 - val_loss: 0.5156 - val_accuracy: 0.9400 Epoch 15/50 298/298 [==============================] - 0s 58us/step - loss: 0.4085 - accuracy: 0.9832 - val_loss: 0.4874 - val_accuracy: 0.9400 Epoch 16/50 298/298 [==============================] - 0s 48us/step - loss: 0.3780 - accuracy: 0.9899 - val_loss: 0.4592 - val_accuracy: 0.9400 Epoch 17/50 298/298 [==============================] - 0s 57us/step - loss: 0.3502 - accuracy: 0.9933 - val_loss: 0.4343 - val_accuracy: 0.9400 Epoch 18/50 298/298 [==============================] - 0s 47us/step - loss: 0.3255 - accuracy: 0.9933 - val_loss: 0.4136 - val_accuracy: 0.9400 Epoch 19/50 298/298 [==============================] - 0s 57us/step - loss: 0.3026 - accuracy: 0.9933 - val_loss: 0.3924 - val_accuracy: 0.9400 Epoch 20/50 298/298 [==============================] - 0s 49us/step - loss: 0.2822 - accuracy: 0.9933 - val_loss: 0.3742 - val_accuracy: 0.9400 Epoch 21/50 298/298 [==============================] - 0s 55us/step - loss: 0.2636 - accuracy: 0.9933 - val_loss: 0.3591 - val_accuracy: 0.9500 Epoch 22/50 298/298 [==============================] - 0s 50us/step - loss: 0.2462 - accuracy: 0.9933 - val_loss: 0.3424 - val_accuracy: 0.9400 Epoch 23/50 298/298 [==============================] - 0s 53us/step - loss: 0.2310 - accuracy: 0.9933 - val_loss: 0.3222 - val_accuracy: 0.9400 Epoch 24/50 298/298 [==============================] - 0s 53us/step - loss: 0.2167 - accuracy: 0.9933 - val_loss: 0.3118 - val_accuracy: 0.9500 Epoch 25/50 298/298 [==============================] - 0s 53us/step - loss: 0.2041 - accuracy: 0.9933 - val_loss: 0.3022 - val_accuracy: 0.9500 Epoch 26/50 298/298 [==============================] - 0s 47us/step - loss: 0.1928 - accuracy: 0.9933 - val_loss: 0.2930 - val_accuracy: 0.9500 Epoch 27/50 298/298 [==============================] - 0s 54us/step - loss: 0.1827 - accuracy: 0.9933 - val_loss: 0.2873 - val_accuracy: 0.9500 Epoch 28/50 298/298 [==============================] - 0s 47us/step - loss: 0.1731 - accuracy: 0.9933 - val_loss: 0.2754 - val_accuracy: 0.9500 Epoch 29/50 298/298 [==============================] - 0s 60us/step - loss: 0.1646 - accuracy: 0.9933 - val_loss: 0.2688 - val_accuracy: 0.9500 Epoch 30/50 298/298 [==============================] - 0s 47us/step - loss: 0.1569 - accuracy: 0.9966 - val_loss: 0.2581 - val_accuracy: 0.9400 Epoch 31/50 298/298 [==============================] - 0s 55us/step - loss: 0.1500 - accuracy: 0.9966 - val_loss: 0.2546 - val_accuracy: 0.9400 Epoch 32/50 298/298 [==============================] - 0s 47us/step - loss: 0.1436 - accuracy: 0.9933 - val_loss: 0.2526 - val_accuracy: 0.9400 Epoch 33/50 298/298 [==============================] - 0s 56us/step - loss: 0.1381 - accuracy: 0.9966 - val_loss: 0.2473 - val_accuracy: 0.9400 Epoch 34/50 298/298 [==============================] - 0s 49us/step - loss: 0.1325 - accuracy: 0.9966 - val_loss: 0.2392 - val_accuracy: 0.9400 Epoch 35/50 298/298 [==============================] - 0s 55us/step - loss: 0.1289 - accuracy: 0.9933 - val_loss: 0.2335 - val_accuracy: 0.9400 Epoch 36/50 298/298 [==============================] - 0s 49us/step - loss: 0.1234 - accuracy: 0.9966 - val_loss: 0.2332 - val_accuracy: 0.9400 Epoch 37/50 298/298 [==============================] - 0s 52us/step - loss: 0.1193 - accuracy: 0.9966 - val_loss: 0.2329 - val_accuracy: 0.9400 Epoch 38/50 298/298 [==============================] - 0s 48us/step - loss: 0.1149 - accuracy: 0.9966 - val_loss: 0.2342 - val_accuracy: 0.9400 Epoch 39/50 298/298 [==============================] - 0s 54us/step - loss: 0.1127 - accuracy: 0.9899 - val_loss: 0.2311 - val_accuracy: 0.9400 Epoch 40/50 298/298 [==============================] - 0s 49us/step - loss: 0.1094 - accuracy: 0.9899 - val_loss: 0.2256 - val_accuracy: 0.9400 Epoch 41/50 298/298 [==============================] - 0s 61us/step - loss: 0.1063 - accuracy: 0.9899 - val_loss: 0.2210 - val_accuracy: 0.9400 Epoch 42/50 298/298 [==============================] - 0s 48us/step - loss: 0.1039 - accuracy: 0.9966 - val_loss: 0.2138 - val_accuracy: 0.9400 Epoch 43/50 298/298 [==============================] - 0s 57us/step - loss: 0.1010 - accuracy: 0.9933 - val_loss: 0.2134 - val_accuracy: 0.9400 Epoch 44/50 298/298 [==============================] - 0s 55us/step - loss: 0.0981 - accuracy: 0.9933 - val_loss: 0.2156 - val_accuracy: 0.9400 Epoch 45/50 298/298 [==============================] - 0s 48us/step - loss: 0.0960 - accuracy: 0.9933 - val_loss: 0.2142 - val_accuracy: 0.9400 Epoch 46/50 298/298 [==============================] - 0s 51us/step - loss: 0.0949 - accuracy: 0.9899 - val_loss: 0.2190 - val_accuracy: 0.9400 Epoch 47/50 298/298 [==============================] - 0s 49us/step - loss: 0.0925 - accuracy: 0.9966 - val_loss: 0.2006 - val_accuracy: 0.9500 Epoch 48/50 298/298 [==============================] - 0s 51us/step - loss: 0.0910 - accuracy: 0.9966 - val_loss: 0.2049 - val_accuracy: 0.9400 Epoch 49/50 298/298 [==============================] - 0s 55us/step - loss: 0.0896 - accuracy: 0.9933 - val_loss: 0.2087 - val_accuracy: 0.9400 Epoch 50/50 298/298 [==============================] - 0s 48us/step - loss: 0.0877 - accuracy: 0.9933 - val_loss: 0.2068 - val_accuracy: 0.9400 171/171 [==============================] - 0s 41us/step Optimizers: <keras.optimizers.Adam object at 0x10a1d9690> Epoch Sizes: 50 Neurons or Units: 64 ['loss', 'accuracy'] [0.10677705138747455, 0.988304078578949] Test score: 0.10677705138747455 Test accuracy: 0.988304078578949 Model: "sequential_47" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_139 (Dense) (None, 128) 3968 _________________________________________________________________ activation_139 (Activation) (None, 128) 0 _________________________________________________________________ dense_140 (Dense) (None, 128) 16512 _________________________________________________________________ activation_140 (Activation) (None, 128) 0 _________________________________________________________________ dense_141 (Dense) (None, 1) 129 _________________________________________________________________ activation_141 (Activation) (None, 1) 0 ================================================================= Total params: 20,609 Trainable params: 20,609 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/50 298/298 [==============================] - 0s 663us/step - loss: 1.9155 - accuracy: 0.8926 - val_loss: 1.4645 - val_accuracy: 0.9200 Epoch 2/50 298/298 [==============================] - 0s 62us/step - loss: 1.1249 - accuracy: 0.9732 - val_loss: 0.9544 - val_accuracy: 0.9100 Epoch 3/50 298/298 [==============================] - 0s 57us/step - loss: 0.6879 - accuracy: 0.9832 - val_loss: 0.6096 - val_accuracy: 0.9500 Epoch 4/50 298/298 [==============================] - 0s 60us/step - loss: 0.4421 - accuracy: 0.9899 - val_loss: 0.4430 - val_accuracy: 0.9500 Epoch 5/50 298/298 [==============================] - 0s 55us/step - loss: 0.3104 - accuracy: 0.9933 - val_loss: 0.3485 - val_accuracy: 0.9500 Epoch 6/50 298/298 [==============================] - 0s 58us/step - loss: 0.2375 - accuracy: 0.9933 - val_loss: 0.3111 - val_accuracy: 0.9500 Epoch 7/50 298/298 [==============================] - 0s 58us/step - loss: 0.1916 - accuracy: 0.9899 - val_loss: 0.2737 - val_accuracy: 0.9500 Epoch 8/50 298/298 [==============================] - 0s 63us/step - loss: 0.1626 - accuracy: 0.9899 - val_loss: 0.2536 - val_accuracy: 0.9500 Epoch 9/50 298/298 [==============================] - 0s 63us/step - loss: 0.1428 - accuracy: 0.9899 - val_loss: 0.2389 - val_accuracy: 0.9500 Epoch 10/50 298/298 [==============================] - 0s 56us/step - loss: 0.1324 - accuracy: 0.9899 - val_loss: 0.2320 - val_accuracy: 0.9400 Epoch 11/50 298/298 [==============================] - 0s 59us/step - loss: 0.1174 - accuracy: 0.9966 - val_loss: 0.2227 - val_accuracy: 0.9300 Epoch 12/50 298/298 [==============================] - 0s 52us/step - loss: 0.1092 - accuracy: 0.9899 - val_loss: 0.2194 - val_accuracy: 0.9500 Epoch 13/50 298/298 [==============================] - 0s 55us/step - loss: 0.1045 - accuracy: 0.9933 - val_loss: 0.1903 - val_accuracy: 0.9600 Epoch 14/50 298/298 [==============================] - 0s 53us/step - loss: 0.0993 - accuracy: 0.9933 - val_loss: 0.2053 - val_accuracy: 0.9500 Epoch 15/50 298/298 [==============================] - 0s 52us/step - loss: 0.0928 - accuracy: 0.9933 - val_loss: 0.1978 - val_accuracy: 0.9500 Epoch 16/50 298/298 [==============================] - 0s 54us/step - loss: 0.0905 - accuracy: 0.9933 - val_loss: 0.1913 - val_accuracy: 0.9400 Epoch 17/50 298/298 [==============================] - 0s 58us/step - loss: 0.0877 - accuracy: 0.9899 - val_loss: 0.2059 - val_accuracy: 0.9500 Epoch 18/50 298/298 [==============================] - 0s 52us/step - loss: 0.0840 - accuracy: 0.9966 - val_loss: 0.1911 - val_accuracy: 0.9600 Epoch 19/50 298/298 [==============================] - 0s 54us/step - loss: 0.0798 - accuracy: 0.9966 - val_loss: 0.1945 - val_accuracy: 0.9500 Epoch 20/50 298/298 [==============================] - 0s 49us/step - loss: 0.0858 - accuracy: 0.9899 - val_loss: 0.1975 - val_accuracy: 0.9500 Epoch 21/50 298/298 [==============================] - 0s 54us/step - loss: 0.0822 - accuracy: 0.9933 - val_loss: 0.1862 - val_accuracy: 0.9600 Epoch 22/50 298/298 [==============================] - 0s 50us/step - loss: 0.0788 - accuracy: 0.9933 - val_loss: 0.2094 - val_accuracy: 0.9500 Epoch 23/50 298/298 [==============================] - 0s 59us/step - loss: 0.0752 - accuracy: 0.9933 - val_loss: 0.1991 - val_accuracy: 0.9300 Epoch 24/50 298/298 [==============================] - 0s 58us/step - loss: 0.0754 - accuracy: 0.9966 - val_loss: 0.2112 - val_accuracy: 0.9500 Epoch 25/50 298/298 [==============================] - 0s 48us/step - loss: 0.0721 - accuracy: 0.9899 - val_loss: 0.2028 - val_accuracy: 0.9500 Epoch 26/50 298/298 [==============================] - 0s 55us/step - loss: 0.0719 - accuracy: 0.9933 - val_loss: 0.1854 - val_accuracy: 0.9500 Epoch 27/50 298/298 [==============================] - 0s 51us/step - loss: 0.0732 - accuracy: 0.9933 - val_loss: 0.1948 - val_accuracy: 0.9500 Epoch 28/50 298/298 [==============================] - 0s 58us/step - loss: 0.0692 - accuracy: 0.9933 - val_loss: 0.2083 - val_accuracy: 0.9400 Epoch 29/50 298/298 [==============================] - 0s 49us/step - loss: 0.0691 - accuracy: 0.9933 - val_loss: 0.1927 - val_accuracy: 0.9400 Epoch 30/50 298/298 [==============================] - 0s 56us/step - loss: 0.0664 - accuracy: 0.9933 - val_loss: 0.1919 - val_accuracy: 0.9500 Epoch 31/50 298/298 [==============================] - 0s 50us/step - loss: 0.0671 - accuracy: 0.9933 - val_loss: 0.1928 - val_accuracy: 0.9600 Epoch 32/50 298/298 [==============================] - 0s 52us/step - loss: 0.0653 - accuracy: 0.9933 - val_loss: 0.1974 - val_accuracy: 0.9400 Epoch 33/50 298/298 [==============================] - 0s 50us/step - loss: 0.0648 - accuracy: 0.9966 - val_loss: 0.2019 - val_accuracy: 0.9400 Epoch 34/50 298/298 [==============================] - 0s 61us/step - loss: 0.0695 - accuracy: 0.9933 - val_loss: 0.2037 - val_accuracy: 0.9500 Epoch 35/50 298/298 [==============================] - 0s 54us/step - loss: 0.0673 - accuracy: 0.9933 - val_loss: 0.2188 - val_accuracy: 0.9400 Epoch 36/50 298/298 [==============================] - 0s 58us/step - loss: 0.0767 - accuracy: 0.9866 - val_loss: 0.2006 - val_accuracy: 0.9400 Epoch 37/50 298/298 [==============================] - 0s 52us/step - loss: 0.0643 - accuracy: 0.9933 - val_loss: 0.1995 - val_accuracy: 0.9500 Epoch 38/50 298/298 [==============================] - 0s 59us/step - loss: 0.0651 - accuracy: 0.9966 - val_loss: 0.2084 - val_accuracy: 0.9500 Epoch 39/50 298/298 [==============================] - 0s 49us/step - loss: 0.0622 - accuracy: 0.9933 - val_loss: 0.2058 - val_accuracy: 0.9400 Epoch 40/50 298/298 [==============================] - 0s 56us/step - loss: 0.0630 - accuracy: 0.9933 - val_loss: 0.1809 - val_accuracy: 0.9600 Epoch 41/50 298/298 [==============================] - 0s 49us/step - loss: 0.0605 - accuracy: 0.9933 - val_loss: 0.2130 - val_accuracy: 0.9400 Epoch 42/50 298/298 [==============================] - 0s 57us/step - loss: 0.0600 - accuracy: 0.9933 - val_loss: 0.2089 - val_accuracy: 0.9500 Epoch 43/50 298/298 [==============================] - 0s 50us/step - loss: 0.0604 - accuracy: 0.9966 - val_loss: 0.1998 - val_accuracy: 0.9500 Epoch 44/50 298/298 [==============================] - 0s 60us/step - loss: 0.0595 - accuracy: 0.9933 - val_loss: 0.1990 - val_accuracy: 0.9400 Epoch 45/50 298/298 [==============================] - 0s 63us/step - loss: 0.0585 - accuracy: 0.9933 - val_loss: 0.2130 - val_accuracy: 0.9400 Epoch 46/50 298/298 [==============================] - 0s 50us/step - loss: 0.0601 - accuracy: 0.9899 - val_loss: 0.2038 - val_accuracy: 0.9400 Epoch 47/50 298/298 [==============================] - 0s 67us/step - loss: 0.0584 - accuracy: 0.9933 - val_loss: 0.1964 - val_accuracy: 0.9500 Epoch 48/50 298/298 [==============================] - 0s 53us/step - loss: 0.0573 - accuracy: 0.9933 - val_loss: 0.1997 - val_accuracy: 0.9400 Epoch 49/50 298/298 [==============================] - 0s 53us/step - loss: 0.0572 - accuracy: 0.9933 - val_loss: 0.1975 - val_accuracy: 0.9500 Epoch 50/50 298/298 [==============================] - 0s 57us/step - loss: 0.0609 - accuracy: 0.9933 - val_loss: 0.1907 - val_accuracy: 0.9500 171/171 [==============================] - 0s 33us/step Optimizers: <keras.optimizers.Adam object at 0x10a1d9690> Epoch Sizes: 50 Neurons or Units: 128 ['loss', 'accuracy'] [0.08644826819150768, 0.988304078578949] Test score: 0.08644826819150768 Test accuracy: 0.988304078578949 Model: "sequential_48" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_142 (Dense) (None, 256) 7936 _________________________________________________________________ activation_142 (Activation) (None, 256) 0 _________________________________________________________________ dense_143 (Dense) (None, 256) 65792 _________________________________________________________________ activation_143 (Activation) (None, 256) 0 _________________________________________________________________ dense_144 (Dense) (None, 1) 257 _________________________________________________________________ activation_144 (Activation) (None, 1) 0 ================================================================= Total params: 73,985 Trainable params: 73,985 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/50 298/298 [==============================] - 0s 741us/step - loss: 2.6545 - accuracy: 0.9295 - val_loss: 1.6411 - val_accuracy: 0.9400 Epoch 2/50 298/298 [==============================] - 0s 78us/step - loss: 1.0131 - accuracy: 0.9664 - val_loss: 0.6235 - val_accuracy: 0.9500 Epoch 3/50 298/298 [==============================] - 0s 76us/step - loss: 0.4228 - accuracy: 0.9899 - val_loss: 0.4087 - val_accuracy: 0.9500 Epoch 4/50 298/298 [==============================] - 0s 70us/step - loss: 0.2503 - accuracy: 0.9899 - val_loss: 0.3069 - val_accuracy: 0.9400 Epoch 5/50 298/298 [==============================] - 0s 78us/step - loss: 0.1734 - accuracy: 0.9933 - val_loss: 0.2449 - val_accuracy: 0.9500 Epoch 6/50 298/298 [==============================] - 0s 78us/step - loss: 0.1315 - accuracy: 0.9933 - val_loss: 0.2040 - val_accuracy: 0.9600 Epoch 7/50 298/298 [==============================] - 0s 68us/step - loss: 0.1201 - accuracy: 0.9899 - val_loss: 0.2198 - val_accuracy: 0.9400 Epoch 8/50 298/298 [==============================] - 0s 73us/step - loss: 0.1015 - accuracy: 0.9899 - val_loss: 0.1915 - val_accuracy: 0.9500 Epoch 9/50 298/298 [==============================] - 0s 71us/step - loss: 0.0949 - accuracy: 0.9966 - val_loss: 0.2041 - val_accuracy: 0.9500 Epoch 10/50 298/298 [==============================] - 0s 73us/step - loss: 0.0915 - accuracy: 0.9899 - val_loss: 0.1990 - val_accuracy: 0.9500 Epoch 11/50 298/298 [==============================] - 0s 75us/step - loss: 0.0825 - accuracy: 0.9899 - val_loss: 0.1842 - val_accuracy: 0.9500 Epoch 12/50 298/298 [==============================] - 0s 77us/step - loss: 0.0798 - accuracy: 0.9966 - val_loss: 0.2052 - val_accuracy: 0.9500 Epoch 13/50 298/298 [==============================] - 0s 82us/step - loss: 0.0783 - accuracy: 0.9899 - val_loss: 0.1990 - val_accuracy: 0.9400 Epoch 14/50 298/298 [==============================] - 0s 70us/step - loss: 0.0739 - accuracy: 0.9933 - val_loss: 0.2028 - val_accuracy: 0.9400 Epoch 15/50 298/298 [==============================] - 0s 81us/step - loss: 0.0729 - accuracy: 0.9933 - val_loss: 0.2046 - val_accuracy: 0.9300 Epoch 16/50 298/298 [==============================] - 0s 84us/step - loss: 0.0843 - accuracy: 0.9899 - val_loss: 0.1778 - val_accuracy: 0.9600 Epoch 17/50 298/298 [==============================] - 0s 74us/step - loss: 0.0733 - accuracy: 0.9933 - val_loss: 0.1717 - val_accuracy: 0.9600 Epoch 18/50 298/298 [==============================] - 0s 91us/step - loss: 0.0713 - accuracy: 0.9899 - val_loss: 0.2311 - val_accuracy: 0.9400 Epoch 19/50 298/298 [==============================] - 0s 83us/step - loss: 0.0714 - accuracy: 0.9866 - val_loss: 0.2113 - val_accuracy: 0.9500 Epoch 20/50 298/298 [==============================] - 0s 78us/step - loss: 0.0653 - accuracy: 0.9933 - val_loss: 0.1734 - val_accuracy: 0.9500 Epoch 21/50 298/298 [==============================] - 0s 81us/step - loss: 0.0651 - accuracy: 0.9933 - val_loss: 0.2017 - val_accuracy: 0.9400 Epoch 22/50 298/298 [==============================] - 0s 68us/step - loss: 0.0664 - accuracy: 0.9899 - val_loss: 0.2090 - val_accuracy: 0.9400 Epoch 23/50 298/298 [==============================] - 0s 90us/step - loss: 0.0633 - accuracy: 0.9933 - val_loss: 0.1951 - val_accuracy: 0.9500 Epoch 24/50 298/298 [==============================] - 0s 88us/step - loss: 0.0654 - accuracy: 0.9933 - val_loss: 0.2096 - val_accuracy: 0.9400 Epoch 25/50 298/298 [==============================] - 0s 91us/step - loss: 0.0652 - accuracy: 0.9933 - val_loss: 0.2050 - val_accuracy: 0.9400 Epoch 26/50 298/298 [==============================] - 0s 76us/step - loss: 0.0633 - accuracy: 0.9899 - val_loss: 0.2090 - val_accuracy: 0.9400 Epoch 27/50 298/298 [==============================] - 0s 81us/step - loss: 0.0612 - accuracy: 0.9933 - val_loss: 0.1986 - val_accuracy: 0.9400 Epoch 28/50 298/298 [==============================] - 0s 86us/step - loss: 0.0582 - accuracy: 0.9933 - val_loss: 0.2142 - val_accuracy: 0.9400 Epoch 29/50 298/298 [==============================] - 0s 88us/step - loss: 0.0595 - accuracy: 0.9933 - val_loss: 0.1845 - val_accuracy: 0.9400 Epoch 30/50 298/298 [==============================] - 0s 92us/step - loss: 0.0595 - accuracy: 0.9933 - val_loss: 0.1872 - val_accuracy: 0.9600 Epoch 31/50 298/298 [==============================] - 0s 79us/step - loss: 0.0580 - accuracy: 0.9933 - val_loss: 0.1915 - val_accuracy: 0.9500 Epoch 32/50 298/298 [==============================] - 0s 82us/step - loss: 0.0559 - accuracy: 0.9933 - val_loss: 0.1910 - val_accuracy: 0.9600 Epoch 33/50 298/298 [==============================] - 0s 90us/step - loss: 0.0550 - accuracy: 0.9966 - val_loss: 0.1924 - val_accuracy: 0.9500 Epoch 34/50 298/298 [==============================] - 0s 96us/step - loss: 0.0556 - accuracy: 0.9933 - val_loss: 0.1878 - val_accuracy: 0.9500 Epoch 35/50 298/298 [==============================] - 0s 94us/step - loss: 0.0554 - accuracy: 0.9933 - val_loss: 0.1990 - val_accuracy: 0.9400 Epoch 36/50 298/298 [==============================] - 0s 98us/step - loss: 0.0602 - accuracy: 0.9933 - val_loss: 0.1976 - val_accuracy: 0.9600 Epoch 37/50 298/298 [==============================] - 0s 108us/step - loss: 0.0710 - accuracy: 0.9899 - val_loss: 0.2793 - val_accuracy: 0.9400 Epoch 38/50 298/298 [==============================] - 0s 89us/step - loss: 0.0612 - accuracy: 0.9933 - val_loss: 