Advanced Deep Learning with Keras

Chapter 2
optimizer='adam',
metrics=['accuracy'])
# train the model with input images and labels
model.fit(x_train,
y_train,
validation_data=(x_test, y_test),
epochs=20,
batch_size=batch_size)
# model accuracy on test dataset
score = model.evaluate(x_test, y_test, batch_size=batch_size)
print("\nTest accuracy: %.1f%%" % (100.0 * score[1]))
By default, MaxPooling2D uses pool_size=2, so the argument has been removed.
In the preceding listing every layer is a function of a tensor. They each generate
a tensor as an output which becomes the input to the next layer. To create this
model, we can call Model() and supply both the inputs and outputs tensors,
or alternatively the lists of tensors. Everything else remains the same.
The same listing can also be trained and evaluated using the fit() and evaluate()
functions, similar to the sequential model. The sequential class is, in fact, a subclass
of the Model class. We need to remember that we inserted the validation_data
argument in the fit() function to see the progress of validation accuracy during
training. The accuracy ranges from 99.3% to 99.4% in 20 epochs.
Creating a two-input and one-output model
We're now going to do something really exciting, creating an advanced model
with two inputs and one output. Before we start, it's important to know that this
is something that is not straightforward in the sequential model.
Let's suppose a new model for the MNIST digit classification is invented, and it's
called the Y-Network, as shown in Figure 2.1.1. The Y-Network uses the same input
twice, both on the left and right CNN branches. The network combines the results
using concatenate layer. The merge operation concatenate is similar to stacking
two tensors of the same shape along the concatenation axis to form one tensor. For
example, concatenating two tensors of shape (3, 3, 16) along the last axis will result
in a tensor of shape (3, 3, 32).
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