What do model.predict() and model.fit() do?

Question

I'm going through this reinforcement learning tutorial and It's been really great so far but could someone please explain what

newQ = model.predict(new_state.reshape(1,64), batch_size=1)

and

model.fit(X_train, y_train, batch_size=batchSize, nb_epoch=1, verbose=1)

mean?

As in what do the arguments bach_size, nb_epoch and verbose do? I know neural networks so explaining in terms of that would be helpful.

You could also send me a link where the documentation of these functions can be found.

Answer 1

First of all it surprises me that you could not find the documentation but I guess you just had bad luck while searching.

The documentation states for model.fit:

fit(self, x, y, batch_size=32, nb_epoch=10, verbose=1, callbacks=[], validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None)

• batch_size: integer. Number of samples per gradient update.
• nb_epoch: integer, the number of times to iterate over the training data arrays.
• verbose: 0, 1, or 2. Verbosity mode. 0 = silent, 1 = verbose, 2 = one log line per epoch.

The batch_size parameter in case of model.predict is just the number of samples used for each prediction step. So calling model.predict one time consumes batch_size number of data samples. This helps for devices that can process large matrices quickly (such as GPUs).