Very large datasets may not fit in the memory allocated to your process. In the
previous steps, we have set up a pipeline where we bring in the entire dataset
in to the memory, prepare the data, and pass the working set to the training
function. Instead, Keras provides an alternative training function
that pulls the data in batches. This allows us to apply the transformations in
the data pipeline to only a small (a multiple of
batch_size) part of the data.
During our experiments, we used batching (code in GitHub) for datasets such as
DBPedia, Amazon reviews, Ag news, and Yelp reviews.
The following code illustrates how to generate data batches and feed them to
def _data_generator(x, y, num_features, batch_size): """Generates batches of vectorized texts for training/validation. # Arguments x: np.matrix, feature matrix. y: np.ndarray, labels. num_features: int, number of features. batch_size: int, number of samples per batch. # Returns Yields feature and label data in batches. """ num_samples = x.shape num_batches = num_samples // batch_size if num_samples % batch_size: num_batches += 1 while 1: for i in range(num_batches): start_idx = i * batch_size end_idx = (i + 1) * batch_size if end_idx > num_samples: end_idx = num_samples x_batch = x[start_idx:end_idx] y_batch = y[start_idx:end_idx] yield x_batch, y_batch # Create training and validation generators. training_generator = _data_generator( x_train, train_labels, num_features, batch_size) validation_generator = _data_generator( x_val, val_labels, num_features, batch_size) # Get number of training steps. This indicated the number of steps it takes # to cover all samples in one epoch. steps_per_epoch = x_train.shape // batch_size if x_train.shape % batch_size: steps_per_epoch += 1 # Get number of validation steps. validation_steps = x_val.shape // batch_size if x_val.shape % batch_size: validation_steps += 1 # Train and validate model. history = model.fit_generator( generator=training_generator, steps_per_epoch=steps_per_epoch, validation_data=validation_generator, validation_steps=validation_steps, callbacks=callbacks, epochs=epochs, verbose=2) # Logs once per epoch.