INNER CODE UNIT · Python

learning_rates

drawbridge/keras-mmoe · synthetic_demo.py:127

    learning_rates = [1e-4, 1e-3, 1e-2]
    adam_optimizer = Adam(lr=learning_rates[0])
    model.compile(
        loss={'y0': 'mean_squared_error', 'y1': 'mean_squared_error'},
        optimizer=adam_optimizer,
        metrics=[metrics.mae]
    )

    # Print out model architecture summary
    model.summary()

    # Train the model
    model.fit(
        x=train_data,
        y=train_label,
        validation_data=(validation_data, validation_label),
        epochs=100
    )

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