Confusion about keras Model: __call__ vs. call vs. predict methods
Adding to @Dmitry Kabanov, they are similar yet they aren't exactly the same thing. If you care about performance, need to look in to critical differences between them.
| model.predict | model(x) |
|---|---|
| loops over the data in batches which means means that predict() calls can scale to very large arrays. | happens in-memory and doesn't scale |
| not differentiable | differentiable |
| use this if you just need the output value | use this when you need to retrieve the gradients |
| Output is NumPy value | Output is a Tensor |
| use this if you have batches of data to be predicted | use this for small dataset |
| relatively slower for small data | relatively faster for small data |
Please check more detailed explanation in Keras FAQs
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.