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.