Confusion about keras Model: __call__ vs. call vs. predict methods

Question

I have realized that I do not quite understand the difference between calling either the __call__, call, or predict method of a Keras' model.

For example, we have a trained keras model. After calling the code:

# After training.
y_pred_1 = model(X_new)
y_pred_2 = model.call(X_new)
y_pred_3 = model.predict(X_new)

I expected that y_pred_1, y_pred_2, and y_pred_3 are all the same. But it turned out that they are not the same.

Could you please explain to me the difference?

Answer 1

Just to complement the answer as I was also searching for this. When you need to specify the training flag of the model for the inference phase, such as, model(X_new, training=False) when you have a batch normalization layer, for example, both predict and predict_on_batch already do that when they are executed.

So, model(X_new, training=False) and model.predict_on_batch(X_new) are equivalent.

The difference between predict and predict_on_batch is that the latter runs over a single batch, and the former runs over a dataset that is splitted into batches and the results merged to produce the final numpy array of predictions.

Beyond the difference mentioned by @Dmitry Kabanov, the functions generate different types of output, __call__ generates a Tensor, and predict and predict_on_batch generate numpy.ndarray, and according to the documentation, __call__ is faster than the predict function for small scale inputs, i.e., which fit in one batch.