0.2377 - val_accuracy: 0.9300 Epoch 39/50 298/298 [==============================] - 0s 84us/step - loss: 0.0555 - accuracy: 0.9966 - val_loss: 0.1954 - val_accuracy: 0.9500 Epoch 40/50 298/298 [==============================] - 0s 101us/step - loss: 0.0535 - accuracy: 0.9933 - val_loss: 0.1866 - val_accuracy: 0.9400 Epoch 41/50 298/298 [==============================] - 0s 91us/step - loss: 0.0534 - accuracy: 0.9933 - val_loss: 0.1876 - val_accuracy: 0.9500 Epoch 42/50 298/298 [==============================] - 0s 75us/step - loss: 0.0522 - accuracy: 0.9966 - val_loss: 0.2068 - val_accuracy: 0.9400 Epoch 43/50 298/298 [==============================] - 0s 90us/step - loss: 0.0517 - accuracy: 0.9966 - val_loss: 0.2019 - val_accuracy: 0.9500 Epoch 44/50 298/298 [==============================] - 0s 93us/step - loss: 0.0513 - accuracy: 0.9933 - val_loss: 0.2090 - val_accuracy: 0.9400 Epoch 45/50 298/298 [==============================] - 0s 92us/step - loss: 0.0522 - accuracy: 0.9966 - val_loss: 0.1934 - val_accuracy: 0.9500 Epoch 46/50 298/298 [==============================] - 0s 93us/step - loss: 0.0639 - accuracy: 0.9866 - val_loss: 0.1824 - val_accuracy: 0.9500 Epoch 47/50 298/298 [==============================] - 0s 93us/step - loss: 0.0551 - accuracy: 0.9933 - val_loss: 0.1884 - val_accuracy: 0.9500 Epoch 48/50 298/298 [==============================] - 0s 77us/step - loss: 0.0519 - accuracy: 0.9966 - val_loss: 0.2261 - val_accuracy: 0.9500 Epoch 49/50 298/298 [==============================] - 0s 88us/step - loss: 0.0566 - accuracy: 0.9899 - val_loss: 0.1884 - val_accuracy: 0.9400 Epoch 50/50 298/298 [==============================] - 0s 94us/step - loss: 0.0507 - accuracy: 0.9966 - val_loss: 0.2099 - val_accuracy: 0.9500 171/171 [==============================] - 0s 36us/step Optimizers: <keras.optimizers.Adam object at 0x10a1d9690> Epoch Sizes: 50 Neurons or Units: 256 ['loss', 'accuracy'] [0.0890013293216103, 0.988304078578949] Test score: 0.0890013293216103 Test accuracy: 0.988304078578949 Model: "sequential_49" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_145 (Dense) (None, 64) 1984 _________________________________________________________________ activation_145 (Activation) (None, 64) 0 _________________________________________________________________ dense_146 (Dense) (None, 64) 4160 _________________________________________________________________ activation_146 (Activation) (None, 64) 0 _________________________________________________________________ dense_147 (Dense) (None, 1) 65 _________________________________________________________________ activation_147 (Activation) (None, 1) 0 ================================================================= Total params: 6,209 Trainable params: 6,209 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/100 298/298 [==============================] - 0s 713us/step - loss: 1.3360 - accuracy: 0.8758 - val_loss: 1.0611 - val_accuracy: 0.9100 Epoch 2/100 298/298 [==============================] - 0s 57us/step - loss: 0.8118 - accuracy: 0.9732 - val_loss: 0.7685 - val_accuracy: 0.9300 Epoch 3/100 298/298 [==============================] - 0s 51us/step - loss: 0.5483 - accuracy: 0.9799 - val_loss: 0.5434 - val_accuracy: 0.9300 Epoch 4/100 298/298 [==============================] - 0s 58us/step - loss: 0.3821 - accuracy: 0.9899 - val_loss: 0.4247 - val_accuracy: 0.9300 Epoch 5/100 298/298 [==============================] - 0s 56us/step - loss: 0.2828 - accuracy: 0.9933 - val_loss: 0.3633 - val_accuracy: 0.9300 Epoch 6/100 298/298 [==============================] - 0s 48us/step - loss: 0.2226 - accuracy: 0.9933 - val_loss: 0.2934 - val_accuracy: 0.9400 Epoch 7/100 298/298 [==============================] - 0s 53us/step - loss: 0.1841 - accuracy: 0.9966 - val_loss: 0.2678 - val_accuracy: 0.9400 Epoch 8/100 298/298 [==============================] - 0s 47us/step - loss: 0.1576 - accuracy: 0.9899 - val_loss: 0.2589 - val_accuracy: 0.9400 Epoch 9/100 298/298 [==============================] - 0s 49us/step - loss: 0.1397 - accuracy: 0.9933 - val_loss: 0.2280 - val_accuracy: 0.9600 Epoch 10/100 298/298 [==============================] - 0s 50us/step - loss: 0.1265 - accuracy: 0.9933 - val_loss: 0.2266 - val_accuracy: 0.9400 Epoch 11/100 298/298 [==============================] - 0s 47us/step - loss: 0.1189 - accuracy: 0.9933 - val_loss: 0.2217 - val_accuracy: 0.9400 Epoch 12/100 298/298 [==============================] - 0s 51us/step - loss: 0.1081 - accuracy: 0.9933 - val_loss: 0.2128 - val_accuracy: 0.9400 Epoch 13/100 298/298 [==============================] - 0s 49us/step - loss: 0.1028 - accuracy: 0.9966 - val_loss: 0.2018 - val_accuracy: 0.9500 Epoch 14/100 298/298 [==============================] - 0s 57us/step - loss: 0.1009 - accuracy: 0.9933 - val_loss: 0.2289 - val_accuracy: 0.9400 Epoch 15/100 298/298 [==============================] - 0s 50us/step - loss: 0.0960 - accuracy: 0.9899 - val_loss: 0.2325 - val_accuracy: 0.9400 Epoch 16/100 298/298 [==============================] - 0s 58us/step - loss: 0.0917 - accuracy: 0.9933 - val_loss: 0.2229 - val_accuracy: 0.9400 Epoch 17/100 298/298 [==============================] - 0s 57us/step - loss: 0.0872 - accuracy: 0.9933 - val_loss: 0.2006 - val_accuracy: 0.9400 Epoch 18/100 298/298 [==============================] - 0s 59us/step - loss: 0.0845 - accuracy: 0.9966 - val_loss: 0.1959 - val_accuracy: 0.9400 Epoch 19/100 298/298 [==============================] - 0s 53us/step - loss: 0.0836 - accuracy: 0.9899 - val_loss: 0.2100 - val_accuracy: 0.9500 Epoch 20/100 298/298 [==============================] - 0s 47us/step - loss: 0.0799 - accuracy: 0.9966 - val_loss: 0.1893 - val_accuracy: 0.9600 Epoch 21/100 298/298 [==============================] - 0s 54us/step - loss: 0.0802 - accuracy: 0.9966 - val_loss: 0.2045 - val_accuracy: 0.9400 Epoch 22/100 298/298 [==============================] - 0s 48us/step - loss: 0.0775 - accuracy: 0.9933 - val_loss: 0.1956 - val_accuracy: 0.9400 Epoch 23/100 298/298 [==============================] - 0s 51us/step - loss: 0.0769 - accuracy: 0.9933 - val_loss: 0.1865 - val_accuracy: 0.9400 Epoch 24/100 298/298 [==============================] - 0s 49us/step - loss: 0.0755 - accuracy: 0.9933 - val_loss: 0.1938 - val_accuracy: 0.9400 Epoch 25/100 298/298 [==============================] - 0s 52us/step - loss: 0.0753 - accuracy: 0.9933 - val_loss: 0.2073 - val_accuracy: 0.9500 Epoch 26/100 298/298 [==============================] - 0s 53us/step - loss: 0.0730 - accuracy: 0.9966 - val_loss: 0.1969 - val_accuracy: 0.9400 Epoch 27/100 298/298 [==============================] - 0s 50us/step - loss: 0.0731 - accuracy: 0.9933 - val_loss: 0.1941 - val_accuracy: 0.9400 Epoch 28/100 298/298 [==============================] - 0s 56us/step - loss: 0.0696 - accuracy: 0.9933 - val_loss: 0.1946 - val_accuracy: 0.9400 Epoch 29/100 298/298 [==============================] - 0s 58us/step - loss: 0.0717 - accuracy: 0.9933 - val_loss: 0.1980 - val_accuracy: 0.9400 Epoch 30/100 298/298 [==============================] - 0s 48us/step - loss: 0.0712 - accuracy: 0.9899 - val_loss: 0.1973 - val_accuracy: 0.9500 Epoch 31/100 298/298 [==============================] - 0s 55us/step - loss: 0.0683 - accuracy: 0.9966 - val_loss: 0.1979 - val_accuracy: 0.9400 Epoch 32/100 298/298 [==============================] - 0s 47us/step - loss: 0.0673 - accuracy: 0.9933 - val_loss: 0.1912 - val_accuracy: 0.9400 Epoch 33/100 298/298 [==============================] - 0s 52us/step - loss: 0.0688 - accuracy: 0.9933 - val_loss: 0.1916 - val_accuracy: 0.9400 Epoch 34/100 298/298 [==============================] - 0s 48us/step - loss: 0.0661 - accuracy: 0.9966 - val_loss: 0.2078 - val_accuracy: 0.9500 Epoch 35/100 298/298 [==============================] - 0s 48us/step - loss: 0.0662 - accuracy: 0.9933 - val_loss: 0.2058 - val_accuracy: 0.9400 Epoch 36/100 298/298 [==============================] - 0s 48us/step - loss: 0.0653 - accuracy: 0.9933 - val_loss: 0.1968 - val_accuracy: 0.9600 Epoch 37/100 298/298 [==============================] - 0s 51us/step - loss: 0.0638 - accuracy: 0.9933 - val_loss: 0.1906 - val_accuracy: 0.9500 Epoch 38/100 298/298 [==============================] - 0s 51us/step - loss: 0.0654 - accuracy: 0.9966 - val_loss: 0.1978 - val_accuracy: 0.9400 Epoch 39/100 298/298 [==============================] - 0s 51us/step - loss: 0.0658 - accuracy: 0.9966 - val_loss: 0.2162 - val_accuracy: 0.9500 Epoch 40/100 298/298 [==============================] - 0s 52us/step - loss: 0.0631 - accuracy: 0.9933 - val_loss: 0.2132 - val_accuracy: 0.9500 Epoch 41/100 298/298 [==============================] - 0s 55us/step - loss: 0.0622 - accuracy: 0.9933 - val_loss: 0.1978 - val_accuracy: 0.9500 Epoch 42/100 298/298 [==============================] - 0s 47us/step - loss: 0.0613 - accuracy: 0.9933 - val_loss: 0.1967 - val_accuracy: 0.9500 Epoch 43/100 298/298 [==============================] - 0s 55us/step - loss: 0.0611 - accuracy: 0.9933 - val_loss: 0.1885 - val_accuracy: 0.9500 Epoch 44/100 298/298 [==============================] - 0s 48us/step - loss: 0.0609 - accuracy: 0.9966 - val_loss: 0.1911 - val_accuracy: 0.9500 Epoch 45/100 298/298 [==============================] - 0s 52us/step - loss: 0.0602 - accuracy: 0.9933 - val_loss: 0.2017 - val_accuracy: 0.9400 Epoch 46/100 298/298 [==============================] - 0s 53us/step - loss: 0.0596 - accuracy: 0.9933 - val_loss: 0.1976 - val_accuracy: 0.9400 Epoch 47/100 298/298 [==============================] - 0s 55us/step - loss: 0.0595 - accuracy: 0.9966 - val_loss: 0.1917 - val_accuracy: 0.9400 Epoch 48/100 298/298 [==============================] - 0s 52us/step - loss: 0.0600 - accuracy: 0.9933 - val_loss: 0.1913 - val_accuracy: 0.9500 Epoch 49/100 298/298 [==============================] - 0s 57us/step - loss: 0.0603 - accuracy: 0.9966 - val_loss: 0.2001 - val_accuracy: 0.9400 Epoch 50/100 298/298 [==============================] - 0s 47us/step - loss: 0.0593 - accuracy: 0.9933 - val_loss: 0.2064 - val_accuracy: 0.9500 Epoch 51/100 298/298 [==============================] - 0s 60us/step - loss: 0.0577 - accuracy: 0.9933 - val_loss: 0.2002 - val_accuracy: 0.9300 Epoch 52/100 298/298 [==============================] - 0s 49us/step - loss: 0.0579 - accuracy: 0.9966 - val_loss: 0.1979 - val_accuracy: 0.9500 Epoch 53/100 298/298 [==============================] - 0s 59us/step - loss: 0.0573 - accuracy: 0.9933 - val_loss: 0.1894 - val_accuracy: 0.9500 Epoch 54/100 298/298 [==============================] - 0s 46us/step - loss: 0.0569 - accuracy: 0.9933 - val_loss: 0.2056 - val_accuracy: 0.9500 Epoch 55/100 298/298 [==============================] - 0s 56us/step - loss: 0.0568 - accuracy: 0.9966 - val_loss: 0.1969 - val_accuracy: 0.9400 Epoch 56/100 298/298 [==============================] - 0s 47us/step - loss: 0.0576 - accuracy: 0.9933 - val_loss: 0.1998 - val_accuracy: 0.9400 Epoch 57/100 298/298 [==============================] - 0s 58us/step - loss: 0.0564 - accuracy: 0.9933 - val_loss: 0.2007 - val_accuracy: 0.9400 Epoch 58/100 298/298 [==============================] - 0s 51us/step - loss: 0.0584 - accuracy: 0.9966 - val_loss: 0.1765 - val_accuracy: 0.9600 Epoch 59/100 298/298 [==============================] - 0s 55us/step - loss: 0.0667 - accuracy: 0.9899 - val_loss: 0.1959 - val_accuracy: 0.9500 Epoch 60/100 298/298 [==============================] - 0s 52us/step - loss: 0.0559 - accuracy: 0.9966 - val_loss: 0.2073 - val_accuracy: 0.9400 Epoch 61/100 298/298 [==============================] - 0s 49us/step - loss: 0.0572 - accuracy: 0.9966 - val_loss: 0.2149 - val_accuracy: 0.9300 Epoch 62/100 298/298 [==============================] - 0s 46us/step - loss: 0.0556 - accuracy: 0.9933 - val_loss: 0.2002 - val_accuracy: 0.9500 Epoch 63/100 298/298 [==============================] - 0s 51us/step - loss: 0.0548 - accuracy: 0.9933 - val_loss: 0.1940 - val_accuracy: 0.9400 Epoch 64/100 298/298 [==============================] - 0s 45us/step - loss: 0.0561 - accuracy: 0.9933 - val_loss: 0.1964 - val_accuracy: 0.9500 Epoch 65/100 298/298 [==============================] - 0s 57us/step - loss: 0.0549 - accuracy: 0.9966 - val_loss: 0.1972 - val_accuracy: 0.9400 Epoch 66/100 298/298 [==============================] - 0s 47us/step - loss: 0.0547 - accuracy: 0.9933 - val_loss: 0.1956 - val_accuracy: 0.9500 Epoch 67/100 298/298 [==============================] - 0s 54us/step - loss: 0.0552 - accuracy: 0.9933 - val_loss: 0.1883 - val_accuracy: 0.9400 Epoch 68/100 298/298 [==============================] - 0s 48us/step - loss: 0.0541 - accuracy: 0.9966 - val_loss: 0.2012 - val_accuracy: 0.9500 Epoch 69/100 298/298 [==============================] - 0s 53us/step - loss: 0.0534 - accuracy: 0.9933 - val_loss: 0.1985 - val_accuracy: 0.9600 Epoch 70/100 298/298 [==============================] - 0s 45us/step - loss: 0.0537 - accuracy: 0.9933 - val_loss: 0.2019 - val_accuracy: 0.9400 Epoch 71/100 298/298 [==============================] - 0s 48us/step - loss: 0.0523 - accuracy: 0.9966 - val_loss: 0.1917 - val_accuracy: 0.9500 Epoch 72/100 298/298 [==============================] - 0s 45us/step - loss: 0.0536 - accuracy: 0.9966 - val_loss: 0.1959 - val_accuracy: 0.9500 Epoch 73/100 298/298 [==============================] - 0s 53us/step - loss: 0.0547 - accuracy: 0.9933 - val_loss: 0.2179 - val_accuracy: 0.9500 Epoch 74/100 298/298 [==============================] - 0s 47us/step - loss: 0.0541 - accuracy: 0.9933 - val_loss: 0.2052 - val_accuracy: 0.9400 Epoch 75/100 298/298 [==============================] - 0s 55us/step - loss: 0.0524 - accuracy: 0.9966 - val_loss: 0.2110 - val_accuracy: 0.9400 Epoch 76/100 298/298 [==============================] - 0s 48us/step - loss: 0.0537 - accuracy: 0.9933 - val_loss: 0.2048 - val_accuracy: 0.9500 Epoch 77/100 298/298 [==============================] - 0s 56us/step - loss: 0.0517 - accuracy: 0.9933 - val_loss: 0.2037 - val_accuracy: 0.9300 Epoch 78/100 298/298 [==============================] - 0s 45us/step - loss: 0.0531 - accuracy: 0.9966 - val_loss: 0.1797 - val_accuracy: 0.9500 Epoch 79/100 298/298 [==============================] - 0s 51us/step - loss: 0.0518 - accuracy: 0.9966 - val_loss: 0.1973 - val_accuracy: 0.9600 Epoch 80/100 298/298 [==============================] - 0s 50us/step - loss: 0.0524 - accuracy: 0.9933 - val_loss: 0.1940 - val_accuracy: 0.9400 Epoch 81/100 298/298 [==============================] - 0s 55us/step - loss: 0.0523 - accuracy: 0.9966 - val_loss: 0.1952 - val_accuracy: 0.9400 Epoch 82/100 298/298 [==============================] - 0s 47us/step - loss: 0.0577 - accuracy: 0.9899 - val_loss: 0.2022 - val_accuracy: 0.9400 Epoch 83/100 298/298 [==============================] - 0s 53us/step - loss: 0.0508 - accuracy: 0.9966 - val_loss: 0.1905 - val_accuracy: 0.9500 Epoch 84/100 298/298 [==============================] - 0s 48us/step - loss: 0.0530 - accuracy: 0.9966 - val_loss: 0.2047 - val_accuracy: 0.9500 Epoch 85/100 298/298 [==============================] - 0s 52us/step - loss: 0.0515 - accuracy: 0.9966 - val_loss: 0.1984 - val_accuracy: 0.9400 Epoch 86/100 298/298 [==============================] - 0s 46us/step - loss: 0.0512 - accuracy: 0.9933 - val_loss: 0.1936 - val_accuracy: 0.9400 Epoch 87/100 298/298 [==============================] - 0s 50us/step - loss: 0.0518 - accuracy: 0.9966 - val_loss: 0.1949 - val_accuracy: 0.9400 Epoch 88/100 298/298 [==============================] - 0s 52us/step - loss: 0.0497 - accuracy: 1.0000 - val_loss: 0.2022 - val_accuracy: 0.9500 Epoch 89/100 298/298 [==============================] - 0s 47us/step - loss: 0.0512 - accuracy: 0.9933 - val_loss: 0.1960 - val_accuracy: 0.9600 Epoch 90/100 298/298 [==============================] - 0s 52us/step - loss: 0.0505 - accuracy: 0.9966 - val_loss: 0.1899 - val_accuracy: 0.9400 Epoch 91/100 298/298 [==============================] - 0s 49us/step - loss: 0.0499 - accuracy: 0.9966 - val_loss: 0.2115 - val_accuracy: 0.9500 Epoch 92/100 298/298 [==============================] - 0s 57us/step - loss: 0.0496 - accuracy: 0.9933 - val_loss: 0.2053 - val_accuracy: 0.9400 Epoch 93/100 298/298 [==============================] - 0s 57us/step - loss: 0.0494 - accuracy: 0.9966 - val_loss: 0.1929 - val_accuracy: 0.9400 Epoch 94/100 298/298 [==============================] - 0s 49us/step - loss: 0.0512 - accuracy: 0.9966 - val_loss: 0.1897 - val_accuracy: 0.9500 Epoch 95/100 298/298 [==============================] - 0s 54us/step - loss: 0.0504 - accuracy: 0.9966 - val_loss: 0.1988 - val_accuracy: 0.9400 Epoch 96/100 298/298 [==============================] - 0s 45us/step - loss: 0.0494 - accuracy: 0.9966 - val_loss: 0.2127 - val_accuracy: 0.9400 Epoch 97/100 298/298 [==============================] - 0s 53us/step - loss: 0.0485 - accuracy: 1.0000 - val_loss: 0.2094 - val_accuracy: 0.9500 Epoch 98/100 298/298 [==============================] - 0s 48us/step - loss: 0.0486 - accuracy: 1.0000 - val_loss: 0.2077 - val_accuracy: 0.9500 Epoch 99/100 298/298 [==============================] - 0s 52us/step - loss: 0.0488 - accuracy: 0.9966 - val_loss: 0.1978 - val_accuracy: 0.9500 Epoch 100/100 298/298 [==============================] - 0s 50us/step - loss: 0.0483 - accuracy: 0.9966 - val_loss: 0.2002 - val_accuracy: 0.9500 171/171 [==============================] - 0s 23us/step Optimizers: <keras.optimizers.Adam object at 0x10a1d9690> Epoch Sizes: 100 Neurons or Units: 64 ['loss', 'accuracy'] [0.08371611401351572, 0.988304078578949] Test score: 0.08371611401351572 Test accuracy: 0.988304078578949 Model: "sequential_50" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_148 (Dense) (None, 128) 3968 _________________________________________________________________ activation_148 (Activation) (None, 128) 0 _________________________________________________________________ dense_149 (Dense) (None, 128) 16512 _________________________________________________________________ activation_149 (Activation) (None, 128) 0 _________________________________________________________________ dense_150 (Dense) (None, 1) 129 _________________________________________________________________ activation_150 (Activation) (None, 1) 0 ================================================================= Total params: 20,609 Trainable params: 20,609 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/100 298/298 [==============================] - 0s 684us/step - loss: 1.8129 - accuracy: 0.8792 - val_loss: 1.2738 - val_accuracy: 0.9400 Epoch 2/100 298/298 [==============================] - 0s 58us/step - loss: 0.9179 - accuracy: 0.9799 - val_loss: 0.6812 - val_accuracy: 0.9500 Epoch 3/100 298/298 [==============================] - 0s 55us/step - loss: 0.4895 - accuracy: 0.9933 - val_loss: 0.4639 - val_accuracy: 0.9500 Epoch 4/100 298/298 [==============================] - 0s 57us/step - loss: 0.2992 - accuracy: 0.9966 - val_loss: 0.3391 - val_accuracy: 0.9400 Epoch 5/100 298/298 [==============================] - 0s 52us/step - loss: 0.2131 - accuracy: 0.9933 - val_loss: 0.2743 - val_accuracy: 0.9400 Epoch 6/100 298/298 [==============================] - 0s 55us/step - loss: 0.1637 - accuracy: 0.9899 - val_loss: 0.2496 - val_accuracy: 0.9500 Epoch 7/100 298/298 [==============================] - 0s 55us/step - loss: 0.1362 - accuracy: 0.9933 - val_loss: 0.2296 - val_accuracy: 0.9400 Epoch 8/100 298/298 [==============================] - 0s 57us/step - loss: 0.1242 - accuracy: 0.9933 - val_loss: 0.2366 - val_accuracy: 0.9300 Epoch 9/100 298/298 [==============================] - 0s 60us/step - loss: 0.1097 - accuracy: 0.9933 - val_loss: 0.1980 - val_accuracy: 0.9700 Epoch 10/100 298/298 [==============================] - 0s 60us/step - loss: 0.0990 - accuracy: 0.9966 - val_loss: 0.2101 - val_accuracy: 0.9500 Epoch 11/100 298/298 [==============================] - 0s 50us/step - loss: 0.0938 - accuracy: 0.9966 - val_loss: 0.2078 - val_accuracy: 0.9300 Epoch 12/100 298/298 [==============================] - 0s 57us/step - loss: 0.0884 - accuracy: 0.9899 - val_loss: 0.2017 - val_accuracy: 0.9500 Epoch 13/100 298/298 [==============================] - 0s 50us/step - loss: 0.0842 - accuracy: 0.9933 - val_loss: 0.2032 - val_accuracy: 0.9400 Epoch 14/100 298/298 [==============================] - 0s 57us/step - loss: 0.0858 - accuracy: 0.9966 - val_loss: 0.1676 - val_accuracy: 0.9600 Epoch 15/100 298/298 [==============================] - 0s 46us/step - loss: 0.0850 - accuracy: 0.9933 - val_loss: 0.2006 - val_accuracy: 0.9500 Epoch 16/100 298/298 [==============================] - 0s 54us/step - loss: 0.0821 - accuracy: 0.9899 - val_loss: 0.2311 - val_accuracy: 0.9400 Epoch 17/100 298/298 [==============================] - 0s 51us/step - loss: 0.0769 - accuracy: 0.9899 - val_loss: 0.1926 - val_accuracy: 0.9400 Epoch 18/100 298/298 [==============================] - 0s 50us/step - loss: 0.0740 - accuracy: 0.9933 - val_loss: 0.1836 - val_accuracy: 0.9500 Epoch 19/100 298/298 [==============================] - 0s 48us/step - loss: 0.0720 - accuracy: 0.9966 - val_loss: 0.1893 - val_accuracy: 0.9600 Epoch 20/100 298/298 [==============================] - 0s 57us/step - loss: 0.0755 - accuracy: 0.9899 - val_loss: 0.1948 - val_accuracy: 0.9600 Epoch 21/100 298/298 [==============================] - 0s 50us/step - loss: 0.0692 - accuracy: 0.9966 - val_loss: 0.2034 - val_accuracy: 0.9400 Epoch 22/100 298/298 [==============================] - 0s 60us/step - loss: 0.0701 - accuracy: 0.9899 - val_loss: 0.2015 - val_accuracy: 0.9400 Epoch 23/100 298/298 [==============================] - 0s 50us/step - loss: 0.0687 - accuracy: 0.9899 - val_loss: 0.1814 - val_accuracy: 0.9500 Epoch 24/100 298/298 [==============================] - 0s 59us/step - loss: 0.0661 - accuracy: 0.9899 - val_loss: 0.2034 - val_accuracy: 0.9400 Epoch 25/100 298/298 [==============================] - 0s 49us/step - loss: 0.0711 - accuracy: 0.9899 - val_loss: 0.1769 - val_accuracy: 0.9500 Epoch 26/100 298/298 [==============================] - 0s 55us/step - loss: 0.0648 - accuracy: 0.9933 - val_loss: 0.1955 - val_accuracy: 0.9500 Epoch 27/100 298/298 [==============================] - 0s 55us/step - loss: 0.0630 - accuracy: 0.9899 - val_loss: 0.2009 - val_accuracy: 0.9400 Epoch 28/100 298/298 [==============================] - 0s 49us/step - loss: 0.0621 - accuracy: 0.9933 - val_loss: 0.1802 - val_accuracy: 0.9500 Epoch 29/100 298/298 [==============================] - 0s 55us/step - loss: 0.0604 - accuracy: 0.9933 - val_loss: 0.1935 - val_accuracy: 0.9400 Epoch 30/100 298/298 [==============================] - 0s 49us/step - loss: 0.0596 - accuracy: 0.9933 - val_loss: 0.2032 - val_accuracy: 0.9400 Epoch 31/100 298/298 [==============================] - 0s 57us/step - loss: 0.0602 - accuracy: 0.9966 - val_loss: 0.2034 - val_accuracy: 0.9400 Epoch 32/100 298/298 [==============================] - 0s 47us/step - loss: 0.0617 - accuracy: 0.9933 - val_loss: 0.1785 - val_accuracy: 0.9500 Epoch 33/100 298/298 [==============================] - 0s 54us/step - loss: 0.0612 - accuracy: 0.9933 - val_loss: 0.2003 - val_accuracy: 0.9400 Epoch 34/100 298/298 [==============================] - 0s 50us/step - loss: 0.0600 - accuracy: 0.9933 - val_loss: 0.1880 - val_accuracy: 0.9400 Epoch 35/100 298/298 [==============================] - 0s 52us/step - loss: 0.0683 - accuracy: 0.9866 - val_loss: 0.1809 - val_accuracy: 0.9500 Epoch 36/100 298/298 [==============================] - 0s 50us/step - loss: 0.0602 - accuracy: 0.9933 - val_loss: 0.1962 - val_accuracy: 0.9500 Epoch 37/100 298/298 [==============================] - 0s 50us/step - loss: 0.0589 - accuracy: 0.9933 - val_loss: 0.2014 - val_accuracy: 0.9400 Epoch 38/100 298/298 [==============================] - 0s 61us/step - loss: 0.0595 - accuracy: 0.9933 - val_loss: 0.2021 - val_accuracy: 0.9400 Epoch 39/100 298/298 [==============================] - 0s 61us/step - loss: 0.0581 - accuracy: 0.9966 - val_loss: 0.1883 - val_accuracy: 0.9500 Epoch 40/100 298/298 [==============================] - 0s 50us/step - loss: 0.0575 - accuracy: 0.9933 - val_loss: 0.1816 - val_accuracy: 0.9400 Epoch 41/100 298/298 [==============================] - 0s 57us/step - loss: 0.0554 - accuracy: 0.9966 - val_loss: 0.2012 - val_accuracy: 0.9400 Epoch 42/100 298/298 [==============================] - 0s 49us/step - loss: 0.0577 - accuracy: 0.9933 - val_loss: 0.2192 - val_accuracy: 0.9500 Epoch 43/100 298/298 [==============================] - 0s 58us/step - loss: 0.0598 - accuracy: 0.9933 - val_loss: 0.1795 - val_accuracy: 0.9500 Epoch 44/100 298/298 [==============================] - 0s 49us/step - loss: 0.0587 - accuracy: 0.9966 - val_loss: 0.2065 - val_accuracy: 0.9500 Epoch 45/100 298/298 [==============================] - 0s 57us/step - loss: 0.0561 - accuracy: 0.9933 - val_loss: 0.2142 - val_accuracy: 0.9400 Epoch 46/100 298/298 [==============================] - 0s 50us/step - loss: 0.0560 - accuracy: 0.9933 - val_loss: 0.1922 - val_accuracy: 0.9500 Epoch 47/100 298/298 [==============================] - 0s 57us/step - loss: 0.0543 - accuracy: 0.9933 - val_loss: 0.2055 - val_accuracy: 0.9500 Epoch 48/100 298/298 [==============================] - 0s 59us/step - loss: 0.0542 - accuracy: 0.9933 - val_loss: 0.1928 - val_accuracy: 0.9500 Epoch 49/100 298/298 [==============================] - 0s 62us/step - loss: 0.0522 - accuracy: 0.9966 - val_loss: 0.1936 - val_accuracy: 0.9500 Epoch 50/100 298/298 [==============================] - 0s 65us/step - loss: 0.0536 - accuracy: 0.9933 - val_loss: 0.1928 - val_accuracy: 0.9500 Epoch 51/100 298/298 [==============================] - 0s 72us/step - loss: 0.0522 - accuracy: 0.9966 - val_loss: 0.1894 - val_accuracy: 0.9500 Epoch 52/100 298/298 [==============================] - 0s 67us/step - loss: 0.0526 - accuracy: 0.9966 - val_loss: 0.2013 - val_accuracy: 0.9400 Epoch 53/100 298/298 [==============================] - 0s 72us/step - loss: 0.0509 - accuracy: 0.9966 - val_loss: 0.1831 - val_accuracy: 0.9500 Epoch 54/100 298/298 [==============================] - 0s 72us/step - loss: 0.0519 - accuracy: 0.9966 - val_loss: 0.1866 - val_accuracy: 0.9400 Epoch 55/100 298/298 [==============================] - 0s 68us/step - loss: 0.0750 - accuracy: 0.9832 - val_loss: 0.2318 - val_accuracy: 0.9400 Epoch 56/100 298/298 [==============================] - 0s 69us/step - loss: 0.0719 - accuracy: 0.9832 - val_loss: 0.2149 - val_accuracy: 0.9500 Epoch 57/100 298/298 [==============================] - 0s 65us/step - loss: 0.0816 - accuracy: 0.9866 - val_loss: 0.2965 - val_accuracy: 0.9100 Epoch 58/100 298/298 [==============================] - 0s 61us/step - loss: 0.0643 - accuracy: 0.9899 - val_loss: 0.2735 - val_accuracy: 0.9300 Epoch 59/100 298/298 [==============================] - 0s 77us/step - loss: 0.0667 - accuracy: 0.9866 - val_loss: 0.2260 - val_accuracy: 0.9300 Epoch 60/100 298/298 [==============================] - 0s 61us/step - loss: 0.0568 - accuracy: 0.9966 - val_loss: 0.2144 - val_accuracy: 0.9400 Epoch 61/100 298/298 [==============================] - 0s 70us/step - loss: 0.0564 - accuracy: 0.9966 - val_loss: 0.1946 - val_accuracy: 0.9500 Epoch 62/100 298/298 [==============================] - 0s 77us/step - loss: 0.0569 - accuracy: 0.9966 - val_loss: 0.2038 - val_accuracy: 0.9500 Epoch 63/100 298/298 [==============================] - 0s 62us/step - loss: 0.0531 - accuracy: 0.9966 - val_loss: 0.2212 - val_accuracy: 0.9400 Epoch 64/100 298/298 [==============================] - 0s 71us/step - loss: 0.0514 - accuracy: 0.9966 - val_loss: 0.2079 - val_accuracy: 0.9500 Epoch 65/100 298/298 [==============================] - 0s 61us/step - loss: 0.0519 - accuracy: 0.9933 - val_loss: 0.1919 - val_accuracy: 0.9600 Epoch 66/100 298/298 [==============================] - 0s 85us/step - loss: 0.0495 - accuracy: 0.9966 - val_loss: 0.1867 - val_accuracy: 0.9400 Epoch 67/100 298/298 [==============================] - 0s 66us/step - loss: 0.0513 - accuracy: 0.9966 - val_loss: 0.2126 - val_accuracy: 0.9500 Epoch 68/100 298/298 [==============================] - 0s 67us/step - loss: 0.0506 - accuracy: 0.9966 - val_loss: 0.2037 - val_accuracy: 0.9400 Epoch 69/100 298/298 [==============================] - 0s 64us/step - loss: 0.0507 - accuracy: 0.9966 - val_loss: 0.2036 - val_accuracy: 0.9500 Epoch 70/100 298/298 [==============================] - 0s 64us/step - loss: 0.0495 - accuracy: 1.0000 - val_loss: 0.1905 - val_accuracy: 0.9400 Epoch 71/100 298/298 [==============================] - 0s 66us/step - loss: 0.0499 - accuracy: 0.9966 - val_loss: 0.1933 - val_accuracy: 0.9500 Epoch 72/100 298/298 [==============================] - 0s 83us/step - loss: 0.0476 - accuracy: 1.0000 - val_loss: 0.1976 - val_accuracy: 0.9400 Epoch 73/100 298/298 [==============================] - 0s 61us/step - loss: 0.0486 - accuracy: 1.0000 - val_loss: 0.2022 - val_accuracy: 0.9300 Epoch 74/100 298/298 [==============================] - 0s 78us/step - loss: 0.0476 - accuracy: 1.0000 - val_loss: 0.2023 - val_accuracy: 0.9500 Epoch 75/100 298/298 [==============================] - 0s 61us/step - loss: 0.0472 - accuracy: 0.9966 - val_loss: 0.1973 - val_accuracy: 0.9500 Epoch 76/100 298/298 [==============================] - 0s 75us/step - loss: 0.0481 - accuracy: 0.9966 - val_loss: 0.1925 - val_accuracy: 0.9400 Epoch 77/100 298/298 [==============================] - 0s 71us/step - loss: 0.0470 - accuracy: 1.0000 - val_loss: 0.1992 - val_accuracy: 0.9500 Epoch 78/100 298/298 [==============================] - 0s 63us/step - loss: 0.0472 - accuracy: 0.9966 - val_loss: 0.2056 - val_accuracy: 0.9500 Epoch 79/100 298/298 [==============================] - 0s 67us/step - loss: 0.0489 - accuracy: 0.9966 - val_loss: 0.2010 - val_accuracy: 0.9500 Epoch 80/100 298/298 [==============================] - 0s 72us/step - loss: 0.0457 - accuracy: 0.9966 - val_loss: 0.2042 - val_accuracy: 0.9500 Epoch 81/100 298/298 [==============================] - 0s 71us/step - loss: 0.0472 - accuracy: 0.9966 - val_loss: 0.1936 - val_accuracy: 0.9500 Epoch 82/100 298/298 [==============================] - 0s 58us/step - loss: 0.0461 - accuracy: 1.0000 - val_loss: 0.2035 - val_accuracy: 0.9400 Epoch 83/100 298/298 [==============================] - 0s 61us/step - loss: 0.0465 - accuracy: 0.9966 - val_loss: 0.2102 - val_accuracy: 0.9400 Epoch 84/100 298/298 [==============================] - 0s 75us/step - loss: 0.0470 - accuracy: 0.9933 - val_loss: 0.1960 - val_accuracy: 0.9500 Epoch 85/100 298/298 [==============================] - 0s 63us/step - loss: 0.0461 - accuracy: 0.9966 - val_loss: 0.1910 - val_accuracy: 0.9400 Epoch 86/100 298/298 [==============================] - 0s 70us/step - loss: 0.0459 - accuracy: 1.0000 - val_loss: 0.1990 - val_accuracy: 0.9400 Epoch 87/100 298/298 [==============================] - 0s 77us/step - loss: 0.0449 - accuracy: 1.0000 - val_loss: 0.1969 - val_accuracy: 0.9400 Epoch 88/100 298/298 [==============================] - 0s 61us/step - loss: 0.0457 - accuracy: 0.9966 - val_loss: 0.2100 - val_accuracy: 0.9400 Epoch 89/100 298/298 [==============================] - 0s 66us/step - loss: 0.0454 - accuracy: 1.0000 - val_loss: 0.1943 - val_accuracy: 0.9500 Epoch 90/100 298/298 [==============================] - 0s 76us/step - loss: 0.0448 - accuracy: 0.9966 - val_loss: 0.1946 - val_accuracy: 0.9500 Epoch 91/100 298/298 [==============================] - 0s 58us/step - loss: 0.0449 - accuracy: 0.9966 - val_loss: 0.1922 - val_accuracy: 0.9400 Epoch 92/100 298/298 [==============================] - 0s 72us/step - loss: 0.0463 - accuracy: 0.9966 - val_loss: 0.1987 - val_accuracy: 0.9500 Epoch 93/100 298/298 [==============================] - 0s 79us/step - loss: 0.0454 - accuracy: 0.9933 - val_loss: 0.1902 - val_accuracy: 0.9600 Epoch 94/100 298/298 [==============================] - 0s 62us/step - loss: 0.0451 - accuracy: 1.0000 - val_loss: 0.1823 - val_accuracy: 0.9500 Epoch 95/100 298/298 [==============================] - 0s 73us/step - loss: 0.0461 - accuracy: 0.9966 - val_loss: 0.1993 - val_accuracy: 0.9500 Epoch 96/100 298/298 [==============================] - 0s 65us/step - loss: 0.0443 - accuracy: 1.0000 - val_loss: 0.2034 - val_accuracy: 0.9500 Epoch 97/100 298/298 [==============================] - 0s 73us/step - loss: 0.0448 - accuracy: 1.0000 - val_loss: 0.2086 - val_accuracy: 0.9400 Epoch 98/100 298/298 [==============================] - 0s 71us/step - loss: 0.0453 - accuracy: 0.9966 - val_loss: 0.1983 - val_accuracy: 0.9500 Epoch 99/100 298/298 [==============================] - 0s 69us/step - loss: 0.0436 - accuracy: 1.0000 - val_loss: 0.1890 - val_accuracy: 0.9400 Epoch 100/100 298/298 [==============================] - 0s 64us/step - loss: 0.0450 - accuracy: 0.9966 - val_loss: 0.2010 - val_accuracy: 0.9400 171/171 [==============================] - 0s 53us/step Optimizers: <keras.optimizers.Adam object at 0x10a1d9690> Epoch Sizes: 100 Neurons or Units: 128 ['loss', 'accuracy'] [0.08477622993856843, 0.988304078578949] Test score: 0.08477622993856843 Test accuracy: 0.988304078578949 Model: "sequential_51" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_151 (Dense) (None, 256) 7936 _________________________________________________________________ activation_151 (Activation) (None, 256) 0 _________________________________________________________________ dense_152 (Dense) (None, 256) 65792 _________________________________________________________________ activation_152 (Activation) (None, 256) 0 _________________________________________________________________ dense_153 (Dense) (None, 1) 257 _________________________________________________________________ activation_153 (Activation) (None, 1) 0 ================================================================= Total params: 73,985 Trainable params: 73,985 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/100 298/298 [==============================] - 0s 703us/step - loss: 2.5386 - accuracy: 0.9027 - val_loss: 1.2928 - val_accuracy: 0.9300 Epoch 2/100 298/298 [==============================] - 0s 74us/step - loss: 0.7681 - accuracy: 0.9765 - val_loss: 0.5430 - val_accuracy: 0.9400 Epoch 3/100 298/298 [==============================] - 0s 80us/step - loss: 0.3301 - accuracy: 0.9832 - val_loss: 0.3862 - val_accuracy: 0.9200 Epoch 4/100 298/298 [==============================] - 0s 73us/step - loss: 0.2102 - accuracy: 0.9899 - val_loss: 0.2759 - val_accuracy: 0.9500 Epoch 5/100 298/298 [==============================] - 0s 79us/step - loss: 0.1426 - accuracy: 0.9866 - val_loss: 0.2030 - val_accuracy: 0.9500 Epoch 6/100 298/298 [==============================] - 0s 93us/step - loss: 0.1158 - accuracy: 0.9899 - val_loss: 0.2155 - val_accuracy: 0.9300 Epoch 7/100 298/298 [==============================] - 0s 92us/step - loss: 0.1088 - accuracy: 0.9866 - val_loss: 0.2078 - val_accuracy: 0.9500 Epoch 8/100 298/298 [==============================] - 0s 101us/step - loss: 0.1063 - accuracy: 0.9899 - val_loss: 0.1993 - val_accuracy: 0.9600 Epoch 9/100 298/298 [==============================] - 0s 97us/step - loss: 0.0898 - accuracy: 0.9933 - val_loss: 0.1908 - val_accuracy: 0.9500 Epoch 10/100 298/298 [==============================] - 0s 92us/step - loss: 0.0831 - accuracy: 0.9933 - val_loss: 0.1994 - val_accuracy: 0.9500 Epoch 11/100 298/298 [==============================] - 0s 119us/step - loss: 0.0791 - accuracy: 0.9933 - val_loss: 0.1842 - val_accuracy: 0.9600 Epoch 12/100 298/298 [==============================] - 0s 89us/step - loss: 0.0749 - accuracy: 0.9933 - val_loss: 0.1971 - val_accuracy: 0.9400 Epoch 13/100 298/298 [==============================] - 0s 102us/step - loss: 0.0763 - accuracy: 0.9899 - val_loss: 0.1901 - val_accuracy: 0.9400 Epoch 14/100 298/298 [==============================] - 0s 89us/step - loss: 0.0722 - accuracy: 0.9933 - val_loss: 0.2067 - val_accuracy: 0.9400 Epoch 15/100 298/298 [==============================] - 0s 112us/step - loss: 0.0772 - accuracy: 0.9933 - val_loss: 0.3510 - val_accuracy: 0.9200 Epoch 16/100 298/298 [==============================] - 0s 82us/step - loss: 0.0869 - accuracy: 0.9832 - val_loss: 0.1906 - val_accuracy: 0.9500 Epoch 17/100 298/298 [==============================] - 0s 81us/step - loss: 0.0756 - accuracy: 0.9966 - val_loss: 0.1781 - val_accuracy: 0.9600 Epoch 18/100 298/298 [==============================] - 0s 96us/step - loss: 0.0761 - accuracy: 0.9866 - val_loss: 0.2020 - val_accuracy: 0.9500 Epoch 19/100 298/298 [==============================] - 0s 94us/step - loss: 0.0716 - accuracy: 0.9899 - val_loss: 0.2394 - val_accuracy: 0.9500 Epoch 20/100 298/298 [==============================] - 0s 87us/step - loss: 0.0692 - accuracy: 0.9933 - val_loss: 0.1962 - val_accuracy: 0.9400 Epoch 21/100 298/298 [==============================] - 0s 91us/step - loss: 0.0639 - accuracy: 0.9933 - val_loss: 0.2121 - val_accuracy: 0.9500 Epoch 22/100 298/298 [==============================] - 0s 87us/step - loss: 0.0628 - accuracy: 0.9933 - val_loss: 0.1848 - val_accuracy: 0.9500 Epoch 23/100 298/298 [==============================] - 0s 86us/step - loss: 0.0589 - accuracy: 0.9933 - val_loss: 0.1862 - val_accuracy: 0.9400 Epoch 24/100 298/298 [==============================] - 0s 77us/step - loss: 0.0623 - accuracy: 0.9933 - val_loss: 0.1926 - val_accuracy: 0.9500 Epoch 25/100 298/298 [==============================] - 0s 82us/step - loss: 0.0592 - accuracy: 0.9899 - val_loss: 0.1996 - val_accuracy: 0.9600 Epoch 26/100 298/298 [==============================] - 0s 79us/step - loss: 0.0587 - accuracy: 0.9933 - val_loss: 0.1968 - val_accuracy: 0.9500 Epoch 27/100 298/298 [==============================] - 0s 78us/step - loss: 0.0616 - accuracy: 0.9933 - val_loss: 0.1882 - val_accuracy: 0.9500 Epoch 28/100 298/298 [==============================] - 0s 76us/step - loss: 0.0583 - accuracy: 0.9933 - val_loss: 0.2229 - val_accuracy: 0.9400 Epoch 29/100 298/298 [==============================] - 0s 74us/step - loss: 0.0857 - accuracy: 0.9832 - val_loss: 0.2407 - val_accuracy: 0.9400 Epoch 30/100 298/298 [==============================] - 0s 73us/step - loss: 0.0745 - accuracy: 0.9866 - val_loss: 0.1920 - val_accuracy: 0.9500 Epoch 31/100 298/298 [==============================] - 0s 74us/step - loss: 0.0644 - accuracy: 0.9899 - val_loss: 0.1886 - val_accuracy: 0.9500 Epoch 32/100 298/298 [==============================] - 0s 77us/step - loss: 0.0588 - accuracy: 0.9933 - val_loss: 0.2240 - val_accuracy: 0.9400 Epoch 33/100 298/298 [==============================] - 0s 71us/step - loss: 0.0578 - accuracy: 0.9933 - val_loss: 0.2291 - val_accuracy: 0.9400 Epoch 34/100 298/298 [==============================] - 0s 79us/step - loss: 0.0552 - accuracy: 0.9966 - val_loss: 0.2161 - val_accuracy: 0.9500 Epoch 35/100 298/298 [==============================] - 0s 82us/step - loss: 0.0542 - accuracy: 0.9933 - val_loss: 0.1961 - val_accuracy: 0.9400 Epoch 36/100 298/298 [==============================] - 0s 77us/step - loss: 0.0529 - accuracy: 0.9933 - val_loss: 0.2068 - val_accuracy: 0.9500 Epoch 37/100 298/298 [==============================] - 0s 70us/step - loss: 0.0525 - accuracy: 0.9966 - val_loss: 0.2027 - val_accuracy: 0.9500 Epoch 38/100 298/298 [==============================] - 0s 68us/step - loss: 0.0522 - accuracy: 0.9966 - val_loss: 0.1945 - val_accuracy: 0.9500 Epoch 39/100 298/298 [==============================] - 0s 75us/step - loss: 0.0521 - accuracy: 0.9966 - val_loss: 0.1980 - val_accuracy: 0.9500 Epoch 40/100 298/298 [==============================] - 0s 67us/step - loss: 0.0520 - accuracy: 0.9933 - val_loss: 0.2057 - val_accuracy: 0.9400 Epoch 41/100 298/298 [==============================] - 0s 72us/step - loss: 0.0528 - accuracy: 0.9966 - val_loss: 0.1906 - val_accuracy: 0.9500 Epoch 42/100 298/298 [==============================] - 0s 75us/step - loss: 0.0543 - accuracy: 