Now that we've covered the individual pieces, let's take a look at our overall network. This looks similar to the models we've previously covered in the book. However, we're using the loss function categorical_crossentropy, which we covered in the Cost function section of this chapter.
We will define our network using the following code:
def build_network(input_features=None): # first we specify an input layer, with a shape == features inputs = Input(shape=(input_features,), name="input") x = Dense(512, activation='relu', name="hidden1")(inputs) x = Dense(256, activation='relu', name="hidden2")(x) x = Dense(128, activation='relu', name="hidden3")(x) prediction = Dense(10, activation='softmax', name="output")(x)
