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附录：批量训练

超大型数据集可能不适合分配给进程的内存。在前面的步骤中，我们设置了一个流水线，用于将整个数据集引入内存、准备数据，并将工作集传递给训练函数。而是会提供一个替代训练函数 (fit_generator)，用于批量拉取数据。这样，我们就可以仅对数据中的一小部分（batch_size 的倍数）应用数据流水线中的转换。在实验期间，我们针对 DBPedia、Amazon 评价、Ag News 和 Yelp 评价等数据集使用了批处理（GitHub 中的代码）。

以下代码说明了如何生成数据批次并将其提供给 fit_generator。

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[0]
    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[0] // batch_size
if x_train.shape[0] % batch_size:
    steps_per_epoch += 1

# Get number of validation steps.
validation_steps = x_val.shape[0] // batch_size
if x_val.shape[0] % 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.

总结