0.9966 - val_loss: 0.2269 - val_accuracy: 0.9500 Epoch 43/100 298/298 [==============================] - 0s 74us/step - loss: 0.0536 - accuracy: 0.9933 - val_loss: 0.2238 - val_accuracy: 0.9500 Epoch 44/100 298/298 [==============================] - 0s 67us/step - loss: 0.0515 - accuracy: 0.9933 - val_loss: 0.2109 - val_accuracy: 0.9400 Epoch 45/100 298/298 [==============================] - 0s 76us/step - loss: 0.0498 - accuracy: 0.9966 - val_loss: 0.2058 - val_accuracy: 0.9500 Epoch 46/100 298/298 [==============================] - 0s 71us/step - loss: 0.0484 - accuracy: 0.9966 - val_loss: 0.2031 - val_accuracy: 0.9500 Epoch 47/100 298/298 [==============================] - 0s 73us/step - loss: 0.0499 - accuracy: 0.9966 - val_loss: 0.2019 - val_accuracy: 0.9400 Epoch 48/100 298/298 [==============================] - 0s 79us/step - loss: 0.0503 - accuracy: 0.9933 - val_loss: 0.2105 - val_accuracy: 0.9400 Epoch 49/100 298/298 [==============================] - 0s 81us/step - loss: 0.0511 - accuracy: 0.9933 - val_loss: 0.1660 - val_accuracy: 0.9600 Epoch 50/100 298/298 [==============================] - 0s 78us/step - loss: 0.0552 - accuracy: 0.9966 - val_loss: 0.2037 - val_accuracy: 0.9400 Epoch 51/100 298/298 [==============================] - 0s 68us/step - loss: 0.0522 - accuracy: 0.9966 - val_loss: 0.2552 - val_accuracy: 0.9400 Epoch 52/100 298/298 [==============================] - 0s 75us/step - loss: 0.0723 - accuracy: 0.9832 - val_loss: 0.2459 - val_accuracy: 0.9400 Epoch 53/100 298/298 [==============================] - 0s 72us/step - loss: 0.0619 - accuracy: 0.9933 - val_loss: 0.2075 - val_accuracy: 0.9400 Epoch 54/100 298/298 [==============================] - 0s 77us/step - loss: 0.0584 - accuracy: 0.9966 - val_loss: 0.1893 - val_accuracy: 0.9500 Epoch 55/100 298/298 [==============================] - 0s 75us/step - loss: 0.0511 - accuracy: 1.0000 - val_loss: 0.2130 - val_accuracy: 0.9500 Epoch 56/100 298/298 [==============================] - 0s 80us/step - loss: 0.0498 - accuracy: 0.9933 - val_loss: 0.1997 - val_accuracy: 0.9500 Epoch 57/100 298/298 [==============================] - 0s 79us/step - loss: 0.0497 - accuracy: 0.9966 - val_loss: 0.2144 - val_accuracy: 0.9500 Epoch 58/100 298/298 [==============================] - 0s 72us/step - loss: 0.0538 - accuracy: 0.9933 - val_loss: 0.2385 - val_accuracy: 0.9400 Epoch 59/100 298/298 [==============================] - 0s 84us/step - loss: 0.0537 - accuracy: 0.9933 - val_loss: 0.1846 - val_accuracy: 0.9500 Epoch 60/100 298/298 [==============================] - 0s 99us/step - loss: 0.0524 - accuracy: 0.9966 - val_loss: 0.2025 - val_accuracy: 0.9500 Epoch 61/100 298/298 [==============================] - 0s 87us/step - loss: 0.0481 - accuracy: 0.9933 - val_loss: 0.2003 - val_accuracy: 0.9500 Epoch 62/100 298/298 [==============================] - 0s 98us/step - loss: 0.0459 - accuracy: 1.0000 - val_loss: 0.1986 - val_accuracy: 0.9600 Epoch 63/100 298/298 [==============================] - 0s 78us/step - loss: 0.0457 - accuracy: 0.9966 - val_loss: 0.1982 - val_accuracy: 0.9400 Epoch 64/100 298/298 [==============================] - 0s 82us/step - loss: 0.0457 - accuracy: 1.0000 - val_loss: 0.2013 - val_accuracy: 0.9400 Epoch 65/100 298/298 [==============================] - 0s 83us/step - loss: 0.0447 - accuracy: 1.0000 - val_loss: 0.2151 - val_accuracy: 0.9500 Epoch 66/100 298/298 [==============================] - 0s 83us/step - loss: 0.0452 - accuracy: 1.0000 - val_loss: 0.2079 - val_accuracy: 0.9400 Epoch 67/100 298/298 [==============================] - 0s 83us/step - loss: 0.0435 - accuracy: 1.0000 - val_loss: 0.1989 - val_accuracy: 0.9500 Epoch 68/100 298/298 [==============================] - 0s 66us/step - loss: 0.0456 - accuracy: 0.9966 - val_loss: 0.1937 - val_accuracy: 0.9500 Epoch 69/100 298/298 [==============================] - 0s 77us/step - loss: 0.0436 - accuracy: 1.0000 - val_loss: 0.2039 - val_accuracy: 0.9500 Epoch 70/100 298/298 [==============================] - 0s 76us/step - loss: 0.0443 - accuracy: 0.9966 - val_loss: 0.1991 - val_accuracy: 0.9400 Epoch 71/100 298/298 [==============================] - 0s 68us/step - loss: 0.0433 - accuracy: 1.0000 - val_loss: 0.1985 - val_accuracy: 0.9400 Epoch 72/100 298/298 [==============================] - 0s 77us/step - loss: 0.0432 - accuracy: 1.0000 - val_loss: 0.2047 - val_accuracy: 0.9500 Epoch 73/100 298/298 [==============================] - 0s 73us/step - loss: 0.0428 - accuracy: 1.0000 - val_loss: 0.2008 - val_accuracy: 0.9400 Epoch 74/100 298/298 [==============================] - 0s 72us/step - loss: 0.0434 - accuracy: 1.0000 - val_loss: 0.2060 - val_accuracy: 0.9400 Epoch 75/100 298/298 [==============================] - 0s 66us/step - loss: 0.0424 - accuracy: 0.9966 - val_loss: 0.2005 - val_accuracy: 0.9500 Epoch 76/100 298/298 [==============================] - 0s 75us/step - loss: 0.0446 - accuracy: 0.9966 - val_loss: 0.1973 - val_accuracy: 0.9400 Epoch 77/100 298/298 [==============================] - 0s 68us/step - loss: 0.0458 - accuracy: 0.9933 - val_loss: 0.2002 - val_accuracy: 0.9400 Epoch 78/100 298/298 [==============================] - 0s 69us/step - loss: 0.0485 - accuracy: 0.9966 - val_loss: 0.1959 - val_accuracy: 0.9500 Epoch 79/100 298/298 [==============================] - 0s 73us/step - loss: 0.0495 - accuracy: 0.9933 - val_loss: 0.1801 - val_accuracy: 0.9600 Epoch 80/100 298/298 [==============================] - 0s 67us/step - loss: 0.0440 - accuracy: 0.9966 - val_loss: 0.2063 - val_accuracy: 0.9600 Epoch 81/100 298/298 [==============================] - 0s 71us/step - loss: 0.0431 - accuracy: 0.9966 - val_loss: 0.1997 - val_accuracy: 0.9500 Epoch 82/100 298/298 [==============================] - 0s 77us/step - loss: 0.0429 - accuracy: 1.0000 - val_loss: 0.2026 - val_accuracy: 0.9400 Epoch 83/100 298/298 [==============================] - 0s 72us/step - loss: 0.0462 - accuracy: 0.9966 - val_loss: 0.2113 - val_accuracy: 0.9500 Epoch 84/100 298/298 [==============================] - 0s 66us/step - loss: 0.0421 - accuracy: 1.0000 - val_loss: 0.1883 - val_accuracy: 0.9600 Epoch 85/100 298/298 [==============================] - 0s 73us/step - loss: 0.0463 - accuracy: 0.9966 - val_loss: 0.2056 - val_accuracy: 0.9500 Epoch 86/100 298/298 [==============================] - 0s 67us/step - loss: 0.0415 - accuracy: 1.0000 - val_loss: 0.2125 - val_accuracy: 0.9600 Epoch 87/100 298/298 [==============================] - 0s 77us/step - loss: 0.0421 - accuracy: 1.0000 - val_loss: 0.2034 - val_accuracy: 0.9400 Epoch 88/100 298/298 [==============================] - 0s 74us/step - loss: 0.0429 - accuracy: 0.9966 - val_loss: 0.1959 - val_accuracy: 0.9500 Epoch 89/100 298/298 [==============================] - 0s 71us/step - loss: 0.0406 - accuracy: 1.0000 - val_loss: 0.1993 - val_accuracy: 0.9600 Epoch 90/100 298/298 [==============================] - 0s 68us/step - loss: 0.0442 - accuracy: 0.9966 - val_loss: 0.2024 - val_accuracy: 0.9500 Epoch 91/100 298/298 [==============================] - 0s 73us/step - loss: 0.0445 - accuracy: 0.9933 - val_loss: 0.1937 - val_accuracy: 0.9500 Epoch 92/100 298/298 [==============================] - 0s 66us/step - loss: 0.0419 - accuracy: 1.0000 - val_loss: 0.1888 - val_accuracy: 0.9500 Epoch 93/100 298/298 [==============================] - 0s 70us/step - loss: 0.0402 - accuracy: 1.0000 - val_loss: 0.2065 - val_accuracy: 0.9500 Epoch 94/100 298/298 [==============================] - 0s 71us/step - loss: 0.0408 - accuracy: 1.0000 - val_loss: 0.2117 - val_accuracy: 0.9300 Epoch 95/100 298/298 [==============================] - 0s 77us/step - loss: 0.0416 - accuracy: 1.0000 - val_loss: 0.2054 - val_accuracy: 0.9600 Epoch 96/100 298/298 [==============================] - 0s 69us/step - loss: 0.0427 - accuracy: 1.0000 - val_loss: 0.2003 - val_accuracy: 0.9400 Epoch 97/100 298/298 [==============================] - 0s 76us/step - loss: 0.0424 - accuracy: 0.9966 - val_loss: 0.2050 - val_accuracy: 0.9500 Epoch 98/100 298/298 [==============================] - 0s 78us/step - loss: 0.0393 - accuracy: 1.0000 - val_loss: 0.1953 - val_accuracy: 0.9400 Epoch 99/100 298/298 [==============================] - 0s 66us/step - loss: 0.0397 - accuracy: 1.0000 - val_loss: 0.2041 - val_accuracy: 0.9400 Epoch 100/100 298/298 [==============================] - 0s 74us/step - loss: 0.0398 - accuracy: 1.0000 - val_loss: 0.1988 - val_accuracy: 0.9400 171/171 [==============================] - 0s 44us/step Optimizers: <keras.optimizers.Adam object at 0x10a1d9690> Epoch Sizes: 100 Neurons or Units: 256 ['loss', 'accuracy'] [0.08736893708942926, 0.9824561476707458] Test score: 0.08736893708942926 Test accuracy: 0.9824561476707458 Model: "sequential_52" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_154 (Dense) (None, 64) 1984 _________________________________________________________________ activation_154 (Activation) (None, 64) 0 _________________________________________________________________ dense_155 (Dense) (None, 64) 4160 _________________________________________________________________ activation_155 (Activation) (None, 64) 0 _________________________________________________________________ dense_156 (Dense) (None, 1) 65 _________________________________________________________________ activation_156 (Activation) (None, 1) 0 ================================================================= Total params: 6,209 Trainable params: 6,209 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/200 298/298 [==============================] - 0s 660us/step - loss: 1.3250 - accuracy: 0.8557 - val_loss: 1.0170 - val_accuracy: 0.9000 Epoch 2/200 298/298 [==============================] - 0s 49us/step - loss: 0.7801 - accuracy: 0.9732 - val_loss: 0.7153 - val_accuracy: 0.9200 Epoch 3/200 298/298 [==============================] - 0s 54us/step - loss: 0.5122 - accuracy: 0.9832 - val_loss: 0.4972 - val_accuracy: 0.9400 Epoch 4/200 298/298 [==============================] - 0s 49us/step - loss: 0.3537 - accuracy: 0.9933 - val_loss: 0.3924 - val_accuracy: 0.9300 Epoch 5/200 298/298 [==============================] - 0s 56us/step - loss: 0.2631 - accuracy: 0.9933 - val_loss: 0.3336 - val_accuracy: 0.9400 Epoch 6/200 298/298 [==============================] - 0s 48us/step - loss: 0.2083 - accuracy: 0.9933 - val_loss: 0.2937 - val_accuracy: 0.9300 Epoch 7/200 298/298 [==============================] - 0s 59us/step - loss: 0.1719 - accuracy: 0.9933 - val_loss: 0.2672 - val_accuracy: 0.9300 Epoch 8/200 298/298 [==============================] - 0s 49us/step - loss: 0.1484 - accuracy: 0.9933 - val_loss: 0.2480 - val_accuracy: 0.9400 Epoch 9/200 298/298 [==============================] - 0s 58us/step - loss: 0.1302 - accuracy: 0.9933 - val_loss: 0.2436 - val_accuracy: 0.9400 Epoch 10/200 298/298 [==============================] - 0s 50us/step - loss: 0.1176 - accuracy: 0.9966 - val_loss: 0.2266 - val_accuracy: 0.9400 Epoch 11/200 298/298 [==============================] - 0s 56us/step - loss: 0.1084 - accuracy: 0.9933 - val_loss: 0.2175 - val_accuracy: 0.9400 Epoch 12/200 298/298 [==============================] - 0s 49us/step - loss: 0.1012 - accuracy: 0.9933 - val_loss: 0.1948 - val_accuracy: 0.9600 Epoch 13/200 298/298 [==============================] - 0s 60us/step - loss: 0.0986 - accuracy: 0.9933 - val_loss: 0.2111 - val_accuracy: 0.9400 Epoch 14/200 298/298 [==============================] - 0s 53us/step - loss: 0.0932 - accuracy: 0.9933 - val_loss: 0.2309 - val_accuracy: 0.9300 Epoch 15/200 298/298 [==============================] - 0s 48us/step - loss: 0.0882 - accuracy: 0.9966 - val_loss: 0.2074 - val_accuracy: 0.9500 Epoch 16/200 298/298 [==============================] - 0s 51us/step - loss: 0.0855 - accuracy: 0.9933 - val_loss: 0.1931 - val_accuracy: 0.9500 Epoch 17/200 298/298 [==============================] - 0s 50us/step - loss: 0.0822 - accuracy: 0.9933 - val_loss: 0.2023 - val_accuracy: 0.9500 Epoch 18/200 298/298 [==============================] - 0s 50us/step - loss: 0.0811 - accuracy: 0.9933 - val_loss: 0.2109 - val_accuracy: 0.9400 Epoch 19/200 298/298 [==============================] - 0s 52us/step - loss: 0.0788 - accuracy: 0.9933 - val_loss: 0.1966 - val_accuracy: 0.9500 Epoch 20/200 298/298 [==============================] - 0s 48us/step - loss: 0.0790 - accuracy: 0.9966 - val_loss: 0.2141 - val_accuracy: 0.9500 Epoch 21/200 298/298 [==============================] - 0s 53us/step - loss: 0.0752 - accuracy: 0.9899 - val_loss: 0.2094 - val_accuracy: 0.9500 Epoch 22/200 298/298 [==============================] - 0s 50us/step - loss: 0.0732 - accuracy: 0.9933 - val_loss: 0.2029 - val_accuracy: 0.9500 Epoch 23/200 298/298 [==============================] - 0s 57us/step - loss: 0.0717 - accuracy: 0.9966 - val_loss: 0.1999 - val_accuracy: 0.9400 Epoch 24/200 298/298 [==============================] - 0s 53us/step - loss: 0.0696 - accuracy: 0.9933 - val_loss: 0.2062 - val_accuracy: 0.9400 Epoch 25/200 298/298 [==============================] - 0s 47us/step - loss: 0.0688 - accuracy: 0.9933 - val_loss: 0.2044 - val_accuracy: 0.9500 Epoch 26/200 298/298 [==============================] - 0s 52us/step - loss: 0.0680 - accuracy: 0.9933 - val_loss: 0.2081 - val_accuracy: 0.9500 Epoch 27/200 298/298 [==============================] - 0s 51us/step - loss: 0.0674 - accuracy: 0.9966 - val_loss: 0.1773 - val_accuracy: 0.9600 Epoch 28/200 298/298 [==============================] - 0s 49us/step - loss: 0.0675 - accuracy: 0.9933 - val_loss: 0.1966 - val_accuracy: 0.9500 Epoch 29/200 298/298 [==============================] - 0s 55us/step - loss: 0.0663 - accuracy: 0.9933 - val_loss: 0.2075 - val_accuracy: 0.9400 Epoch 30/200 298/298 [==============================] - 0s 48us/step - loss: 0.0669 - accuracy: 0.9933 - val_loss: 0.1910 - val_accuracy: 0.9500 Epoch 31/200 298/298 [==============================] - 0s 55us/step - loss: 0.0668 - accuracy: 0.9933 - val_loss: 0.1805 - val_accuracy: 0.9500 Epoch 32/200 298/298 [==============================] - 0s 48us/step - loss: 0.0634 - accuracy: 0.9933 - val_loss: 0.1946 - val_accuracy: 0.9600 Epoch 33/200 298/298 [==============================] - 0s 58us/step - loss: 0.0639 - accuracy: 0.9966 - val_loss: 0.1933 - val_accuracy: 0.9600 Epoch 34/200 298/298 [==============================] - 0s 53us/step - loss: 0.0629 - accuracy: 0.9933 - val_loss: 0.1860 - val_accuracy: 0.9500 Epoch 35/200 298/298 [==============================] - 0s 46us/step - loss: 0.0619 - accuracy: 0.9933 - val_loss: 0.1865 - val_accuracy: 0.9500 Epoch 36/200 298/298 [==============================] - 0s 54us/step - loss: 0.0629 - accuracy: 0.9966 - val_loss: 0.1979 - val_accuracy: 0.9500 Epoch 37/200 298/298 [==============================] - 0s 48us/step - loss: 0.0628 - accuracy: 0.9933 - val_loss: 0.1908 - val_accuracy: 0.9500 Epoch 38/200 298/298 [==============================] - 0s 57us/step - loss: 0.0616 - accuracy: 0.9933 - val_loss: 0.1973 - val_accuracy: 0.9500 Epoch 39/200 298/298 [==============================] - 0s 51us/step - loss: 0.0611 - accuracy: 0.9933 - val_loss: 0.1979 - val_accuracy: 0.9600 Epoch 40/200 298/298 [==============================] - 0s 46us/step - loss: 0.0600 - accuracy: 0.9966 - val_loss: 0.1992 - val_accuracy: 0.9400 Epoch 41/200 298/298 [==============================] - 0s 55us/step - loss: 0.0597 - accuracy: 0.9933 - val_loss: 0.2021 - val_accuracy: 0.9500 Epoch 42/200 298/298 [==============================] - 0s 50us/step - loss: 0.0603 - accuracy: 0.9933 - val_loss: 0.2121 - val_accuracy: 0.9400 Epoch 43/200 298/298 [==============================] - 0s 56us/step - loss: 0.0586 - accuracy: 0.9966 - val_loss: 0.1885 - val_accuracy: 0.9500 Epoch 44/200 298/298 [==============================] - 0s 48us/step - loss: 0.0589 - accuracy: 0.9933 - val_loss: 0.1877 - val_accuracy: 0.9500 Epoch 45/200 298/298 [==============================] - 0s 58us/step - loss: 0.0574 - accuracy: 0.9933 - val_loss: 0.2006 - val_accuracy: 0.9500 Epoch 46/200 298/298 [==============================] - 0s 55us/step - loss: 0.0576 - accuracy: 0.9933 - val_loss: 0.2062 - val_accuracy: 0.9400 Epoch 47/200 298/298 [==============================] - 0s 51us/step - loss: 0.0619 - accuracy: 0.9899 - val_loss: 0.1924 - val_accuracy: 0.9600 Epoch 48/200 298/298 [==============================] - 0s 55us/step - loss: 0.0566 - accuracy: 0.9933 - val_loss: 0.2016 - val_accuracy: 0.9600 Epoch 49/200 298/298 [==============================] - 0s 49us/step - loss: 0.0585 - accuracy: 0.9966 - val_loss: 0.1995 - val_accuracy: 0.9500 Epoch 50/200 298/298 [==============================] - 0s 52us/step - loss: 0.0589 - accuracy: 0.9933 - val_loss: 0.2065 - val_accuracy: 0.9500 Epoch 51/200 298/298 [==============================] - 0s 50us/step - loss: 0.0562 - accuracy: 0.9933 - val_loss: 0.1696 - val_accuracy: 0.9600 Epoch 52/200 298/298 [==============================] - 0s 53us/step - loss: 0.0580 - accuracy: 0.9933 - val_loss: 0.1890 - val_accuracy: 0.9600 Epoch 53/200 298/298 [==============================] - 0s 50us/step - loss: 0.0558 - accuracy: 0.9966 - val_loss: 0.1943 - val_accuracy: 0.9400 Epoch 54/200 298/298 [==============================] - 0s 50us/step - loss: 0.0578 - accuracy: 1.0000 - val_loss: 0.2002 - val_accuracy: 0.9600 Epoch 55/200 298/298 [==============================] - 0s 56us/step - loss: 0.0557 - accuracy: 0.9966 - val_loss: 0.1878 - val_accuracy: 0.9600 Epoch 56/200 298/298 [==============================] - 0s 57us/step - loss: 0.0557 - accuracy: 0.9966 - val_loss: 0.1857 - val_accuracy: 0.9600 Epoch 57/200 298/298 [==============================] - 0s 48us/step - loss: 0.0558 - accuracy: 0.9933 - val_loss: 0.1965 - val_accuracy: 0.9600 Epoch 58/200 298/298 [==============================] - 0s 54us/step - loss: 0.0539 - accuracy: 0.9933 - val_loss: 0.1885 - val_accuracy: 0.9600 Epoch 59/200 298/298 [==============================] - 0s 49us/step - loss: 0.0537 - accuracy: 1.0000 - val_loss: 0.1925 - val_accuracy: 0.9600 Epoch 60/200 298/298 [==============================] - 0s 52us/step - loss: 0.0539 - accuracy: 0.9966 - val_loss: 0.1953 - val_accuracy: 0.9500 Epoch 61/200 298/298 [==============================] - 0s 51us/step - loss: 0.0547 - accuracy: 0.9933 - val_loss: 0.1859 - val_accuracy: 0.9500 Epoch 62/200 298/298 [==============================] - 0s 54us/step - loss: 0.0549 - accuracy: 0.9966 - val_loss: 0.2086 - val_accuracy: 0.9400 Epoch 63/200 298/298 [==============================] - 0s 50us/step - loss: 0.0531 - accuracy: 0.9966 - val_loss: 0.2349 - val_accuracy: 0.9500 Epoch 64/200 298/298 [==============================] - 0s 50us/step - loss: 0.0558 - accuracy: 0.9933 - val_loss: 0.2135 - val_accuracy: 0.9500 Epoch 65/200 298/298 [==============================] - 0s 52us/step - loss: 0.0528 - accuracy: 0.9933 - val_loss: 0.1868 - val_accuracy: 0.9400 Epoch 66/200 298/298 [==============================] - 0s 55us/step - loss: 0.0527 - accuracy: 0.9933 - val_loss: 0.1926 - val_accuracy: 0.9600 Epoch 67/200 298/298 [==============================] - 0s 51us/step - loss: 0.0533 - accuracy: 0.9966 - val_loss: 0.1953 - val_accuracy: 0.9500 Epoch 68/200 298/298 [==============================] - 0s 57us/step - loss: 0.0519 - accuracy: 0.9966 - val_loss: 0.1861 - val_accuracy: 0.9500 Epoch 69/200 298/298 [==============================] - 0s 52us/step - loss: 0.0519 - accuracy: 0.9933 - val_loss: 0.1850 - val_accuracy: 0.9500 Epoch 70/200 298/298 [==============================] - 0s 51us/step - loss: 0.0514 - accuracy: 0.9966 - val_loss: 0.1999 - val_accuracy: 0.9400 Epoch 71/200 298/298 [==============================] - 0s 48us/step - loss: 0.0527 - accuracy: 0.9966 - val_loss: 0.2121 - val_accuracy: 0.9400 Epoch 72/200 298/298 [==============================] - 0s 54us/step - loss: 0.0580 - accuracy: 0.9899 - val_loss: 0.2007 - val_accuracy: 0.9500 Epoch 73/200 298/298 [==============================] - 0s 49us/step - loss: 0.0514 - accuracy: 0.9966 - val_loss: 0.1694 - val_accuracy: 0.9600 Epoch 74/200 298/298 [==============================] - 0s 52us/step - loss: 0.0520 - accuracy: 1.0000 - val_loss: 0.1879 - val_accuracy: 0.9400 Epoch 75/200 298/298 [==============================] - 0s 47us/step - loss: 0.0519 - accuracy: 0.9933 - val_loss: 0.1998 - val_accuracy: 0.9600 Epoch 76/200 298/298 [==============================] - 0s 55us/step - loss: 0.0511 - accuracy: 0.9933 - val_loss: 0.1926 - val_accuracy: 0.9500 Epoch 77/200 298/298 [==============================] - 0s 48us/step - loss: 0.0504 - accuracy: 0.9966 - val_loss: 0.1944 - val_accuracy: 0.9600 Epoch 78/200 298/298 [==============================] - 0s 57us/step - loss: 0.0500 - accuracy: 0.9933 - val_loss: 0.1940 - val_accuracy: 0.9600 Epoch 79/200 298/298 [==============================] - 0s 47us/step - loss: 0.0500 - accuracy: 1.0000 - val_loss: 0.1946 - val_accuracy: 0.9500 Epoch 80/200 298/298 [==============================] - 0s 57us/step - loss: 0.0495 - accuracy: 1.0000 - val_loss: 0.1973 - val_accuracy: 0.9500 Epoch 81/200 298/298 [==============================] - 0s 48us/step - loss: 0.0500 - accuracy: 0.9966 - val_loss: 0.1948 - val_accuracy: 0.9500 Epoch 82/200 298/298 [==============================] - 0s 54us/step - loss: 0.0497 - accuracy: 0.9933 - val_loss: 0.2083 - val_accuracy: 0.9400 Epoch 83/200 298/298 [==============================] - 0s 48us/step - loss: 0.0496 - accuracy: 1.0000 - val_loss: 0.1930 - val_accuracy: 0.9400 Epoch 84/200 298/298 [==============================] - 0s 54us/step - loss: 0.0487 - accuracy: 0.9966 - val_loss: 0.1937 - val_accuracy: 0.9600 Epoch 85/200 298/298 [==============================] - 0s 46us/step - loss: 0.0493 - accuracy: 0.9933 - val_loss: 0.1961 - val_accuracy: 0.9500 Epoch 86/200 298/298 [==============================] - 0s 53us/step - loss: 0.0484 - accuracy: 0.9966 - val_loss: 0.1931 - val_accuracy: 0.9500 Epoch 87/200 298/298 [==============================] - 0s 50us/step - loss: 0.0498 - accuracy: 0.9966 - val_loss: 0.2019 - val_accuracy: 0.9400 Epoch 88/200 298/298 [==============================] - 0s 57us/step - loss: 0.0493 - accuracy: 1.0000 - val_loss: 0.1996 - val_accuracy: 0.9400 Epoch 89/200 298/298 [==============================] - 0s 49us/step - loss: 0.0483 - accuracy: 0.9966 - val_loss: 0.1938 - val_accuracy: 0.9500 Epoch 90/200 298/298 [==============================] - 0s 55us/step - loss: 0.0483 - accuracy: 0.9966 - val_loss: 0.1981 - val_accuracy: 0.9500 Epoch 91/200 298/298 [==============================] - 0s 54us/step - loss: 0.0486 - accuracy: 0.9966 - val_loss: 0.1886 - val_accuracy: 0.9500 Epoch 92/200 298/298 [==============================] - 0s 54us/step - loss: 0.0493 - accuracy: 0.9933 - val_loss: 0.1930 - val_accuracy: 0.9500 Epoch 93/200 298/298 [==============================] - 0s 52us/step - loss: 0.0482 - accuracy: 1.0000 - val_loss: 0.1869 - val_accuracy: 0.9500 Epoch 94/200 298/298 [==============================] - 0s 51us/step - loss: 0.0475 - accuracy: 1.0000 - val_loss: 0.1922 - val_accuracy: 0.9500 Epoch 95/200 298/298 [==============================] - 0s 46us/step - loss: 0.0481 - accuracy: 0.9933 - val_loss: 0.1937 - val_accuracy: 0.9600 Epoch 96/200 298/298 [==============================] - 0s 59us/step - loss: 0.0469 - accuracy: 0.9966 - val_loss: 0.2003 - val_accuracy: 0.9500 Epoch 97/200 298/298 [==============================] - 0s 47us/step - loss: 0.0482 - accuracy: 0.9966 - val_loss: 0.1940 - val_accuracy: 0.9600 Epoch 98/200 298/298 [==============================] - 0s 55us/step - loss: 0.0477 - accuracy: 0.9933 - val_loss: 0.1916 - val_accuracy: 0.9500 Epoch 99/200 298/298 [==============================] - 0s 46us/step - loss: 0.0486 - accuracy: 0.9933 - val_loss: 0.1943 - val_accuracy: 0.9500 Epoch 100/200 298/298 [==============================] - 0s 51us/step - loss: 0.0452 - accuracy: 1.0000 - val_loss: 0.2141 - val_accuracy: 0.9400 Epoch 101/200 298/298 [==============================] - 0s 47us/step - loss: 0.0514 - accuracy: 0.9933 - val_loss: 0.2103 - val_accuracy: 0.9300 Epoch 102/200 298/298 [==============================] - 0s 54us/step - loss: 0.0484 - accuracy: 0.9933 - val_loss: 0.2007 - val_accuracy: 0.9500 Epoch 103/200 298/298 [==============================] - 0s 49us/step - loss: 0.0466 - accuracy: 0.9966 - val_loss: 0.1932 - val_accuracy: 0.9600 Epoch 104/200 298/298 [==============================] - 0s 55us/step - loss: 0.0485 - accuracy: 0.9966 - val_loss: 0.1920 - val_accuracy: 0.9500 Epoch 105/200 298/298 [==============================] - 0s 51us/step - loss: 0.0461 - accuracy: 0.9966 - val_loss: 0.1914 - val_accuracy: 0.9500 Epoch 106/200 298/298 [==============================] - 0s 64us/step - loss: 0.0485 - accuracy: 0.9966 - val_loss: 0.1972 - val_accuracy: 0.9500 Epoch 107/200 298/298 [==============================] - 0s 52us/step - loss: 0.0469 - accuracy: 0.9966 - val_loss: 0.1905 - val_accuracy: 0.9500 Epoch 108/200 298/298 [==============================] - 0s 56us/step - loss: 0.0460 - accuracy: 1.0000 - val_loss: 0.1875 - val_accuracy: 0.9600 Epoch 109/200 298/298 [==============================] - 0s 50us/step - loss: 0.0488 - accuracy: 0.9933 - val_loss: 0.1991 - val_accuracy: 0.9500 Epoch 110/200 298/298 [==============================] - 0s 55us/step - loss: 0.0458 - accuracy: 0.9966 - val_loss: 0.1802 - val_accuracy: 0.9500 Epoch 111/200 298/298 [==============================] - 0s 61us/step - loss: 0.0466 - accuracy: 1.0000 - val_loss: 0.1756 - val_accuracy: 0.9500 Epoch 112/200 298/298 [==============================] - 0s 49us/step - loss: 0.0469 - accuracy: 0.9966 - val_loss: 0.1827 - val_accuracy: 0.9500 Epoch 113/200 298/298 [==============================] - 0s 58us/step - loss: 0.0454 - accuracy: 0.9966 - val_loss: 0.1853 - val_accuracy: 0.9400 Epoch 114/200 298/298 [==============================] - 0s 52us/step - loss: 0.0462 - accuracy: 1.0000 - val_loss: 0.2003 - val_accuracy: 0.9400 Epoch 115/200 298/298 [==============================] - 0s 60us/step - loss: 0.0458 - accuracy: 0.9966 - val_loss: 0.2017 - val_accuracy: 0.9600 Epoch 116/200 298/298 [==============================] - 0s 53us/step - loss: 0.0452 - accuracy: 0.9966 - val_loss: 0.2031 - val_accuracy: 0.9500 Epoch 117/200 298/298 [==============================] - 0s 62us/step - loss: 0.0455 - accuracy: 0.9966 - val_loss: 0.1868 - val_accuracy: 0.9600 Epoch 118/200 298/298 [==============================] - 0s 54us/step - loss: 0.0472 - accuracy: 1.0000 - val_loss: 0.1700 - val_accuracy: 0.9600 Epoch 119/200 298/298 [==============================] - 0s 52us/step - loss: 0.0515 - accuracy: 0.9966 - val_loss: 0.2018 - val_accuracy: 0.9600 Epoch 120/200 298/298 [==============================] - 0s 56us/step - loss: 0.0474 - accuracy: 0.9933 - val_loss: 0.2301 - val_accuracy: 0.9500 Epoch 121/200 298/298 [==============================] - 0s 54us/step - loss: 0.0484 - accuracy: 0.9933 - val_loss: 0.2136 - val_accuracy: 0.9400 Epoch 122/200 298/298 [==============================] - 0s 59us/step - loss: 0.0451 - accuracy: 1.0000 - val_loss: 0.1864 - val_accuracy: 0.9500 Epoch 123/200 298/298 [==============================] - 0s 53us/step - loss: 0.0446 - accuracy: 0.9966 - val_loss: 0.1962 - val_accuracy: 0.9500 Epoch 124/200 298/298 [==============================] - 0s 60us/step - loss: 0.0444 - accuracy: 1.0000 - val_loss: 0.1987 - val_accuracy: 0.9500 Epoch 125/200 298/298 [==============================] - 0s 51us/step - loss: 0.0445 - accuracy: 1.0000 - val_loss: 0.2030 - val_accuracy: 0.9500 Epoch 126/200 298/298 [==============================] - 0s 59us/step - loss: 0.0446 - accuracy: 0.9966 - val_loss: 0.1938 - val_accuracy: 0.9500 Epoch 127/200 298/298 [==============================] - 0s 52us/step - loss: 0.0441 - accuracy: 1.0000 - val_loss: 0.1911 - val_accuracy: 0.9400 Epoch 128/200 298/298 [==============================] - 0s 63us/step - loss: 0.0450 - accuracy: 0.9966 - val_loss: 0.1761 - val_accuracy: 0.9600 Epoch 129/200 298/298 [==============================] - 0s 55us/step - loss: 0.0474 - accuracy: 0.9966 - val_loss: 0.1833 - val_accuracy: 0.9500 Epoch 130/200 298/298 [==============================] - 0s 51us/step - loss: 0.0462 - accuracy: 1.0000 - val_loss: 0.2021 - val_accuracy: 0.9400 Epoch 131/200 298/298 [==============================] - 0s 52us/step - loss: 0.0455 - accuracy: 0.9966 - val_loss: 0.2085 - val_accuracy: 0.9500 Epoch 132/200 298/298 [==============================] - 0s 54us/step - loss: 0.0461 - accuracy: 0.9966 - val_loss: 0.1877 - val_accuracy: 0.9400 Epoch 133/200 298/298 [==============================] - 0s 47us/step - loss: 0.0448 - accuracy: 0.9966 - val_loss: 0.1817 - val_accuracy: 0.9500 Epoch 134/200 298/298 [==============================] - 0s 55us/step - loss: 0.0479 - accuracy: 0.9966 - val_loss: 0.2048 - val_accuracy: 0.9500 Epoch 135/200 298/298 [==============================] - 0s 49us/step - loss: 0.0445 - accuracy: 1.0000 - val_loss: 0.2082 - val_accuracy: 0.9500 Epoch 136/200 298/298 [==============================] - 0s 57us/step - loss: 0.0443 - accuracy: 1.0000 - val_loss: 0.1888 - val_accuracy: 0.9500 Epoch 137/200 298/298 [==============================] - 0s 58us/step - loss: 0.0435 - accuracy: 1.0000 - val_loss: 0.1833 - val_accuracy: 0.9500 Epoch 138/200 298/298 [==============================] - 0s 48us/step - loss: 0.0442 - accuracy: 1.0000 - val_loss: 0.1966 - val_accuracy: 0.9400 Epoch 139/200 298/298 [==============================] - 0s 55us/step - loss: 0.0440 - accuracy: 0.9966 - val_loss: 0.1978 - val_accuracy: 0.9500 Epoch 140/200 298/298 [==============================] - 0s 52us/step - loss: 0.0440 - accuracy: 0.9966 - val_loss: 0.2052 - val_accuracy: 0.9500 Epoch 141/200 298/298 [==============================] - 0s 53us/step - loss: 0.0431 - accuracy: 0.9966 - val_loss: 0.1919 - val_accuracy: 0.9500 Epoch 142/200 298/298 [==============================] - 0s 51us/step - loss: 0.0428 - accuracy: 1.0000 - val_loss: 0.1898 - val_accuracy: 0.9600 Epoch 143/200 298/298 [==============================] - 0s 47us/step - loss: 0.0456 - accuracy: 1.0000 - val_loss: 0.1846 - val_accuracy: 0.9500 Epoch 144/200 298/298 [==============================] - 0s 55us/step - loss: 0.0422 - accuracy: 1.0000 - val_loss: 0.2035 - val_accuracy: 0.9500 Epoch 145/200 298/298 [==============================] - 0s 55us/step - loss: 0.0449 - accuracy: 0.9966 - val_loss: 0.1948 - val_accuracy: 0.9600 Epoch 146/200 298/298 [==============================] - 0s 50us/step - loss: 0.0451 - accuracy: 0.9966 - val_loss: 0.2083 - val_accuracy: 0.9600 Epoch 147/200 298/298 [==============================] - 0s 55us/step - loss: 0.0477 - accuracy: 0.9966 - val_loss: 0.2236 - val_accuracy: 0.9500 Epoch 148/200 298/298 [==============================] - 0s 51us/step - loss: 0.0480 - accuracy: 0.9966 - val_loss: 0.2172 - val_accuracy: 0.9400 Epoch 149/200 298/298 [==============================] - 0s 55us/step - loss: 0.0442 - accuracy: 1.0000 - val_loss: 0.2220 - val_accuracy: 0.9400 Epoch 150/200 298/298 [==============================] - 0s 50us/step - loss: 0.0441 - accuracy: 1.0000 - val_loss: 0.1922 - val_accuracy: 0.9500 Epoch 151/200 298/298 [==============================] - 0s 54us/step - loss: 0.0429 - accuracy: 0.9966 - val_loss: 0.1892 - val_accuracy: 0.9500 Epoch 152/200 298/298 [==============================] - 0s 50us/step - loss: 0.0421 - accuracy: 1.0000 - val_loss: 0.1873 - val_accuracy: 0.9600 Epoch 153/200 298/298 [==============================] - 0s 57us/step - loss: 0.0423 - accuracy: 1.0000 - val_loss: 0.1867 - val_accuracy: 0.9500 Epoch 154/200 298/298 [==============================] - 0s 51us/step - loss: 0.0417 - accuracy: 1.0000 - val_loss: 0.1907 - val_accuracy: 0.9600 Epoch 155/200 298/298 [==============================] - 0s 51us/step - loss: 0.0418 - accuracy: 1.0000 - val_loss: 0.1888 - val_accuracy: 0.9600 Epoch 156/200 298/298 [==============================] - 0s 47us/step - loss: 0.0424 - accuracy: 1.0000 - val_loss: 0.1905 - val_accuracy: 0.9500 Epoch 157/200 298/298 [==============================] - 0s 53us/step - loss: 0.0425 - accuracy: 1.0000 - val_loss: 0.1740 - val_accuracy: 0.9600 Epoch 158/200 298/298 [==============================] - 0s 48us/step - loss: 0.0417 - accuracy: 1.0000 - val_loss: 0.1800 - val_accuracy: 0.9500 Epoch 159/200 298/298 [==============================] - 0s 58us/step - loss: 0.0428 - accuracy: 0.9966 - val_loss: 0.1864 - val_accuracy: 0.9500 Epoch 160/200 298/298 [==============================] - 0s 45us/step - loss: 0.0417 - accuracy: 1.0000 - val_loss: 0.2051 - val_accuracy: 0.9600 Epoch 161/200 298/298 [==============================] - 0s 57us/step - loss: 0.0450 - accuracy: 0.9966 - val_loss: 0.2105 - val_accuracy: 0.9500 Epoch 162/200 298/298 [==============================] - 0s 48us/step - loss: 0.0428 - accuracy: 0.9966 - val_loss: 0.1987 - val_accuracy: 0.9600 Epoch 163/200 298/298 [==============================] - 0s 59us/step - loss: 0.0414 - accuracy: 1.0000 - val_loss: 0.1924 - val_accuracy: 0.9400 Epoch 164/200 298/298 [==============================] - 0s 49us/step - loss: 0.0411 - accuracy: 1.0000 - val_loss: 0.1946 - val_accuracy: 0.9500 Epoch 165/200 298/298 [==============================] - 0s 52us/step - loss: 0.0420 - accuracy: 1.0000 - val_loss: 0.2025 - val_accuracy: 0.9400 Epoch 166/200 298/298 [==============================] - 0s 49us/step - loss: 0.0410 - accuracy: 1.0000 - val_loss: 0.2011 - val_accuracy: 0.9500 Epoch 167/200 298/298 [==============================] - 0s 60us/step - loss: 0.0410 - accuracy: 1.0000 - val_loss: 0.1915 - val_accuracy: 0.9500 Epoch 168/200 298/298 [==============================] - 0s 49us/step - loss: 0.0412 - accuracy: 1.0000 - val_loss: 0.1899 - val_accuracy: 0.9400 Epoch 169/200 298/298 [==============================] - 0s 57us/step - loss: 0.0415 - accuracy: 1.0000 - val_loss: 0.1933 - val_accuracy: 0.9600 Epoch 170/200 298/298 [==============================] - 0s 49us/step - loss: 0.0408 - accuracy: 1.0000 - val_loss: 0.1908 - val_accuracy: 0.9500 Epoch 171/200 298/298 [==============================] - 0s 56us/step - loss: 0.0412 - accuracy: 1.0000 - val_loss: 0.1906 - val_accuracy: 0.9500 Epoch 172/200 298/298 [==============================] - 0s 50us/step - loss: 0.0411 - accuracy: 0.9966 - val_loss: 0.1977 - val_accuracy: 0.9600 Epoch 173/200 298/298 [==============================] - 0s 57us/step - loss: 0.0404 - accuracy: 1.0000 - val_loss: 0.1872 - val_accuracy: 0.9500 Epoch 174/200 298/298 [==============================] - 0s 51us/step - loss: 0.0405 - accuracy: 1.0000 - val_loss: 0.1936 - val_accuracy: 0.9500 Epoch 175/200 298/298 [==============================] - 0s 56us/step - loss: 0.0406 - accuracy: 1.0000 - val_loss: 0.2090 - val_accuracy: 0.9400 Epoch 176/200 298/298 [==============================] - 0s 55us/step - loss: 0.0405 - accuracy: 1.0000 - val_loss: 0.1983 - val_accuracy: 0.9500 Epoch 177/200 298/298 [==============================] - 0s 51us/step - loss: 0.0402 - accuracy: 1.0000 - val_loss: 0.1865 - val_accuracy: 0.9500 Epoch 178/200 298/298 [==============================] - 0s 52us/step - loss: 0.0402 - accuracy: 1.0000 - val_loss: 0.1931 - val_accuracy: 0.9500 Epoch 179/200 298/298 [==============================] - 0s 48us/step - loss: 0.0417 - accuracy: 0.9966 - val_loss: 0.2130 - val_accuracy: 0.9500 Epoch 180/200 298/298 [==============================] - 0s 48us/step - loss: 0.0425 - accuracy: 0.9966 - val_loss: 0.2004 - val_accuracy: 0.9400 Epoch 181/200 298/298 [==============================] - 0s 52us/step - loss: 0.0424 - accuracy: 0.9966 - val_loss: 0.2033 - val_accuracy: 0.9500 Epoch 182/200 298/298 [==============================] - 0s 49us/step - loss: 0.0405 - accuracy: 1.0000 - val_loss: 0.2090 - val_accuracy: 0.9400 Epoch 183/200 298/298 [==============================] - 0s 56us/step - loss: 0.0402 - accuracy: 1.0000 - val_loss: 0.2010 - val_accuracy: 0.9500 Epoch 184/200 298/298 [==============================] - 0s 49us/step - loss: 0.0401 - accuracy: 1.0000 - val_loss: 0.1975 - val_accuracy: 0.9600 Epoch 185/200 298/298 [==============================] - 0s 56us/step - loss: 0.0399 - accuracy: 1.0000 - val_loss: 0.1955 - val_accuracy: 0.9500 Epoch 186/200 298/298 [==============================] - 0s 52us/step - loss: 0.0394 - accuracy: 1.0000 - val_loss: 0.1948 - val_accuracy: 0.9400 Epoch 187/200 298/298 [==============================] - 0s 46us/step - loss: 0.0396 - accuracy: 1.0000 - val_loss: 0.1976 - val_accuracy: 0.9500 Epoch 188/200 298/298 [==============================] - 0s 55us/step - loss: 0.0396 - accuracy: 1.0000 - val_loss: 0.1936 - val_accuracy: 0.9600 Epoch 189/200 298/298 [==============================] - 0s 52us/step - loss: 0.0400 - accuracy: 0.9966 - val_loss: 0.1871 - val_accuracy: 0.9500 Epoch 190/200 298/298 [==============================] - 0s 54us/step - loss: 0.0399 - accuracy: 1.0000 - val_loss: 0.1974 - val_accuracy: 0.9400 Epoch 191/200 298/298 [==============================] - 0s 53us/step - loss: 0.0389 - accuracy: 1.0000 - val_loss: 0.1957 - val_accuracy: 0.9600 Epoch 192/200 298/298 [==============================] - 0s 54us/step - loss: 0.0400 - accuracy: 0.9966 - val_loss: 0.1870 - val_accuracy: 0.9500 Epoch 193/200 298/298 [==============================] - 0s 50us/step - loss: 0.0441 - accuracy: 0.9966 - val_loss: 0.2044 - val_accuracy: 0.9500 Epoch 194/200 298/298 [==============================] - 0s 50us/step - loss: 0.0450 - accuracy: 0.9966 - val_loss: 0.2130 - val_accuracy: 0.9500 Epoch 195/200 298/298 [==============================] - 0s 57us/step - loss: 0.0414 - accuracy: 0.9966 - val_loss: 0.1852 - val_accuracy: 0.9500 Epoch 196/200 298/298 [==============================] - 0s 56us/step - loss: 0.0395 - accuracy: 1.0000 - val_loss: 0.1876 - val_accuracy: 0.9400 Epoch 197/200 298/298 [==============================] - 0s 46us/step - loss: 0.0399 - accuracy: 1.0000 - val_loss: 0.1851 - val_accuracy: 0.9500 Epoch 198/200 298/298 [==============================] - 0s 58us/step - loss: 0.0395 - accuracy: 0.9966 - val_loss: 0.1902 - val_accuracy: 0.9500 Epoch 199/200 298/298 [==============================] - 0s 47us/step - loss: 0.0391 - accuracy: 1.0000 - val_loss: 0.1905 - val_accuracy: 0.9600 Epoch 200/200 298/298 [==============================] - 0s 52us/step - loss: 0.0393 - accuracy: 1.0000 - val_loss: 0.1940 - val_accuracy: 0.9600 171/171 [==============================] - 0s 24us/step Optimizers: <keras.optimizers.Adam object at 0x10a1d9690> Epoch Sizes: 200 Neurons or Units: 64 ['loss', 'accuracy'] [0.08247754165129355, 0.988304078578949] Test score: 0.08247754165129355 Test accuracy: 0.988304078578949 Model: "sequential_53" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_157 (Dense) (None, 128) 3968 _________________________________________________________________ activation_157 (Activation) (None, 128) 0 _________________________________________________________________ dense_158 (Dense) (None, 128) 16512 _________________________________________________________________ activation_158 (Activation) (None, 128) 0 _________________________________________________________________ dense_159 (Dense) (None, 1) 129 _________________________________________________________________ activation_159 (Activation) (None, 1) 0 ================================================================= Total params: 20,609 Trainable params: 20,609 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/200 298/298 [==============================] - 0s 662us/step - loss: 1.8141 - accuracy: 0.8725 - val_loss: 1.2653 - val_accuracy: 0.9100 Epoch 2/200 298/298 [==============================] - 0s 56us/step - loss: 0.8813 - accuracy: 0.9765 - val_loss: 0.6781 - val_accuracy: 