Where do I call the BatchNormalization function in Keras?
Just to answer this question in a little more detail, and as Pavel said, Batch Normalization is just another layer, so you can use it as such to create your desired network architecture.
The general use case is to use BN between the linear and non-linear layers in your network, because it normalizes the input to your activation function, so that you're centered in the linear section of the activation function (such as Sigmoid). There's a small discussion of it here
In your case above, this might look like:
# import BatchNormalization
from keras.layers.normalization import BatchNormalization
# instantiate model
model = Sequential()
# we can think of this chunk as the input layer
model.add(Dense(64, input_dim=14, init='uniform'))
model.add(BatchNormalization())
model.add(Activation('tanh'))
model.add(Dropout(0.5))
# we can think of this chunk as the hidden layer
model.add(Dense(64, init='uniform'))
model.add(BatchNormalization())
model.add(Activation('tanh'))
model.add(Dropout(0.5))
# we can think of this chunk as the output layer
model.add(Dense(2, init='uniform'))
model.add(BatchNormalization())
model.add(Activation('softmax'))
# setting up the optimization of our weights
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='binary_crossentropy', optimizer=sgd)
# running the fitting
model.fit(X_train, y_train, nb_epoch=20, batch_size=16, show_accuracy=True, validation_split=0.2, verbose = 2)
Hope this clarifies things a bit more.
This thread is misleading. Tried commenting on Lucas Ramadan's answer, but I don't have the right privileges yet, so I'll just put this here.
Batch normalization works best after the activation function, and here or here is why: it was developed to prevent internal covariate shift. Internal covariate shift occurs when the distribution of the activations of a layer shifts significantly throughout training. Batch normalization is used so that the distribution of the inputs (and these inputs are literally the result of an activation function) to a specific layer doesn't change over time due to parameter updates from each batch (or at least, allows it to change in an advantageous way). It uses batch statistics to do the normalizing, and then uses the batch normalization parameters (gamma and beta in the original paper) "to make sure that the transformation inserted in the network can represent the identity transform" (quote from original paper). But the point is that we're trying to normalize the inputs to a layer, so it should always go immediately before the next layer in the network. Whether or not that's after an activation function is dependent on the architecture in question.
This thread has some considerable debate about whether BN should be applied before non-linearity of current layer or to the activations of the previous layer.
Although there is no correct answer, the authors of Batch Normalization say that It should be applied immediately before the non-linearity of the current layer. The reason ( quoted from original paper) -
"We add the BN transform immediately before the nonlinearity, by normalizing x = Wu+b. We could have also normalized the layer inputs u, but since u is likely the output of another nonlinearity, the shape of its distribution is likely to change during training, and constraining its first and second moments would not eliminate the covariate shift. In contrast, Wu + b is more likely to have a symmetric, non-sparse distribution, that is “more Gaussian” (Hyv¨arinen & Oja, 2000); normalizing it is likely to produce activations with a stable distribution."
It's almost become a trend now to have a Conv2D followed by a ReLu followed by a BatchNormalization layer. So I made up a small function to call all of them at once. Makes the model definition look a whole lot cleaner and easier to read.
def Conv2DReluBatchNorm(n_filter, w_filter, h_filter, inputs):
return BatchNormalization()(Activation(activation='relu')(Convolution2D(n_filter, w_filter, h_filter, border_mode='same')(inputs)))