Advanced Deep Learning with Keras

Deep Neural Networks
Everything else after the concatenate layer will remain the same as the previous
CNN model. That is Flatten-Dropout-Dense:
Figure 2.1.1: The Y-Network accepts the same input twice but processes the input in two branches
of convolutional networks. The outputs of the branches are combined using the concatenate layer.
The last layer prediction is going to be similar to the previous CNN example.
To improve the performance of the model in Listing 2.1.1, we can propose several
changes. Firstly, the branches of the Y-Network are doubling the number of filters
to compensate for the halving of the feature maps size after MaxPooling2D(). For
example, if the output of the first convolution is (28, 28, 32), after max pooling
the new shape is (14, 14, 32). The next convolution will have a filter size of 64
and output dimensions of (14, 14, 64).
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