0.9300 Epoch 3/200 298/298 [==============================] - 0s 59us/step - loss: 0.4499 - accuracy: 0.9899 - val_loss: 0.4057 - val_accuracy: 0.9500 Epoch 4/200 298/298 [==============================] - 0s 50us/step - loss: 0.2716 - accuracy: 0.9933 - val_loss: 0.3164 - val_accuracy: 0.9400 Epoch 5/200 298/298 [==============================] - 0s 57us/step - loss: 0.1897 - accuracy: 0.9933 - val_loss: 0.2722 - val_accuracy: 0.9400 Epoch 6/200 298/298 [==============================] - 0s 50us/step - loss: 0.1468 - accuracy: 0.9933 - val_loss: 0.2436 - val_accuracy: 0.9400 Epoch 7/200 298/298 [==============================] - 0s 60us/step - loss: 0.1229 - accuracy: 0.9933 - val_loss: 0.2134 - val_accuracy: 0.9400 Epoch 8/200 298/298 [==============================] - 0s 55us/step - loss: 0.1109 - accuracy: 0.9933 - val_loss: 0.2390 - val_accuracy: 0.9400 Epoch 9/200 298/298 [==============================] - 0s 51us/step - loss: 0.1016 - accuracy: 0.9899 - val_loss: 0.1848 - val_accuracy: 0.9600 Epoch 10/200 298/298 [==============================] - 0s 56us/step - loss: 0.0940 - accuracy: 0.9933 - val_loss: 0.1969 - val_accuracy: 0.9400 Epoch 11/200 298/298 [==============================] - 0s 51us/step - loss: 0.0872 - accuracy: 0.9933 - val_loss: 0.2223 - val_accuracy: 0.9200 Epoch 12/200 298/298 [==============================] - 0s 53us/step - loss: 0.0818 - accuracy: 0.9933 - val_loss: 0.2115 - val_accuracy: 0.9400 Epoch 13/200 298/298 [==============================] - 0s 55us/step - loss: 0.0793 - accuracy: 0.9899 - val_loss: 0.1859 - val_accuracy: 0.9500 Epoch 14/200 298/298 [==============================] - 0s 51us/step - loss: 0.0775 - accuracy: 0.9899 - val_loss: 0.1956 - val_accuracy: 0.9400 Epoch 15/200 298/298 [==============================] - 0s 53us/step - loss: 0.0746 - accuracy: 0.9933 - val_loss: 0.1954 - val_accuracy: 0.9500 Epoch 16/200 298/298 [==============================] - 0s 49us/step - loss: 0.0719 - accuracy: 0.9966 - val_loss: 0.2046 - val_accuracy: 0.9500 Epoch 17/200 298/298 [==============================] - 0s 52us/step - loss: 0.0737 - accuracy: 0.9899 - val_loss: 0.2019 - val_accuracy: 0.9500 Epoch 18/200 298/298 [==============================] - 0s 56us/step - loss: 0.0684 - accuracy: 0.9933 - val_loss: 0.1838 - val_accuracy: 0.9400 Epoch 19/200 298/298 [==============================] - 0s 50us/step - loss: 0.0692 - accuracy: 0.9899 - val_loss: 0.2361 - val_accuracy: 0.9400 Epoch 20/200 298/298 [==============================] - 0s 57us/step - loss: 0.0675 - accuracy: 0.9966 - val_loss: 0.1871 - val_accuracy: 0.9500 Epoch 21/200 298/298 [==============================] - 0s 49us/step - loss: 0.0671 - accuracy: 0.9933 - val_loss: 0.1858 - val_accuracy: 0.9400 Epoch 22/200 298/298 [==============================] - 0s 53us/step - loss: 0.0648 - accuracy: 0.9966 - val_loss: 0.1880 - val_accuracy: 0.9500 Epoch 23/200 298/298 [==============================] - 0s 52us/step - loss: 0.0628 - accuracy: 0.9933 - val_loss: 0.1886 - val_accuracy: 0.9400 Epoch 24/200 298/298 [==============================] - 0s 52us/step - loss: 0.0628 - accuracy: 0.9933 - val_loss: 0.1941 - val_accuracy: 0.9500 Epoch 25/200 298/298 [==============================] - 0s 50us/step - loss: 0.0620 - accuracy: 0.9899 - val_loss: 0.2171 - val_accuracy: 0.9400 Epoch 26/200 298/298 [==============================] - 0s 59us/step - loss: 0.0720 - accuracy: 0.9866 - val_loss: 0.2041 - val_accuracy: 0.9500 Epoch 27/200 298/298 [==============================] - 0s 51us/step - loss: 0.0630 - accuracy: 0.9933 - val_loss: 0.2193 - val_accuracy: 0.9300 Epoch 28/200 298/298 [==============================] - 0s 60us/step - loss: 0.0613 - accuracy: 0.9933 - val_loss: 0.2612 - val_accuracy: 0.9400 Epoch 29/200 298/298 [==============================] - 0s 51us/step - loss: 0.0601 - accuracy: 0.9933 - val_loss: 0.2167 - val_accuracy: 0.9400 Epoch 30/200 298/298 [==============================] - 0s 56us/step - loss: 0.0620 - accuracy: 0.9933 - val_loss: 0.1896 - val_accuracy: 0.9500 Epoch 31/200 298/298 [==============================] - 0s 54us/step - loss: 0.0637 - accuracy: 0.9899 - val_loss: 0.2342 - val_accuracy: 0.9400 Epoch 32/200 298/298 [==============================] - 0s 59us/step - loss: 0.0646 - accuracy: 0.9899 - val_loss: 0.2139 - val_accuracy: 0.9500 Epoch 33/200 298/298 [==============================] - 0s 61us/step - loss: 0.0559 - accuracy: 0.9966 - val_loss: 0.1953 - val_accuracy: 0.9500 Epoch 34/200 298/298 [==============================] - 0s 69us/step - loss: 0.0582 - accuracy: 0.9933 - val_loss: 0.2250 - val_accuracy: 0.9500 Epoch 35/200 298/298 [==============================] - 0s 71us/step - loss: 0.0584 - accuracy: 0.9933 - val_loss: 0.2148 - val_accuracy: 0.9500 Epoch 36/200 298/298 [==============================] - 0s 61us/step - loss: 0.0578 - accuracy: 0.9933 - val_loss: 0.1969 - val_accuracy: 0.9400 Epoch 37/200 298/298 [==============================] - 0s 62us/step - loss: 0.0614 - accuracy: 0.9933 - val_loss: 0.2214 - val_accuracy: 0.9400 Epoch 38/200 298/298 [==============================] - 0s 57us/step - loss: 0.0614 - accuracy: 0.9899 - val_loss: 0.2148 - val_accuracy: 0.9400 Epoch 39/200 298/298 [==============================] - 0s 65us/step - loss: 0.0578 - accuracy: 1.0000 - val_loss: 0.2173 - val_accuracy: 0.9300 Epoch 40/200 298/298 [==============================] - 0s 68us/step - loss: 0.0558 - accuracy: 0.9933 - val_loss: 0.2204 - val_accuracy: 0.9400 Epoch 41/200 298/298 [==============================] - 0s 68us/step - loss: 0.0546 - accuracy: 0.9966 - val_loss: 0.2049 - val_accuracy: 0.9300 Epoch 42/200 298/298 [==============================] - 0s 60us/step - loss: 0.0527 - accuracy: 0.9933 - val_loss: 0.1976 - val_accuracy: 0.9400 Epoch 43/200 298/298 [==============================] - 0s 56us/step - loss: 0.0534 - accuracy: 0.9966 - val_loss: 0.2062 - val_accuracy: 0.9500 Epoch 44/200 298/298 [==============================] - 0s 66us/step - loss: 0.0519 - accuracy: 0.9966 - val_loss: 0.1970 - val_accuracy: 0.9500 Epoch 45/200 298/298 [==============================] - 0s 77us/step - loss: 0.0535 - accuracy: 0.9933 - val_loss: 0.1969 - val_accuracy: 0.9400 Epoch 46/200 298/298 [==============================] - 0s 71us/step - loss: 0.0525 - accuracy: 0.9966 - val_loss: 0.2181 - val_accuracy: 0.9400 Epoch 47/200 298/298 [==============================] - 0s 59us/step - loss: 0.0512 - accuracy: 0.9966 - val_loss: 0.2149 - val_accuracy: 0.9400 Epoch 48/200 298/298 [==============================] - 0s 67us/step - loss: 0.0514 - accuracy: 0.9933 - val_loss: 0.1974 - val_accuracy: 0.9400 Epoch 49/200 298/298 [==============================] - 0s 75us/step - loss: 0.0557 - accuracy: 0.9899 - val_loss: 0.2121 - val_accuracy: 0.9500 Epoch 50/200 298/298 [==============================] - 0s 76us/step - loss: 0.0536 - accuracy: 0.9899 - val_loss: 0.2227 - val_accuracy: 0.9400 Epoch 51/200 298/298 [==============================] - 0s 82us/step - loss: 0.0564 - accuracy: 0.9933 - val_loss: 0.1698 - val_accuracy: 0.9600 Epoch 52/200 298/298 [==============================] - 0s 87us/step - loss: 0.0498 - accuracy: 1.0000 - val_loss: 0.2066 - val_accuracy: 0.9400 Epoch 53/200 298/298 [==============================] - 0s 65us/step - loss: 0.0506 - accuracy: 0.9933 - val_loss: 0.2299 - val_accuracy: 0.9300 Epoch 54/200 298/298 [==============================] - 0s 71us/step - loss: 0.0511 - accuracy: 0.9966 - val_loss: 0.2129 - val_accuracy: 0.9300 Epoch 55/200 298/298 [==============================] - 0s 72us/step - loss: 0.0492 - accuracy: 0.9966 - val_loss: 0.2144 - val_accuracy: 0.9400 Epoch 56/200 298/298 [==============================] - 0s 57us/step - loss: 0.0485 - accuracy: 0.9966 - val_loss: 0.2014 - val_accuracy: 0.9500 Epoch 57/200 298/298 [==============================] - 0s 76us/step - loss: 0.0485 - accuracy: 0.9966 - val_loss: 0.1956 - val_accuracy: 0.9500 Epoch 58/200 298/298 [==============================] - 0s 68us/step - loss: 0.0489 - accuracy: 0.9933 - val_loss: 0.2113 - val_accuracy: 0.9400 Epoch 59/200 298/298 [==============================] - 0s 55us/step - loss: 0.0479 - accuracy: 0.9966 - val_loss: 0.1991 - val_accuracy: 0.9500 Epoch 60/200 298/298 [==============================] - 0s 66us/step - loss: 0.0485 - accuracy: 0.9966 - val_loss: 0.2053 - val_accuracy: 0.9400 Epoch 61/200 298/298 [==============================] - 0s 78us/step - loss: 0.0483 - accuracy: 0.9933 - val_loss: 0.2015 - val_accuracy: 0.9500 Epoch 62/200 298/298 [==============================] - 0s 53us/step - loss: 0.0473 - accuracy: 0.9966 - val_loss: 0.2008 - val_accuracy: 0.9500 Epoch 63/200 298/298 [==============================] - 0s 70us/step - loss: 0.0488 - accuracy: 0.9933 - val_loss: 0.2151 - val_accuracy: 0.9500 Epoch 64/200 298/298 [==============================] - 0s 66us/step - loss: 0.0475 - accuracy: 0.9966 - val_loss: 0.2048 - val_accuracy: 0.9400 Epoch 65/200 298/298 [==============================] - 0s 53us/step - loss: 0.0482 - accuracy: 0.9966 - val_loss: 0.2068 - val_accuracy: 0.9300 Epoch 66/200 298/298 [==============================] - 0s 67us/step - loss: 0.0467 - accuracy: 1.0000 - val_loss: 0.2292 - val_accuracy: 0.9500 Epoch 67/200 298/298 [==============================] - 0s 58us/step - loss: 0.0491 - accuracy: 0.9933 - val_loss: 0.2198 - val_accuracy: 0.9400 Epoch 68/200 298/298 [==============================] - 0s 63us/step - loss: 0.0474 - accuracy: 0.9933 - val_loss: 0.1878 - val_accuracy: 0.9400 Epoch 69/200 298/298 [==============================] - 0s 59us/step - loss: 0.0469 - accuracy: 0.9966 - val_loss: 0.2120 - val_accuracy: 0.9400 Epoch 70/200 298/298 [==============================] - 0s 60us/step - loss: 0.0514 - accuracy: 0.9933 - val_loss: 0.2485 - val_accuracy: 0.9400 Epoch 71/200 298/298 [==============================] - 0s 67us/step - loss: 0.0504 - accuracy: 0.9933 - val_loss: 0.2231 - val_accuracy: 0.9500 Epoch 72/200 298/298 [==============================] - 0s 60us/step - loss: 0.0484 - accuracy: 1.0000 - val_loss: 0.1766 - val_accuracy: 0.9600 Epoch 73/200 298/298 [==============================] - 0s 66us/step - loss: 0.0516 - accuracy: 0.9966 - val_loss: 0.2080 - val_accuracy: 0.9500 Epoch 74/200 298/298 [==============================] - 0s 65us/step - loss: 0.0454 - accuracy: 1.0000 - val_loss: 0.1917 - val_accuracy: 0.9600 Epoch 75/200 298/298 [==============================] - 0s 51us/step - loss: 0.0477 - accuracy: 0.9933 - val_loss: 0.1902 - val_accuracy: 0.9600 Epoch 76/200 298/298 [==============================] - 0s 64us/step - loss: 0.0489 - accuracy: 0.9933 - val_loss: 0.2084 - val_accuracy: 0.9500 Epoch 77/200 298/298 [==============================] - 0s 62us/step - loss: 0.0466 - accuracy: 0.9966 - val_loss: 0.2371 - val_accuracy: 0.9500 Epoch 78/200 298/298 [==============================] - 0s 66us/step - loss: 0.0459 - accuracy: 0.9966 - val_loss: 0.2281 - val_accuracy: 0.9300 Epoch 79/200 298/298 [==============================] - 0s 74us/step - loss: 0.0504 - accuracy: 1.0000 - val_loss: 0.1908 - val_accuracy: 0.9600 Epoch 80/200 298/298 [==============================] - 0s 58us/step - loss: 0.0476 - accuracy: 0.9966 - val_loss: 0.1987 - val_accuracy: 0.9600 Epoch 81/200 298/298 [==============================] - 0s 63us/step - loss: 0.0515 - accuracy: 0.9933 - val_loss: 0.2075 - val_accuracy: 0.9600 Epoch 82/200 298/298 [==============================] - 0s 52us/step - loss: 0.0513 - accuracy: 0.9933 - val_loss: 0.2741 - val_accuracy: 0.9500 Epoch 83/200 298/298 [==============================] - 0s 70us/step - loss: 0.0516 - accuracy: 0.9933 - val_loss: 0.2360 - val_accuracy: 0.9200 Epoch 84/200 298/298 [==============================] - 0s 65us/step - loss: 0.0559 - accuracy: 0.9966 - val_loss: 0.1892 - val_accuracy: 0.9600 Epoch 85/200 298/298 [==============================] - 0s 57us/step - loss: 0.0475 - accuracy: 0.9966 - val_loss: 0.2299 - val_accuracy: 0.9500 Epoch 86/200 298/298 [==============================] - 0s 69us/step - loss: 0.0473 - accuracy: 0.9966 - val_loss: 0.2146 - val_accuracy: 0.9500 Epoch 87/200 298/298 [==============================] - 0s 52us/step - loss: 0.0448 - accuracy: 1.0000 - val_loss: 0.2003 - val_accuracy: 0.9400 Epoch 88/200 298/298 [==============================] - 0s 65us/step - loss: 0.0466 - accuracy: 0.9966 - val_loss: 0.2008 - val_accuracy: 0.9500 Epoch 89/200 298/298 [==============================] - 0s 65us/step - loss: 0.0558 - accuracy: 0.9933 - val_loss: 0.1985 - val_accuracy: 0.9500 Epoch 90/200 298/298 [==============================] - 0s 49us/step - loss: 0.0480 - accuracy: 0.9966 - val_loss: 0.2296 - val_accuracy: 0.9500 Epoch 91/200 298/298 [==============================] - 0s 53us/step - loss: 0.0474 - accuracy: 0.9966 - val_loss: 0.2050 - val_accuracy: 0.9500 Epoch 92/200 298/298 [==============================] - 0s 52us/step - loss: 0.0451 - accuracy: 1.0000 - val_loss: 0.2014 - val_accuracy: 0.9500 Epoch 93/200 298/298 [==============================] - 0s 55us/step - loss: 0.0441 - accuracy: 1.0000 - val_loss: 0.2203 - val_accuracy: 0.9400 Epoch 94/200 298/298 [==============================] - 0s 49us/step - loss: 0.0455 - accuracy: 0.9933 - val_loss: 0.2080 - val_accuracy: 0.9500 Epoch 95/200 298/298 [==============================] - 0s 55us/step - loss: 0.0486 - accuracy: 0.9933 - val_loss: 0.2209 - val_accuracy: 0.9600 Epoch 96/200 298/298 [==============================] - 0s 51us/step - loss: 0.0477 - accuracy: 1.0000 - val_loss: 0.1992 - val_accuracy: 0.9500 Epoch 97/200 298/298 [==============================] - 0s 61us/step - loss: 0.0442 - accuracy: 1.0000 - val_loss: 0.2049 - val_accuracy: 0.9500 Epoch 98/200 298/298 [==============================] - 0s 49us/step - loss: 0.0427 - accuracy: 1.0000 - val_loss: 0.2046 - val_accuracy: 0.9500 Epoch 99/200 298/298 [==============================] - 0s 54us/step - loss: 0.0431 - accuracy: 1.0000 - val_loss: 0.2071 - val_accuracy: 0.9500 Epoch 100/200 298/298 [==============================] - 0s 48us/step - loss: 0.0423 - accuracy: 1.0000 - val_loss: 0.2186 - val_accuracy: 0.9400 Epoch 101/200 298/298 [==============================] - 0s 58us/step - loss: 0.0427 - accuracy: 1.0000 - val_loss: 0.2068 - val_accuracy: 0.9400 Epoch 102/200 298/298 [==============================] - 0s 50us/step - loss: 0.0422 - accuracy: 1.0000 - val_loss: 0.2115 - val_accuracy: 0.9500 Epoch 103/200 298/298 [==============================] - 0s 55us/step - loss: 0.0457 - accuracy: 0.9966 - val_loss: 0.2086 - val_accuracy: 0.9500 Epoch 104/200 298/298 [==============================] - 0s 47us/step - loss: 0.0481 - accuracy: 0.9933 - val_loss: 0.1981 - val_accuracy: 0.9400 Epoch 105/200 298/298 [==============================] - 0s 56us/step - loss: 0.0427 - accuracy: 0.9966 - val_loss: 0.2240 - val_accuracy: 0.9400 Epoch 106/200 298/298 [==============================] - 0s 49us/step - loss: 0.0509 - accuracy: 0.9899 - val_loss: 0.2202 - val_accuracy: 0.9500 Epoch 107/200 298/298 [==============================] - 0s 52us/step - loss: 0.0518 - accuracy: 0.9899 - val_loss: 0.2225 - val_accuracy: 0.9500 Epoch 108/200 298/298 [==============================] - 0s 51us/step - loss: 0.0438 - accuracy: 0.9933 - val_loss: 0.2063 - val_accuracy: 0.9400 Epoch 109/200 298/298 [==============================] - 0s 55us/step - loss: 0.0456 - accuracy: 0.9966 - val_loss: 0.1928 - val_accuracy: 0.9500 Epoch 110/200 298/298 [==============================] - 0s 55us/step - loss: 0.0454 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9200 Epoch 111/200 298/298 [==============================] - 0s 58us/step - loss: 0.0459 - accuracy: 0.9933 - val_loss: 0.2878 - val_accuracy: 0.9300 Epoch 112/200 298/298 [==============================] - 0s 68us/step - loss: 0.0441 - accuracy: 0.9966 - val_loss: 0.2534 - val_accuracy: 0.9300 Epoch 113/200 298/298 [==============================] - 0s 52us/step - loss: 0.0423 - accuracy: 1.0000 - val_loss: 0.2142 - val_accuracy: 0.9400 Epoch 114/200 298/298 [==============================] - 0s 64us/step - loss: 0.0430 - accuracy: 0.9966 - val_loss: 0.1944 - val_accuracy: 0.9400 Epoch 115/200 298/298 [==============================] - 0s 59us/step - loss: 0.0412 - accuracy: 1.0000 - val_loss: 0.2023 - val_accuracy: 0.9400 Epoch 116/200 298/298 [==============================] - 0s 61us/step - loss: 0.0419 - accuracy: 1.0000 - val_loss: 0.2095 - val_accuracy: 0.9400 Epoch 117/200 298/298 [==============================] - 0s 54us/step - loss: 0.0412 - accuracy: 1.0000 - val_loss: 0.1980 - val_accuracy: 0.9500 Epoch 118/200 298/298 [==============================] - 0s 53us/step - loss: 0.0409 - accuracy: 1.0000 - val_loss: 0.1984 - val_accuracy: 0.9500 Epoch 119/200 298/298 [==============================] - 0s 52us/step - loss: 0.0407 - accuracy: 1.0000 - val_loss: 0.2071 - val_accuracy: 0.9500 Epoch 120/200 298/298 [==============================] - 0s 55us/step - loss: 0.0410 - accuracy: 1.0000 - val_loss: 0.2043 - val_accuracy: 0.9400 Epoch 121/200 298/298 [==============================] - 0s 62us/step - loss: 0.0456 - accuracy: 0.9966 - val_loss: 0.1958 - val_accuracy: 0.9400 Epoch 122/200 298/298 [==============================] - 0s 50us/step - loss: 0.0410 - accuracy: 1.0000 - val_loss: 0.2263 - val_accuracy: 0.9500 Epoch 123/200 298/298 [==============================] - 0s 62us/step - loss: 0.0423 - accuracy: 0.9966 - val_loss: 0.2213 - val_accuracy: 0.9500 Epoch 124/200 298/298 [==============================] - 0s 52us/step - loss: 0.0397 - accuracy: 1.0000 - val_loss: 0.2102 - val_accuracy: 0.9400 Epoch 125/200 298/298 [==============================] - 0s 59us/step - loss: 0.0423 - accuracy: 1.0000 - val_loss: 0.2156 - val_accuracy: 0.9300 Epoch 126/200 298/298 [==============================] - 0s 53us/step - loss: 0.0392 - accuracy: 1.0000 - val_loss: 0.2080 - val_accuracy: 0.9500 Epoch 127/200 298/298 [==============================] - 0s 61us/step - loss: 0.0417 - accuracy: 0.9966 - val_loss: 0.1948 - val_accuracy: 0.9500 Epoch 128/200 298/298 [==============================] - 0s 53us/step - loss: 0.0425 - accuracy: 0.9966 - val_loss: 0.2057 - val_accuracy: 0.9400 Epoch 129/200 298/298 [==============================] - 0s 58us/step - loss: 0.0412 - accuracy: 0.9966 - val_loss: 0.2274 - val_accuracy: 0.9500 Epoch 130/200 298/298 [==============================] - 0s 60us/step - loss: 0.0407 - accuracy: 1.0000 - val_loss: 0.2126 - val_accuracy: 0.9400 Epoch 131/200 298/298 [==============================] - 0s 52us/step - loss: 0.0400 - accuracy: 1.0000 - val_loss: 0.2135 - val_accuracy: 0.9400 Epoch 132/200 298/298 [==============================] - 0s 58us/step - loss: 0.0393 - accuracy: 1.0000 - val_loss: 0.2126 - val_accuracy: 0.9400 Epoch 133/200 298/298 [==============================] - 0s 50us/step - loss: 0.0401 - accuracy: 0.9966 - val_loss: 0.2121 - val_accuracy: 0.9500 Epoch 134/200 298/298 [==============================] - 0s 61us/step - loss: 0.0411 - accuracy: 0.9966 - val_loss: 0.1991 - val_accuracy: 0.9400 Epoch 135/200 298/298 [==============================] - 0s 49us/step - loss: 0.0455 - accuracy: 0.9966 - val_loss: 0.1930 - val_accuracy: 0.9500 Epoch 136/200 298/298 [==============================] - 0s 55us/step - loss: 0.0415 - accuracy: 0.9966 - val_loss: 0.2321 - val_accuracy: 0.9500 Epoch 137/200 298/298 [==============================] - 0s 51us/step - loss: 0.0410 - accuracy: 0.9966 - val_loss: 0.2169 - val_accuracy: 0.9500 Epoch 138/200 298/298 [==============================] - 0s 60us/step - loss: 0.0457 - accuracy: 0.9966 - val_loss: 0.2255 - val_accuracy: 0.9400 Epoch 139/200 298/298 [==============================] - 0s 50us/step - loss: 0.0394 - accuracy: 1.0000 - val_loss: 0.2267 - val_accuracy: 0.9500 Epoch 140/200 298/298 [==============================] - 0s 59us/step - loss: 