tf-2-workflow-smpipelines/train_model/train.py [27:74]:
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    return parser.parse_known_args()


def get_train_data(train_dir):

    x_train = np.load(os.path.join(train_dir, 'x_train.npy'))
    y_train = np.load(os.path.join(train_dir, 'y_train.npy'))
    print('x train', x_train.shape,'y train', y_train.shape)

    return x_train, y_train


def get_test_data(test_dir):

    x_test = np.load(os.path.join(test_dir, 'x_test.npy'))
    y_test = np.load(os.path.join(test_dir, 'y_test.npy'))
    print('x test', x_test.shape,'y test', y_test.shape)

    return x_test, y_test


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)

    device = '/cpu:0'
    print(device)
    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))

    with tf.device(device):

        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)
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tf-2-workflow/train_model/train.py [27:74]:
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    return parser.parse_known_args()


def get_train_data(train_dir):
    
    x_train = np.load(os.path.join(train_dir, 'x_train.npy'))
    y_train = np.load(os.path.join(train_dir, 'y_train.npy'))
    print('x train', x_train.shape,'y train', y_train.shape)

    return x_train, y_train


def get_test_data(test_dir):
    
    x_test = np.load(os.path.join(test_dir, 'x_test.npy'))
    y_test = np.load(os.path.join(test_dir, 'y_test.npy'))
    print('x test', x_test.shape,'y test', y_test.shape)

    return x_test, y_test
   

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)
    
    device = '/cpu:0' 
    print(device)
    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))

    with tf.device(device):
        
        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)
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