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Neural Network Foundations with TensorFlow 2.0
model.add(layers.GlobalMaxPooling1D())
model.add(layers.Dense(128, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(1, activation='sigmoid'))
return model
Now we need to train our model, and this piece of code is very similar to what we
did with MNIST. Let's see:
(X_train, y_train), (X_test, y_test) = load_data()
model = build_model()
model.summary()
model.compile(optimizer = "adam", loss = "binary_crossentropy",
metrics = ["accuracy"]
)
score = model.fit(X_train, y_train,
epochs = EPOCHS,
batch_size = BATCH_SIZE,
validation_data = (X_test, y_test)
)
score = model.evaluate(X_test, y_test, batch_size=BATCH_SIZE)
print("\nTest score:", score[0])
print('Test accuracy:', score[1])
Let's see the network and then run a few iterations:
Figure 36: The results of the network following a few iterations
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