0.0402 - accuracy: 1.0000 - val_loss: 0.2041 - val_accuracy: 0.9500 Epoch 141/200 298/298 [==============================] - 0s 50us/step - loss: 0.0396 - accuracy: 1.0000 - val_loss: 0.2028 - val_accuracy: 0.9400 Epoch 142/200 298/298 [==============================] - 0s 62us/step - loss: 0.0392 - accuracy: 1.0000 - val_loss: 0.2095 - val_accuracy: 0.9400 Epoch 143/200 298/298 [==============================] - 0s 51us/step - loss: 0.0388 - accuracy: 1.0000 - val_loss: 0.2150 - val_accuracy: 0.9400 Epoch 144/200 298/298 [==============================] - 0s 57us/step - loss: 0.0381 - accuracy: 1.0000 - val_loss: 0.2073 - val_accuracy: 0.9400 Epoch 145/200 298/298 [==============================] - 0s 52us/step - loss: 0.0395 - accuracy: 1.0000 - val_loss: 0.2071 - val_accuracy: 0.9400 Epoch 146/200 298/298 [==============================] - 0s 55us/step - loss: 0.0393 - accuracy: 0.9966 - val_loss: 0.2037 - val_accuracy: 0.9400 Epoch 147/200 298/298 [==============================] - 0s 52us/step - loss: 0.0383 - accuracy: 1.0000 - val_loss: 0.1963 - val_accuracy: 0.9400 Epoch 148/200 298/298 [==============================] - 0s 57us/step - loss: 0.0390 - accuracy: 1.0000 - val_loss: 0.2164 - val_accuracy: 0.9400 Epoch 149/200 298/298 [==============================] - 0s 58us/step - loss: 0.0387 - accuracy: 1.0000 - val_loss: 0.2202 - val_accuracy: 0.9400 Epoch 150/200 298/298 [==============================] - 0s 51us/step - loss: 0.0380 - accuracy: 1.0000 - val_loss: 0.1990 - val_accuracy: 0.9400 Epoch 151/200 298/298 [==============================] - 0s 59us/step - loss: 0.0377 - accuracy: 1.0000 - val_loss: 0.2009 - val_accuracy: 0.9400 Epoch 152/200 298/298 [==============================] - 0s 52us/step - loss: 0.0376 - accuracy: 1.0000 - val_loss: 0.2081 - val_accuracy: 0.9500 Epoch 153/200 298/298 [==============================] - 0s 64us/step - loss: 0.0387 - accuracy: 1.0000 - val_loss: 0.2114 - val_accuracy: 0.9400 Epoch 154/200 298/298 [==============================] - 0s 54us/step - loss: 0.0378 - accuracy: 1.0000 - val_loss: 0.1993 - val_accuracy: 0.9500 Epoch 155/200 298/298 [==============================] - 0s 61us/step - loss: 0.0375 - accuracy: 1.0000 - val_loss: 0.1875 - val_accuracy: 0.9500 Epoch 156/200 298/298 [==============================] - 0s 62us/step - loss: 0.0378 - accuracy: 1.0000 - val_loss: 0.1979 - val_accuracy: 0.9400 Epoch 157/200 298/298 [==============================] - 0s 55us/step - loss: 0.0377 - accuracy: 1.0000 - val_loss: 0.2051 - val_accuracy: 0.9400 Epoch 158/200 298/298 [==============================] - 0s 57us/step - loss: 0.0385 - accuracy: 1.0000 - val_loss: 0.2138 - val_accuracy: 0.9500 Epoch 159/200 298/298 [==============================] - 0s 49us/step - loss: 0.0380 - accuracy: 1.0000 - val_loss: 0.2099 - val_accuracy: 0.9300 Epoch 160/200 298/298 [==============================] - 0s 58us/step - loss: 0.0383 - accuracy: 0.9966 - val_loss: 0.2119 - val_accuracy: 0.9400 Epoch 161/200 298/298 [==============================] - 0s 50us/step - loss: 0.0376 - accuracy: 1.0000 - val_loss: 0.2044 - val_accuracy: 0.9400 Epoch 162/200 298/298 [==============================] - 0s 55us/step - loss: 0.0379 - accuracy: 1.0000 - val_loss: 0.2018 - val_accuracy: 0.9500 Epoch 163/200 298/298 [==============================] - 0s 52us/step - loss: 0.0366 - accuracy: 1.0000 - val_loss: 0.2013 - val_accuracy: 0.9500 Epoch 164/200 298/298 [==============================] - 0s 62us/step - loss: 0.0375 - accuracy: 1.0000 - val_loss: 0.2012 - val_accuracy: 0.9500 Epoch 165/200 298/298 [==============================] - 0s 55us/step - loss: 0.0369 - accuracy: 1.0000 - val_loss: 0.1943 - val_accuracy: 0.9500 Epoch 166/200 298/298 [==============================] - 0s 61us/step - loss: 0.0372 - accuracy: 1.0000 - val_loss: 0.2132 - val_accuracy: 0.9500 Epoch 167/200 298/298 [==============================] - 0s 59us/step - loss: 0.0375 - accuracy: 1.0000 - val_loss: 0.2133 - val_accuracy: 0.9400 Epoch 168/200 298/298 [==============================] - 0s 50us/step - loss: 0.0368 - accuracy: 1.0000 - val_loss: 0.2038 - val_accuracy: 0.9400 Epoch 169/200 298/298 [==============================] - 0s 56us/step - loss: 0.0406 - accuracy: 0.9966 - val_loss: 0.2031 - val_accuracy: 0.9400 Epoch 170/200 298/298 [==============================] - 0s 52us/step - loss: 0.0374 - accuracy: 1.0000 - val_loss: 0.1863 - val_accuracy: 0.9400 Epoch 171/200 298/298 [==============================] - 0s 58us/step - loss: 0.0385 - accuracy: 0.9966 - val_loss: 0.1937 - val_accuracy: 0.9500 Epoch 172/200 298/298 [==============================] - 0s 51us/step - loss: 0.0369 - accuracy: 0.9966 - val_loss: 0.1845 - val_accuracy: 0.9500 Epoch 173/200 298/298 [==============================] - 0s 57us/step - loss: 0.0366 - accuracy: 1.0000 - val_loss: 0.1942 - val_accuracy: 0.9400 Epoch 174/200 298/298 [==============================] - 0s 48us/step - loss: 0.0366 - accuracy: 1.0000 - val_loss: 0.2061 - val_accuracy: 0.9500 Epoch 175/200 298/298 [==============================] - 0s 55us/step - loss: 0.0366 - accuracy: 1.0000 - val_loss: 0.1986 - val_accuracy: 0.9600 Epoch 176/200 298/298 [==============================] - 0s 50us/step - loss: 0.0361 - accuracy: 1.0000 - val_loss: 0.1985 - val_accuracy: 0.9500 Epoch 177/200 298/298 [==============================] - 0s 49us/step - loss: 0.0361 - accuracy: 1.0000 - val_loss: 0.2022 - val_accuracy: 0.9400 Epoch 178/200 298/298 [==============================] - 0s 53us/step - loss: 0.0361 - accuracy: 1.0000 - val_loss: 0.2025 - val_accuracy: 0.9500 Epoch 179/200 298/298 [==============================] - 0s 61us/step - loss: 0.0376 - accuracy: 1.0000 - val_loss: 0.2048 - val_accuracy: 0.9500 Epoch 180/200 298/298 [==============================] - 0s 50us/step - loss: 0.0384 - accuracy: 1.0000 - val_loss: 0.2031 - val_accuracy: 0.9300 Epoch 181/200 298/298 [==============================] - 0s 56us/step - loss: 0.0381 - accuracy: 1.0000 - val_loss: 0.2253 - val_accuracy: 0.9500 Epoch 182/200 298/298 [==============================] - 0s 48us/step - loss: 0.0372 - accuracy: 1.0000 - val_loss: 0.2224 - val_accuracy: 0.9400 Epoch 183/200 298/298 [==============================] - 0s 58us/step - loss: 0.0365 - accuracy: 1.0000 - val_loss: 0.1811 - val_accuracy: 0.9600 Epoch 184/200 298/298 [==============================] - 0s 54us/step - loss: 0.0360 - accuracy: 1.0000 - val_loss: 0.2058 - val_accuracy: 0.9400 Epoch 185/200 298/298 [==============================] - 0s 58us/step - loss: 0.0369 - accuracy: 1.0000 - val_loss: 0.2088 - val_accuracy: 0.9300 Epoch 186/200 298/298 [==============================] - 0s 53us/step - loss: 0.0363 - accuracy: 0.9966 - val_loss: 0.2042 - val_accuracy: 0.9400 Epoch 187/200 298/298 [==============================] - 0s 53us/step - loss: 0.0415 - accuracy: 0.9966 - val_loss: 0.2015 - val_accuracy: 0.9600 Epoch 188/200 298/298 [==============================] - 0s 53us/step - loss: 0.0462 - accuracy: 0.9933 - val_loss: 0.2276 - val_accuracy: 0.9400 Epoch 189/200 298/298 [==============================] - 0s 54us/step - loss: 0.0443 - accuracy: 0.9933 - val_loss: 0.2174 - val_accuracy: 0.9500 Epoch 190/200 298/298 [==============================] - 0s 51us/step - loss: 0.0444 - accuracy: 0.9933 - val_loss: 0.2626 - val_accuracy: 0.9500 Epoch 191/200 298/298 [==============================] - 0s 49us/step - loss: 0.0378 - accuracy: 0.9966 - val_loss: 0.2455 - val_accuracy: 0.9500 Epoch 192/200 298/298 [==============================] - 0s 50us/step - loss: 0.0387 - accuracy: 0.9933 - val_loss: 0.2113 - val_accuracy: 0.9500 Epoch 193/200 298/298 [==============================] - 0s 52us/step - loss: 0.0365 - accuracy: 1.0000 - val_loss: 0.1939 - val_accuracy: 0.9500 Epoch 194/200 298/298 [==============================] - 0s 51us/step - loss: 0.0355 - accuracy: 1.0000 - val_loss: 0.2053 - val_accuracy: 0.9600 Epoch 195/200 298/298 [==============================] - 0s 55us/step - loss: 0.0375 - accuracy: 0.9966 - val_loss: 0.2049 - val_accuracy: 0.9500 Epoch 196/200 298/298 [==============================] - 0s 50us/step - loss: 0.0360 - accuracy: 1.0000 - val_loss: 0.2123 - val_accuracy: 0.9500 Epoch 197/200 298/298 [==============================] - 0s 56us/step - loss: 0.0361 - accuracy: 1.0000 - val_loss: 0.2174 - val_accuracy: 0.9500 Epoch 198/200 298/298 [==============================] - 0s 51us/step - loss: 0.0356 - accuracy: 1.0000 - val_loss: 0.2155 - val_accuracy: 0.9500 Epoch 199/200 298/298 [==============================] - 0s 69us/step - loss: 0.0359 - accuracy: 0.9966 - val_loss: 0.2056 - val_accuracy: 0.9400 Epoch 200/200 298/298 [==============================] - 0s 72us/step - loss: 0.0362 - accuracy: 1.0000 - val_loss: 0.2128 - val_accuracy: 0.9400 171/171 [==============================] - 0s 43us/step Optimizers: <keras.optimizers.Adam object at 0x10a1d9690> Epoch Sizes: 200 Neurons or Units: 128 ['loss', 'accuracy'] [0.08605057750529016, 0.9824561476707458] Test score: 0.08605057750529016 Test accuracy: 0.9824561476707458 Model: "sequential_54" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_160 (Dense) (None, 256) 7936 _________________________________________________________________ activation_160 (Activation) (None, 256) 0 _________________________________________________________________ dense_161 (Dense) (None, 256) 65792 _________________________________________________________________ activation_161 (Activation) (None, 256) 0 _________________________________________________________________ dense_162 (Dense) (None, 1) 257 _________________________________________________________________ activation_162 (Activation) (None, 1) 0 ================================================================= Total params: 73,985 Trainable params: 73,985 Non-trainable params: 0 _________________________________________________________________ Train on 298 samples, validate on 100 samples Epoch 1/200 298/298 [==============================] - 0s 817us/step - loss: 2.5271 - accuracy: 0.9161 - val_loss: 1.3423 - val_accuracy: 0.9400 Epoch 2/200 298/298 [==============================] - 0s 94us/step - loss: 0.7692 - accuracy: 0.9866 - val_loss: 0.4973 - val_accuracy: 0.9400 Epoch 3/200 298/298 [==============================] - 0s 98us/step - loss: 0.3287 - accuracy: 0.9933 - val_loss: 0.3407 - val_accuracy: 0.9500 Epoch 4/200 298/298 [==============================] - 0s 92us/step - loss: 0.1992 - accuracy: 0.9899 - val_loss: 0.2331 - val_accuracy: 0.9600 Epoch 5/200 298/298 [==============================] - 0s 98us/step - loss: 0.1323 - accuracy: 0.9966 - val_loss: 0.2200 - val_accuracy: 0.9500 Epoch 6/200 298/298 [==============================] - 0s 98us/step - loss: 0.1084 - accuracy: 0.9866 - val_loss: 0.2390 - val_accuracy: 0.9400 Epoch 7/200 298/298 [==============================] - 0s 99us/step - loss: 0.0983 - accuracy: 0.9933 - val_loss: 0.2197 - val_accuracy: 0.9600 Epoch 8/200 298/298 [==============================] - 0s 102us/step - loss: 0.0916 - accuracy: 0.9866 - val_loss: 0.1842 - val_accuracy: 0.9500 Epoch 9/200 298/298 [==============================] - 0s 101us/step - loss: 0.0835 - accuracy: 0.9866 - val_loss: 0.2280 - val_accuracy: 0.9500 Epoch 10/200 298/298 [==============================] - 0s 86us/step - loss: 0.0839 - accuracy: 0.9899 - val_loss: 0.2154 - val_accuracy: 0.9400 Epoch 11/200 298/298 [==============================] - 0s 83us/step - loss: 0.0890 - accuracy: 0.9832 - val_loss: 0.2156 - val_accuracy: 0.9500 Epoch 12/200 298/298 [==============================] - 0s 81us/step - loss: 0.0741 - accuracy: 0.9899 - val_loss: 0.1986 - val_accuracy: 0.9400 Epoch 13/200 298/298 [==============================] - 0s 88us/step - loss: 0.0716 - accuracy: 0.9933 - val_loss: 0.1867 - val_accuracy: 0.9600 Epoch 14/200 298/298 [==============================] - 0s 86us/step - loss: 0.0712 - accuracy: 0.9933 - val_loss: 0.1879 - val_accuracy: 0.9500 Epoch 15/200 298/298 [==============================] - 0s 90us/step - loss: 0.0655 - accuracy: 0.9933 - val_loss: 0.2026 - val_accuracy: 0.9300 Epoch 16/200 298/298 [==============================] - 0s 70us/step - loss: 0.0664 - accuracy: 0.9933 - val_loss: 0.2047 - val_accuracy: 0.9500 Epoch 17/200 298/298 [==============================] - 0s 83us/step - loss: 0.0693 - accuracy: 0.9933 - val_loss: 0.1942 - val_accuracy: 0.9500 Epoch 18/200 298/298 [==============================] - 0s 77us/step - loss: 0.0625 - accuracy: 0.9933 - val_loss: 0.2150 - val_accuracy: 0.9500 Epoch 19/200 298/298 [==============================] - 0s 80us/step - loss: 0.0669 - accuracy: 0.9899 - val_loss: 0.1894 - val_accuracy: 0.9600 Epoch 20/200 298/298 [==============================] - 0s 68us/step - loss: 0.0642 - accuracy: 0.9933 - val_loss: 0.2028 - val_accuracy: 0.9500 Epoch 21/200 298/298 [==============================] - 0s 81us/step - loss: 0.0618 - accuracy: 0.9933 - val_loss: 0.2324 - val_accuracy: 0.9600 Epoch 22/200 298/298 [==============================] - 0s 81us/step - loss: 0.0657 - accuracy: 0.9899 - val_loss: 0.2173 - val_accuracy: 0.9500 Epoch 23/200 298/298 [==============================] - 0s 78us/step - loss: 0.0698 - accuracy: 0.9899 - val_loss: 0.2426 - val_accuracy: 0.9500 Epoch 24/200 298/298 [==============================] - 0s 77us/step - loss: 0.0600 - accuracy: 0.9966 - val_loss: 0.2269 - val_accuracy: 0.9600 Epoch 25/200 298/298 [==============================] - 0s 79us/step - loss: 0.0628 - accuracy: 0.9899 - val_loss: 0.2474 - val_accuracy: 0.9500 Epoch 26/200 298/298 [==============================] - 0s 78us/step - loss: 0.0638 - accuracy: 0.9933 - val_loss: 0.2138 - val_accuracy: 0.9500 Epoch 27/200 298/298 [==============================] - 0s 70us/step - loss: 0.0719 - accuracy: 0.9866 - val_loss: 0.2265 - val_accuracy: 0.9500 Epoch 28/200 298/298 [==============================] - 0s 81us/step - loss: 0.0649 - accuracy: 0.9899 - val_loss: 0.2929 - val_accuracy: 0.9400 Epoch 29/200 298/298 [==============================] - 0s 83us/step - loss: 0.0602 - accuracy: 0.9899 - val_loss: 0.2165 - val_accuracy: 0.9400 Epoch 30/200 298/298 [==============================] - 0s 83us/step - loss: 0.0576 - accuracy: 0.9933 - val_loss: 0.1825 - val_accuracy: 0.9400 Epoch 31/200 298/298 [==============================] - 0s 80us/step - loss: 0.0681 - accuracy: 0.9866 - val_loss: 0.2681 - val_accuracy: 0.9400 Epoch 32/200 298/298 [==============================] - 0s 78us/step - loss: 0.0576 - accuracy: 0.9933 - val_loss: 0.2216 - val_accuracy: 0.9400 Epoch 33/200 298/298 [==============================] - 0s 84us/step - loss: 0.0585 - accuracy: 0.9933 - val_loss: 0.2099 - val_accuracy: 0.9400 Epoch 34/200 298/298 [==============================] - 0s 79us/step - loss: 0.0552 - accuracy: 0.9933 - val_loss: 0.2264 - val_accuracy: 0.9400 Epoch 35/200 298/298 [==============================] - 0s 72us/step - loss: 0.0566 - accuracy: 0.9933 - val_loss: 0.2131 - val_accuracy: 0.9500 Epoch 36/200 298/298 [==============================] - 0s 78us/step - loss: 0.0542 - accuracy: 0.9933 - val_loss: 0.2012 - val_accuracy: 0.9400 Epoch 37/200 298/298 [==============================] - 0s 79us/step - loss: 0.0554 - accuracy: 0.9933 - val_loss: 0.2307 - val_accuracy: 0.9500 Epoch 38/200 298/298 [==============================] - 0s 84us/step - loss: 0.0522 - accuracy: 0.9966 - val_loss: 0.1977 - val_accuracy: 0.9400 Epoch 39/200 298/298 [==============================] - 0s 92us/step - loss: 0.0520 - accuracy: 0.9933 - val_loss: 0.2155 - val_accuracy: 0.9500 Epoch 40/200 298/298 [==============================] - 0s 81us/step - loss: 0.0519 - accuracy: 0.9933 - val_loss: 0.2085 - val_accuracy: 0.9500 Epoch 41/200 298/298 [==============================] - 0s 88us/step - loss: 0.0591 - accuracy: 0.9899 - val_loss: 0.2270 - val_accuracy: 0.9400 Epoch 42/200 298/298 [==============================] - 0s 83us/step - loss: 0.0561 - accuracy: 0.9933 - val_loss: 0.2514 - val_accuracy: 0.9400 Epoch 43/200 298/298 [==============================] - 0s 90us/step - loss: 0.0515 - accuracy: 0.9933 - val_loss: 0.1998 - val_accuracy: 0.9500 Epoch 44/200 298/298 [==============================] - 0s 102us/step - loss: 0.0505 - accuracy: 0.9966 - val_loss: 0.2040 - val_accuracy: 0.9500 Epoch 45/200 298/298 [==============================] - 0s 101us/step - loss: 0.0483 - accuracy: 0.9966 - val_loss: 0.2201 - val_accuracy: 0.9400 Epoch 46/200 298/298 [==============================] - 0s 100us/step - loss: 0.0520 - accuracy: 0.9966 - val_loss: 0.2196 - val_accuracy: 0.9500 Epoch 47/200 298/298 [==============================] - 0s 94us/step - loss: 0.0509 - accuracy: 0.9933 - val_loss: 0.2057 - val_accuracy: 0.9400 Epoch 48/200 298/298 [==============================] - 0s 71us/step - loss: 0.0511 - accuracy: 0.9933 - val_loss: 0.2141 - val_accuracy: 0.9400 Epoch 49/200 298/298 [==============================] - 0s 82us/step - loss: 0.0500 - accuracy: 0.9899 - val_loss: 0.2185 - val_accuracy: 0.9400 Epoch 50/200 298/298 [==============================] - 0s 80us/step - loss: 0.0480 - accuracy: 0.9933 - val_loss: 0.2016 - val_accuracy: 0.9500 Epoch 51/200 298/298 [==============================] - 0s 79us/step - loss: 0.0509 - accuracy: 0.9933 - val_loss: 0.2188 - val_accuracy: 0.9500 Epoch 52/200 298/298 [==============================] - 0s 65us/step - loss: 0.0488 - accuracy: 0.9933 - val_loss: 0.2082 - val_accuracy: 0.9500 Epoch 53/200 298/298 [==============================] - 0s 75us/step - loss: 0.0473 - accuracy: 1.0000 - val_loss: 0.2181 - val_accuracy: 0.9500 Epoch 54/200 298/298 [==============================] - 0s 76us/step - loss: 0.0486 - accuracy: 0.9966 - val_loss: 0.2150 - val_accuracy: 0.9400 Epoch 55/200 298/298 [==============================] - 0s 68us/step - loss: 0.0466 - accuracy: 1.0000 - val_loss: 0.1925 - val_accuracy: 0.9400 Epoch 56/200 298/298 [==============================] - 0s 72us/step - loss: 0.0465 - accuracy: 0.9966 - val_loss: 0.2108 - val_accuracy: 0.9500 Epoch 57/200 298/298 [==============================] - 0s 75us/step - loss: 0.0482 - accuracy: 0.9933 - val_loss: 0.2162 - val_accuracy: 0.9500 Epoch 58/200 298/298 [==============================] - 0s 71us/step - loss: 0.0470 - accuracy: 1.0000 - val_loss: 0.2102 - val_accuracy: 0.9400 Epoch 59/200 298/298 [==============================] - 0s 77us/step - loss: 0.0657 - accuracy: 0.9866 - val_loss: 0.1971 - val_accuracy: 0.9500 Epoch 60/200 298/298 [==============================] - 0s 85us/step - loss: 0.0647 - accuracy: 0.9832 - val_loss: 0.2273 - val_accuracy: 0.9500 Epoch 61/200 298/298 [==============================] - 0s 82us/step - loss: 0.0804 - accuracy: 0.9799 - val_loss: 0.3522 - val_accuracy: 0.9500 Epoch 62/200 298/298 [==============================] - 0s 83us/step - loss: 0.0723 - accuracy: 0.9866 - val_loss: 0.3370 - val_accuracy: 0.9100 Epoch 63/200 298/298 [==============================] - 0s 76us/step - loss: 0.0564 - accuracy: 0.9899 - val_loss: 0.2505 - val_accuracy: 0.9300 Epoch 64/200 298/298 [==============================] - 0s 85us/step - loss: 0.0532 - accuracy: 0.9966 - val_loss: 0.1857 - val_accuracy: 0.9600 Epoch 65/200 298/298 [==============================] - 0s 80us/step - loss: 0.0497 - accuracy: 0.9933 - val_loss: 0.2529 - val_accuracy: 0.9400 Epoch 66/200 298/298 [==============================] - 0s 76us/step - loss: 0.0508 - accuracy: 0.9933 - val_loss: 0.2277 - val_accuracy: 0.9500 Epoch 67/200 298/298 [==============================] - 0s 88us/step - loss: 0.0494 - accuracy: 0.9966 - val_loss: 0.1973 - val_accuracy: 0.9500 Epoch 68/200 298/298 [==============================] - 0s 74us/step - loss: 0.0473 - accuracy: 1.0000 - val_loss: 0.2086 - val_accuracy: 0.9300 Epoch 69/200 298/298 [==============================] - 0s 83us/step - loss: 0.0456 - accuracy: 0.9966 - val_loss: 0.2134 - val_accuracy: 