tensorflow_script_mode_california_housing_local_training_and_batch_transform/code/california_housing_tf2.py [40:83]:
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    print('x test', x_test.shape,'y test', y_test.shape)

    return x_test, y_test


def get_model():

    inputs = tf.keras.Input(shape=(8,))
    hidden_1 = tf.keras.layers.Dense(8, activation='tanh')(inputs)
    hidden_2 = tf.keras.layers.Dense(4, activation='sigmoid')(hidden_1)
    outputs = tf.keras.layers.Dense(1)(hidden_2)
    return tf.keras.Model(inputs=inputs, outputs=outputs)


if __name__ == "__main__":

    args, _ = parse_args()

    print('Training data location: {}'.format(args.train))
    print('Test data location: {}'.format(args.test))
    x_train, y_train = get_train_data(args.train)
    x_test, y_test = get_test_data(args.test)

    batch_size = args.batch_size
    epochs = args.epochs
    learning_rate = args.learning_rate
    print('batch_size = {}, epochs = {}, learning rate = {}'.format(batch_size, epochs, learning_rate))


    model = get_model()
    optimizer = tf.keras.optimizers.SGD(learning_rate)
    model.compile(optimizer=optimizer, loss='mse')
    model.fit(x_train,
              y_train,
              batch_size=batch_size,
              epochs=epochs,
              validation_data=(x_test, y_test))

    # evaluate on test set
    scores = model.evaluate(x_test, y_test, batch_size, verbose=2)
    print("\nTest MSE :", scores)

    # save model
    model.save(args.sm_model_dir + '/1')
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tensorflow_script_mode_california_housing_local_training_and_serving/code/california_housing_tf2.py [38:81]:
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    print('x test', x_test.shape,'y test', y_test.shape)

    return x_test, y_test


def get_model():

    inputs = tf.keras.Input(shape=(8,))
    hidden_1 = tf.keras.layers.Dense(8, activation='tanh')(inputs)
    hidden_2 = tf.keras.layers.Dense(4, activation='sigmoid')(hidden_1)
    outputs = tf.keras.layers.Dense(1)(hidden_2)
    return tf.keras.Model(inputs=inputs, outputs=outputs)


if __name__ == "__main__":

    args, _ = parse_args()

    print('Training data location: {}'.format(args.train))
    print('Test data location: {}'.format(args.test))
    x_train, y_train = get_train_data(args.train)
    x_test, y_test = get_test_data(args.test)

    batch_size = args.batch_size
    epochs = args.epochs
    learning_rate = args.learning_rate
    print('batch_size = {}, epochs = {}, learning rate = {}'.format(batch_size, epochs, learning_rate))


    model = get_model()
    optimizer = tf.keras.optimizers.SGD(learning_rate)
    model.compile(optimizer=optimizer, loss='mse')
    model.fit(x_train,
              y_train,
              batch_size=batch_size,
              epochs=epochs,
              validation_data=(x_test, y_test))

    # evaluate on test set
    scores = model.evaluate(x_test, y_test, batch_size, verbose=2)
    print("\nTest MSE :", scores)

    # save model
    model.save(args.sm_model_dir + '/1')
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