Hits: 4 TensorFlow Neural Network Tutorial in Python TensorFlow is an open-source library for machine learning applications. It’s the Google Brain’s second generation system, after replacing the close-sourced DistBelief, and is used by Google for both research and production applications. TensorFlow applications can be written in a few languages: Python, Go, Java and C. This …
Hits: 1 TensorFlow: Save and Restore Models in Python Training a deep neural network model could take quite some time, depending on the complexity of your model, the amount of data you have, the hardware you’re running your models on, etc. On most of the occasions you’ll need to save your progress to a file, …
Hits: 4 Linear Regression Using Tensorflow Brief Summary of Linear Regression Linear Regression is a very common statistical method that allows us to learn a function or relationship from a given set of continuous data. For example, we are given some data points of x and corresponding y and we need to learn the relationship between them that …
Hits: 4 k-Fold Cross-Validating Neural Networks If we have smaller data it can be useful to benefit from k-fold cross-validation to maximize our ability to evaluate the neural network’s performance. This is possible in Keras because we can “wrap” any neural network such that it can use the evaluation features available in scikit-learn, including k-fold …
Hits: 7 Visualize Performance History Preliminaries /* Load libraries */ import numpy as np from keras.datasets import imdb from keras.preprocessing.text import Tokenizer from keras import models from keras import layers import matplotlib.pyplot as plt /* Set random seed */ np.random.seed(0) Using TensorFlow backend. Load Movie Review Data /* Set the number of features we want …
Hits: 9 Visualize Neural Network Architecture Preliminaries /* Load libraries */ from keras import models from keras import layers from IPython.display import SVG from keras.utils.vis_utils import model_to_dot from keras.utils import plot_model Using TensorFlow backend. Construct Neural Network Architecture /* Start neural network */ network = models.Sequential() /* Add fully connected layer with a ReLU activation …
Hits: 8 Visualize Loss History Preliminaries /* Load libraries */ import numpy as np from keras.datasets import imdb from keras.preprocessing.text import Tokenizer from keras import models from keras import layers import matplotlib.pyplot as plt /* Set random seed */ np.random.seed(0) Using TensorFlow backend. Load Movie Review Data /* Set the number of features we want …
Hits: 9 Tuning Neural Network Hyperparameters Preliminaries /* Load libraries */ import numpy as np from keras import models from keras import layers from keras.wrappers.scikit_learn import KerasClassifier from sklearn.model_selection import GridSearchCV from sklearn.datasets import make_classification /* Set random seed */ np.random.seed(0) Using TensorFlow backend. Generate Target And Feature Data /* Number of features */ number_of_features …
Hits: 3 Save Model Training Progress Preliminaries /* Load libraries */ import numpy as np from keras.datasets import imdb from keras.preprocessing.text import Tokenizer from keras import models from keras import layers from keras.callbacks import ModelCheckpoint #/* Set random seed */ np.random.seed(0) Load IMDB Movie Review Data /* Set the number of features we want */ …
Hits: 3 Preprocessing Data For Neural Networks Typically, a neural network’s parameters are initialized (i.e. created) as small random numbers. Neural networks often behave poorly when the feature values much larger than parameter values. Furthermore, since an observation’s feature values will are combined as they pass through individual units, it is important that all features …
Hits: 2 Neural Network Weight Regularization Preliminaries /* Load libraries */ import numpy as np from keras.datasets import imdb from keras.preprocessing.text import Tokenizer from keras import models from keras import layers from keras import regularizers /* Set random seed */ np.random.seed(0) Using TensorFlow backend. Load Movie Review Text Data /* Set the number of features …
Hits: 3 Neural Network Early Stopping Preliminaries /* Load libraries */ import numpy as np from keras.datasets import imdb from keras.preprocessing.text import Tokenizer from keras import models from keras import layers from keras.callbacks import EarlyStopping, ModelCheckpoint /* Set random seed */ np.random.seed(0) Using TensorFlow backend. Load Movie Review Text Data /* Set the number of …
Hits: 8 LSTM Recurrent Neural Network Oftentimes we have text data that we want to classify. While it is possible to use a type of convolutional network, we are going to focus on a more popular option: the recurrent neural network. The key feature of recurrent neural networks is that information loops back in the …
Hits: 4 Feedforward Neural Network For Multiclass Classification Preliminaries /* Load libraries */ import numpy as np from keras.datasets import reuters from keras.utils.np_utils import to_categorical from keras.preprocessing.text import Tokenizer from keras import models from keras import layers /* Set random seed */ np.random.seed(0) Using TensorFlow backend. Load Movie Review Data /* Set the number of …
Hits: 1 Convolutional Neural Network Preliminaries import numpy as np from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten from keras.layers.convolutional import Conv2D, MaxPooling2D from keras.utils import np_utils from keras import backend as K /* Set that the color channel value will be first */ K.set_image_data_format(‘channels_first’) /* Set seed */ …
Hits: 4 Adding Dropout Preliminaries /* Load libraries */ import numpy as np from keras.datasets import imdb from keras.preprocessing.text import Tokenizer from keras import models from keras import layers /* Set random seed */ np.random.seed(0) Using TensorFlow backend. Load IMDB Movie Review Data /* Set the number of features we want */ number_of_features = 1000 …