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Chapter 1
Y_train = tf.keras.utils.to_categorical(Y_train, NB_CLASSES)
Y_test = tf.keras.utils.to_categorical(Y_test, NB_CLASSES)
# Building the model.
model = tf.keras.models.Sequential()
model.add(keras.layers.Dense(N_HIDDEN,
input_shape=(RESHAPED,),
name='dense_layer', activation='relu'))
model.add(keras.layers.Dropout(DROPOUT))
model.add(keras.layers.Dense(N_HIDDEN,
name='dense_layer_2', activation='relu'))
model.add(keras.layers.Dropout(DROPOUT))
model.add(keras.layers.Dense(NB_CLASSES,
name='dense_layer_3', activation='softmax'))
# Summary of the model.
model.summary()
# Compiling the model.
model.compile(optimizer='SGD',
loss='categorical_crossentropy',
metrics=['accuracy'])
# Training the model.
model.fit(X_train, Y_train,
batch_size=BATCH_SIZE, epochs=EPOCHS,
verbose=VERBOSE, validation_split=VALIDATION_SPLIT)
# Evaluating the model.
test_loss, test_acc = model.evaluate(X_test, Y_test)
print('Test accuracy:', test_acc)
Let's run the code for 200 iterations as before, and we'll see that this net achieves an
accuracy of 91.70% on training, 94.42% on validation, and 94.15% on testing:
Figure 17: Further testing of the neutal network, with accuracies shown
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