0.9400 Epoch 70/200 298/298 [==============================] - 0s 83us/step - loss: 0.0455 - accuracy: 0.9966 - val_loss: 0.2134 - val_accuracy: 0.9400 Epoch 71/200 298/298 [==============================] - 0s 69us/step - loss: 0.0445 - accuracy: 0.9966 - val_loss: 0.2206 - val_accuracy: 0.9400 Epoch 72/200 298/298 [==============================] - 0s 91us/step - loss: 0.0441 - accuracy: 1.0000 - val_loss: 0.2127 - val_accuracy: 0.9400 Epoch 73/200 298/298 [==============================] - 0s 81us/step - loss: 0.0443 - accuracy: 1.0000 - val_loss: 0.2097 - val_accuracy: 0.9400 Epoch 74/200 298/298 [==============================] - 0s 82us/step - loss: 0.0440 - accuracy: 0.9966 - val_loss: 0.2007 - val_accuracy: 0.9400 Epoch 75/200 298/298 [==============================] - 0s 73us/step - loss: 0.0457 - accuracy: 0.9966 - val_loss: 0.1971 - val_accuracy: 0.9500 Epoch 76/200 298/298 [==============================] - 0s 75us/step - loss: 0.0434 - accuracy: 1.0000 - val_loss: 0.2167 - val_accuracy: 0.9400 Epoch 77/200 298/298 [==============================] - 0s 77us/step - loss: 0.0441 - accuracy: 1.0000 - val_loss: 0.1935 - val_accuracy: 0.9500 Epoch 78/200 298/298 [==============================] - 0s 84us/step - loss: 0.0445 - accuracy: 0.9933 - val_loss: 0.1981 - val_accuracy: 0.9400 Epoch 79/200 298/298 [==============================] - 0s 79us/step - loss: 0.0524 - accuracy: 0.9933 - val_loss: 0.2365 - val_accuracy: 0.9500 Epoch 80/200 298/298 [==============================] - 0s 74us/step - loss: 0.0471 - accuracy: 0.9933 - val_loss: 0.2402 - val_accuracy: 0.9400 Epoch 81/200 298/298 [==============================] - 0s 88us/step - loss: 0.0454 - accuracy: 0.9966 - val_loss: 0.2308 - val_accuracy: 0.9200 Epoch 82/200 298/298 [==============================] - 0s 84us/step - loss: 0.0475 - accuracy: 0.9966 - val_loss: 0.2339 - val_accuracy: 0.9400 Epoch 83/200 298/298 [==============================] - 0s 77us/step - loss: 0.0427 - accuracy: 1.0000 - val_loss: 0.2221 - val_accuracy: 0.9500 Epoch 84/200 298/298 [==============================] - 0s 82us/step - loss: 0.0433 - accuracy: 1.0000 - val_loss: 0.2195 - val_accuracy: 0.9400 Epoch 85/200 298/298 [==============================] - 0s 70us/step - loss: 0.0429 - accuracy: 1.0000 - val_loss: 0.2191 - val_accuracy: 0.9400 Epoch 86/200 298/298 [==============================] - 0s 83us/step - loss: 0.0431 - accuracy: 0.9966 - val_loss: 0.2104 - val_accuracy: 0.9400 Epoch 87/200 298/298 [==============================] - 0s 84us/step - loss: 0.0430 - accuracy: 1.0000 - val_loss: 0.2021 - val_accuracy: 0.9300 Epoch 88/200 298/298 [==============================] - 0s 77us/step - loss: 0.0432 - accuracy: 1.0000 - val_loss: 0.2264 - val_accuracy: 0.9400 Epoch 89/200 298/298 [==============================] - 0s 87us/step - loss: 0.0417 - accuracy: 1.0000 - val_loss: 0.2184 - val_accuracy: 0.9500 Epoch 90/200 298/298 [==============================] - 0s 83us/step - loss: 0.0413 - accuracy: 1.0000 - val_loss: 0.2166 - val_accuracy: 0.9500 Epoch 91/200 298/298 [==============================] - 0s 78us/step - loss: 0.0408 - accuracy: 1.0000 - val_loss: 0.2092 - val_accuracy: 0.9400 Epoch 92/200 298/298 [==============================] - 0s 66us/step - loss: 0.0416 - accuracy: 0.9966 - val_loss: 0.2043 - val_accuracy: 0.9400 Epoch 93/200 298/298 [==============================] - 0s 81us/step - loss: 0.0412 - accuracy: 1.0000 - val_loss: 0.2006 - val_accuracy: 0.9400 Epoch 94/200 298/298 [==============================] - 0s 79us/step - loss: 0.0417 - accuracy: 1.0000 - val_loss: 0.2114 - val_accuracy: 0.9400 Epoch 95/200 298/298 [==============================] - 0s 72us/step - loss: 0.0406 - accuracy: 1.0000 - val_loss: 0.2100 - val_accuracy: 0.9500 Epoch 96/200 298/298 [==============================] - 0s 80us/step - loss: 0.0411 - accuracy: 1.0000 - val_loss: 0.2045 - val_accuracy: 0.9400 Epoch 97/200 298/298 [==============================] - 0s 75us/step - loss: 0.0402 - accuracy: 1.0000 - val_loss: 0.2031 - val_accuracy: 0.9300 Epoch 98/200 298/298 [==============================] - 0s 76us/step - loss: 0.0397 - accuracy: 1.0000 - val_loss: 0.2014 - val_accuracy: 0.9400 Epoch 99/200 298/298 [==============================] - 0s 66us/step - loss: 0.0409 - accuracy: 0.9966 - val_loss: 0.2061 - val_accuracy: 0.9400 Epoch 100/200 298/298 [==============================] - 0s 76us/step - loss: 0.0408 - accuracy: 1.0000 - val_loss: 0.2032 - val_accuracy: 0.9500 Epoch 101/200 298/298 [==============================] - 0s 77us/step - loss: 0.0395 - accuracy: 1.0000 - val_loss: 0.2075 - val_accuracy: 0.9400 Epoch 102/200 298/298 [==============================] - 0s 73us/step - loss: 0.0403 - accuracy: 1.0000 - val_loss: 0.1785 - val_accuracy: 0.9600 Epoch 103/200 298/298 [==============================] - 0s 76us/step - loss: 0.0404 - accuracy: 1.0000 - val_loss: 0.2104 - val_accuracy: 0.9500 Epoch 104/200 298/298 [==============================] - 0s 73us/step - loss: 0.0397 - accuracy: 1.0000 - val_loss: 0.2250 - val_accuracy: 0.9400 Epoch 105/200 298/298 [==============================] - 0s 77us/step - loss: 0.0394 - accuracy: 1.0000 - val_loss: 0.2363 - val_accuracy: 0.9300 Epoch 106/200 298/298 [==============================] - 0s 64us/step - loss: 0.0397 - accuracy: 0.9966 - val_loss: 0.2291 - val_accuracy: 0.9400 Epoch 107/200 298/298 [==============================] - 0s 77us/step - loss: 0.0408 - accuracy: 0.9966 - val_loss: 0.2275 - val_accuracy: 0.9400 Epoch 108/200 298/298 [==============================] - 0s 75us/step - loss: 0.0401 - accuracy: 1.0000 - val_loss: 0.2259 - val_accuracy: 0.9500 Epoch 109/200 298/298 [==============================] - 0s 66us/step - loss: 0.0418 - accuracy: 1.0000 - val_loss: 0.1796 - val_accuracy: 0.9600 Epoch 110/200 298/298 [==============================] - 0s 79us/step - loss: 0.0398 - accuracy: 1.0000 - val_loss: 0.1809 - val_accuracy: 0.9600 Epoch 111/200 298/298 [==============================] - 0s 75us/step - loss: 0.0391 - accuracy: 1.0000 - val_loss: 0.2058 - val_accuracy: 0.9400 Epoch 112/200 298/298 [==============================] - 0s 75us/step - loss: 0.0390 - accuracy: 0.9966 - val_loss: 0.2043 - val_accuracy: 0.9400 Epoch 113/200 298/298 [==============================] - 0s 69us/step - loss: 0.0385 - accuracy: 1.0000 - val_loss: 0.2121 - val_accuracy: 0.9400 Epoch 114/200 298/298 [==============================] - 0s 79us/step - loss: 0.0401 - accuracy: 1.0000 - val_loss: 0.2062 - val_accuracy: 0.9400 Epoch 115/200 298/298 [==============================] - 0s 77us/step - loss: 0.0391 - accuracy: 0.9966 - val_loss: 0.2088 - val_accuracy: 0.9500 Epoch 116/200 298/298 [==============================] - 0s 68us/step - loss: 0.0378 - accuracy: 1.0000 - val_loss: 0.2131 - val_accuracy: 0.9400 Epoch 117/200 298/298 [==============================] - 0s 77us/step - loss: 0.0391 - accuracy: 1.0000 - val_loss: 0.2174 - val_accuracy: 0.9300 Epoch 118/200 298/298 [==============================] - 0s 78us/step - loss: 0.0388 - accuracy: 0.9966 - val_loss: 0.2062 - val_accuracy: 0.9400 Epoch 119/200 298/298 [==============================] - 0s 77us/step - loss: 0.0383 - accuracy: 1.0000 - val_loss: 0.2128 - val_accuracy: 0.9500 Epoch 120/200 298/298 [==============================] - 0s 68us/step - loss: 0.0381 - accuracy: 1.0000 - val_loss: 0.2146 - val_accuracy: 0.9500 Epoch 121/200 298/298 [==============================] - 0s 76us/step - loss: 0.0405 - accuracy: 0.9966 - val_loss: 0.1949 - val_accuracy: 0.9500 Epoch 122/200 298/298 [==============================] - 0s 74us/step - loss: 0.0410 - accuracy: 0.9966 - val_loss: 0.2078 - val_accuracy: 0.9400 Epoch 123/200 298/298 [==============================] - 0s 76us/step - loss: 0.0385 - accuracy: 1.0000 - val_loss: 0.2152 - val_accuracy: 0.9500 Epoch 124/200 298/298 [==============================] - 0s 77us/step - loss: 0.0379 - accuracy: 1.0000 - val_loss: 0.2128 - val_accuracy: 0.9500 Epoch 125/200 298/298 [==============================] - 0s 74us/step - loss: 0.0384 - accuracy: 1.0000 - val_loss: 0.2086 - val_accuracy: 0.9500 Epoch 126/200 298/298 [==============================] - 0s 71us/step - loss: 0.0396 - accuracy: 0.9966 - val_loss: 0.2048 - val_accuracy: 0.9400 Epoch 127/200 298/298 [==============================] - 0s 73us/step - loss: 0.0399 - accuracy: 1.0000 - val_loss: 0.2073 - val_accuracy: 0.9400 Epoch 128/200 298/298 [==============================] - 0s 77us/step - loss: 0.0368 - accuracy: 1.0000 - val_loss: 0.2184 - val_accuracy: 0.9400 Epoch 129/200 298/298 [==============================] - 0s 79us/step - loss: 0.0375 - accuracy: 0.9966 - val_loss: 0.1994 - val_accuracy: 0.9400 Epoch 130/200 298/298 [==============================] - 0s 66us/step - loss: 0.0371 - accuracy: 1.0000 - val_loss: 0.2007 - val_accuracy: 0.9400 Epoch 131/200 298/298 [==============================] - 0s 73us/step - loss: 0.0362 - accuracy: 1.0000 - val_loss: 0.2077 - val_accuracy: 0.9300 Epoch 132/200 298/298 [==============================] - 0s 75us/step - loss: 0.0368 - accuracy: 1.0000 - val_loss: 0.2008 - val_accuracy: 0.9300 Epoch 133/200 298/298 [==============================] - 0s 66us/step - loss: 0.0364 - accuracy: 1.0000 - val_loss: 0.2221 - val_accuracy: 0.9400 Epoch 134/200 298/298 [==============================] - 0s 78us/step - loss: 0.0364 - accuracy: 1.0000 - val_loss: 0.2094 - val_accuracy: 0.9400 Epoch 135/200 298/298 [==============================] - 0s 75us/step - loss: 0.0368 - accuracy: 1.0000 - val_loss: 0.2080 - val_accuracy: 0.9300 Epoch 136/200 298/298 [==============================] - 0s 73us/step - loss: 0.0365 - accuracy: 1.0000 - val_loss: 0.2132 - val_accuracy: 0.9300 Epoch 137/200 298/298 [==============================] - 0s 69us/step - loss: 0.0374 - accuracy: 0.9966 - val_loss: 0.2025 - val_accuracy: 0.9400 Epoch 138/200 298/298 [==============================] - 0s 78us/step - loss: 0.0387 - accuracy: 1.0000 - val_loss: 0.2067 - val_accuracy: 0.9400 Epoch 139/200 298/298 [==============================] - 0s 76us/step - loss: 0.0373 - accuracy: 1.0000 - val_loss: 0.2083 - val_accuracy: 0.9400 Epoch 140/200 298/298 [==============================] - 0s 70us/step - loss: 0.0363 - accuracy: 1.0000 - val_loss: 0.2077 - val_accuracy: 0.9300 Epoch 141/200 298/298 [==============================] - 0s 82us/step - loss: 0.0360 - accuracy: 1.0000 - val_loss: 0.2224 - val_accuracy: 0.9300 Epoch 142/200 298/298 [==============================] - 0s 79us/step - loss: 0.0372 - accuracy: 0.9966 - val_loss: 0.2077 - val_accuracy: 0.9400 Epoch 143/200 298/298 [==============================] - 0s 69us/step - loss: 0.0378 - accuracy: 1.0000 - val_loss: 0.2103 - val_accuracy: 0.9400 Epoch 144/200 298/298 [==============================] - 0s 84us/step - loss: 0.0392 - accuracy: 0.9966 - val_loss: 0.2295 - val_accuracy: 0.9400 Epoch 145/200 298/298 [==============================] - 0s 78us/step - loss: 0.0380 - accuracy: 0.9966 - val_loss: 0.2117 - val_accuracy: 0.9300 Epoch 146/200 298/298 [==============================] - 0s 78us/step - loss: 0.0357 - accuracy: 1.0000 - val_loss: 0.2051 - val_accuracy: 0.9600 Epoch 147/200 298/298 [==============================] - 0s 67us/step - loss: 0.0360 - accuracy: 1.0000 - val_loss: 0.2024 - val_accuracy: 0.9500 Epoch 148/200 298/298 [==============================] - 0s 78us/step - loss: 0.0360 - accuracy: 1.0000 - val_loss: 0.2122 - val_accuracy: 0.9400 Epoch 149/200 298/298 [==============================] - 0s 79us/step - loss: 0.0355 - accuracy: 1.0000 - val_loss: 0.2076 - val_accuracy: 0.9400 Epoch 150/200 298/298 [==============================] - 0s 79us/step - loss: 0.0353 - accuracy: 1.0000 - val_loss: 0.2101 - val_accuracy: 0.9400 Epoch 151/200 298/298 [==============================] - 0s 68us/step - loss: 0.0354 - accuracy: 1.0000 - val_loss: 0.2158 - val_accuracy: 0.9300 Epoch 152/200 298/298 [==============================] - 0s 79us/step - loss: 0.0352 - accuracy: 1.0000 - val_loss: 0.2084 - val_accuracy: 0.9400 Epoch 153/200 298/298 [==============================] - 0s 86us/step - loss: 0.0352 - accuracy: 0.9966 - val_loss: 0.1881 - val_accuracy: 0.9500 Epoch 154/200 298/298 [==============================] - 0s 90us/step - loss: 0.0349 - accuracy: 1.0000 - val_loss: 0.2008 - val_accuracy: 0.9300 Epoch 155/200 298/298 [==============================] - 0s 84us/step - loss: 0.0351 - accuracy: 1.0000 - val_loss: 0.2051 - val_accuracy: 0.9500 Epoch 156/200 298/298 [==============================] - 0s 96us/step - loss: 0.0367 - accuracy: 0.9966 - val_loss: 0.1944 - val_accuracy: 0.9500 Epoch 157/200 298/298 [==============================] - 0s 106us/step - loss: 0.0353 - accuracy: 1.0000 - val_loss: 0.1986 - val_accuracy: 0.9500 Epoch 158/200 298/298 [==============================] - 0s 114us/step - loss: 0.0355 - accuracy: 0.9966 - val_loss: 0.2121 - val_accuracy: 0.9400 Epoch 159/200 298/298 [==============================] - 0s 98us/step - loss: 0.0349 - accuracy: 1.0000 - val_loss: 0.2014 - val_accuracy: 0.9500 Epoch 160/200 298/298 [==============================] - 0s 118us/step - loss: 0.0351 - accuracy: 1.0000 - val_loss: 0.2130 - val_accuracy: 0.9500 Epoch 161/200 298/298 [==============================] - 0s 132us/step - loss: 0.0348 - accuracy: 1.0000 - val_loss: 0.2061 - val_accuracy: 0.9400 Epoch 162/200 298/298 [==============================] - 0s 110us/step - loss: 0.0344 - accuracy: 1.0000 - val_loss: 0.2026 - val_accuracy: 0.9400 Epoch 163/200 298/298 [==============================] - 0s 115us/step - loss: 0.0344 - accuracy: 1.0000 - val_loss: 0.2147 - val_accuracy: 0.9400 Epoch 164/200 298/298 [==============================] - 0s 110us/step - loss: 0.0347 - accuracy: 1.0000 - val_loss: 0.2046 - val_accuracy: 0.9400 Epoch 165/200 298/298 [==============================] - 0s 108us/step - loss: 0.0363 - accuracy: 0.9966 - val_loss: 0.1941 - val_accuracy: 0.9500 Epoch 166/200 298/298 [==============================] - 0s 109us/step - loss: 0.0366 - accuracy: 0.9966 - val_loss: 0.2106 - val_accuracy: 0.9400 Epoch 167/200 298/298 [==============================] - 0s 109us/step - loss: 0.0379 - accuracy: 0.9966 - val_loss: 0.2015 - val_accuracy: 0.9500 Epoch 168/200 298/298 [==============================] - 0s 109us/step - loss: 0.0367 - accuracy: 0.9966 - val_loss: 0.2074 - val_accuracy: 0.9600 Epoch 169/200 298/298 [==============================] - 0s 107us/step - loss: 0.0347 - accuracy: 1.0000 - val_loss: 0.2349 - val_accuracy: 0.9500 Epoch 170/200 298/298 [==============================] - 0s 103us/step - loss: 0.0363 - accuracy: 1.0000 - val_loss: 0.1953 - val_accuracy: 0.9500 Epoch 171/200 298/298 [==============================] - 0s 105us/step - loss: 0.0356 - accuracy: 1.0000 - val_loss: 0.2126 - val_accuracy: 0.9300 Epoch 172/200 298/298 [==============================] - 0s 111us/step - loss: 0.0351 - accuracy: 0.9966 - val_loss: 0.2150 - val_accuracy: 0.9300 Epoch 173/200 298/298 [==============================] - 0s 111us/step - loss: 0.0339 - accuracy: 1.0000 - val_loss: 0.2059 - val_accuracy: 0.9300 Epoch 174/200 298/298 [==============================] - 0s 117us/step - loss: 0.0335 - accuracy: 1.0000 - val_loss: 0.1996 - val_accuracy: 0.9400 Epoch 175/200 298/298 [==============================] - 0s 113us/step - loss: 0.0333 - accuracy: 1.0000 - val_loss: 0.2017 - val_accuracy: 0.9400 Epoch 176/200 298/298 [==============================] - 0s 104us/step - loss: 0.0338 - accuracy: 1.0000 - val_loss: 0.2020 - val_accuracy: 0.9400 Epoch 177/200 298/298 [==============================] - 0s 121us/step - loss: 0.0353 - accuracy: 1.0000 - val_loss: 0.2111 - val_accuracy: 0.9500 Epoch 178/200 298/298 [==============================] - 0s 101us/step - loss: 0.0338 - accuracy: 1.0000 - val_loss: 0.2029 - val_accuracy: 0.9500 Epoch 179/200 298/298 [==============================] - 0s 120us/step - loss: 0.0333 - accuracy: 1.0000 - val_loss: 0.1976 - val_accuracy: 0.9500 Epoch 180/200 298/298 [==============================] - 0s 84us/step - loss: 0.0336 - accuracy: 1.0000 - val_loss: 0.2120 - val_accuracy: 0.9500 Epoch 181/200 298/298 [==============================] - 0s 83us/step - loss: 0.0347 - accuracy: 0.9966 - val_loss: 0.1971 - val_accuracy: 0.9500 Epoch 182/200 298/298 [==============================] - 0s 80us/step - loss: 0.0355 - accuracy: 1.0000 - val_loss: 0.2164 - val_accuracy: 0.9500 Epoch 183/200 298/298 [==============================] - 0s 77us/step - loss: 0.0351 - accuracy: 0.9966 - val_loss: 0.1984 - val_accuracy: 0.9500 Epoch 184/200 298/298 [==============================] - 0s 68us/step - loss: 0.0364 - accuracy: 0.9966 - val_loss: 0.2191 - val_accuracy: 0.9400 Epoch 185/200 298/298 [==============================] - 0s 77us/step - loss: 0.0385 - accuracy: 0.9966 - val_loss: 0.2128 - val_accuracy: 0.9400 Epoch 186/200 298/298 [==============================] - 0s 77us/step - loss: 0.0374 - accuracy: 0.9966 - val_loss: 0.2103 - val_accuracy: 0.9400 Epoch 187/200 298/298 [==============================] - 0s 71us/step - loss: 0.0345 - accuracy: 0.9966 - val_loss: 0.2286 - val_accuracy: 0.9400 Epoch 188/200 298/298 [==============================] - 0s 72us/step - loss: 0.0327 - accuracy: 1.0000 - val_loss: 0.2172 - val_accuracy: 0.9300 Epoch 189/200 298/298 [==============================] - 0s 78us/step - loss: 0.0351 - accuracy: 1.0000 - val_loss: 0.2125 - val_accuracy: 0.9500 Epoch 190/200 298/298 [==============================] - 0s 73us/step - loss: 0.0335 - accuracy: 1.0000 - val_loss: 0.2094 - val_accuracy: 0.9300 Epoch 191/200 298/298 [==============================] - 0s 70us/step - loss: 0.0332 - accuracy: 1.0000 - val_loss: 0.2051 - val_accuracy: 0.9400 Epoch 192/200 298/298 [==============================] - 0s 77us/step - loss: 0.0333 - accuracy: 0.9966 - val_loss: 0.2037 - val_accuracy: 0.9300 Epoch 193/200 298/298 [==============================] - 0s 80us/step - loss: 0.0401 - accuracy: 0.9933 - val_loss: 0.2439 - val_accuracy: 0.9400 Epoch 194/200 298/298 [==============================] - 0s 73us/step - loss: 0.0349 - accuracy: 1.0000 - val_loss: 0.2169 - val_accuracy: 0.9400 Epoch 195/200 298/298 [==============================] - 0s 72us/step - loss: 0.0330 - accuracy: 1.0000 - val_loss: 0.2031 - val_accuracy: 0.9500 Epoch 196/200 298/298 [==============================] - 0s 79us/step - loss: 0.0324 - accuracy: 1.0000 - val_loss: 0.2064 - val_accuracy: 0.9400 Epoch 197/200 298/298 [==============================] - 0s 84us/step - loss: 0.0325 - accuracy: 1.0000 - val_loss: 0.1986 - val_accuracy: 0.9500 Epoch 198/200 298/298 [==============================] - 0s 77us/step - loss: 0.0323 - accuracy: 1.0000 - val_loss: 0.2107 - val_accuracy: 0.9300 Epoch 199/200 298/298 [==============================] - 0s 102us/step - loss: 0.0328 - accuracy: 1.0000 - val_loss: 0.2002 - val_accuracy: 0.9500 Epoch 200/200 298/298 [==============================] - 0s 102us/step - loss: 0.0330 - accuracy: 1.0000 - val_loss: 0.1991 - val_accuracy: 0.9500 171/171 [==============================] - 0s 48us/step Optimizers: <keras.optimizers.Adam object at 0x10a1d9690> Epoch Sizes: 200 Neurons or Units: 256 ['loss', 'accuracy'] [0.08704098192049049, 0.9766082167625427] Test score: 0.08704098192049049 Test accuracy: 0.9766082167625427 [0.9590643048286438, 0.9649122953414917, 0.9590643048286438, 0.9824561476707458, 0.988304078578949, 0.988304078578949, 0.9766082167625427, 0.988304078578949, 0.988304078578949, 0.988304078578949, 0.988304078578949, 0.9707602262496948, 0.9941520690917969, 0.9824561476707458, 0.988304078578949, 0.988304078578949, 0.9824561476707458, 0.9824561476707458, 0.988304078578949, 0.988304078578949, 0.988304078578949, 0.988304078578949, 0.988304078578949, 0.9824561476707458, 0.988304078578949, 0.9824561476707458, 0.9766082167625427]
Execution Time 73.13525581359863 seconds: