Advanced Deep Learning with Keras

Introducing Advanced Deep Learning with Keras
Listing 1.3.2 shows the model summary of the proposed network. It requires
a total of 269,322 parameters. This is substantial considering that we have a simple
task of classifying MNIST digits. MLPs are not parameter efficient. The number of
parameters can be computed from Figure 1.3.4 by focusing on how the output of the
perceptron is computed. From input to Dense layer: 784 × 256 + 256 = 200,960. From
first Dense to second Dense: 256 × 256 + 256 = 65,792. From second Dense to the
output layer: 10 × 256 + 10 = 2,570. The total is 269,322.
Listing 1.3.2 shows a summary of an MLP MNIST digit classifier model:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_1 (Dense) (None, 256) 200960
_________________________________________________________________
activation_1 (Activation) (None, 256) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 256) 0
_________________________________________________________________
dense_2 (Dense) (None, 256) 65792
_________________________________________________________________
activation_2 (Activation) (None, 256) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 256) 0
_________________________________________________________________
dense_3 (Dense) (None, 10) 2570
_________________________________________________________________
activation_3 (Activation) (None, 10) 0
=================================================================
Total params: 269,322
Trainable params: 269,322
Non-trainable params: 0
Another way of verifying the network is by calling:
plot_model(model, to_file='mlp-mnist.png', show_shapes=True)
Figure 1.3.9 shows the plot. You'll find that this is similar to the results of summary()
but graphically shows the interconnection and I/O of each layer.
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