# ignore warnings
import warnings
warnings.filterwarnings("ignore")
from pandas import read_csv
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from keras.optimizers import SGD
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
# load dataset
dataframe = read_csv("sonar.mines.data.csv", header=None)
dataset = dataframe.values
# split into input (X) and output (Y) variables
X = dataset[:,0:60].astype(float)
Y = dataset[:,60]
# encode class values as integers
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)
# baseline
def create_baseline():
# create model
model = Sequential()
model.add(Dense(60, input_dim=60, activation='relu'))
model.add(Dense(30, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# Compile model
sgd = SGD(lr=0.01, momentum=0.8)
model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])
return model
estimators = []
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasClassifier(build_fn=create_baseline, epochs=300, batch_size=16, verbose=0)))
pipeline = Pipeline(estimators)
kfold = StratifiedKFold(n_splits=10, shuffle=True)
results = cross_val_score(pipeline, X, encoded_Y, cv=kfold)
print(); print("Accuracy Results: ")
print("Baseline: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))