Batch data generator for sequence of chars

batch_generator_CLTG.py

def batch_generator_sequence(features_seq, label_seq, batch_size, seq_len):
    """Generator function that yields batches of data (input and target)

    Args:
        batch_size (int): number of examples (in this case, sentences) per batch.
        max_length (int): maximum length of the output tensor.
        NOTE: max_length includes the end-of-sentence character that will be added
                to the tensor.  
                Keep in mind that the length of the tensor is always 1 + the length
                of the original line of characters.
        input_lines (list): list of the input data to group into batches.
        target_lines (list): list of the target data to group into batches.
        shuffle (bool, optional): True if the generator should generate random batches of data. Defaults to True.

    Yields:
        tuple: two copies of the batch and the mask 
    """
    # calculate the number of batches we can supply
    num_batches = len(features_seq) // (batch_size * seq_len)
    if num_batches == 0:
        raise ValueError("No batches created. Use smaller batch size or sequence length.")
    # calculate effective length of text to use
    rounded_len = num_batches * batch_size * seq_len
    # Reshape the features matrix in batch size x num_batches * seq_len
    x = np.reshape(features_seq[: rounded_len], [batch_size, num_batches * seq_len])
    
    # Reshape the target matrix in batch size x num_batches * seq_len
    y = np.reshape(label_seq[: rounded_len], [batch_size, num_batches * seq_len])
    
    epoch = 0
    while True:
        # roll so that no need to reset rnn states over epochs
        x_epoch = np.split(np.roll(x, -epoch, axis=0), num_batches, axis=1)
        y_epoch = np.split(np.roll(y, -epoch, axis=0), num_batches, axis=1)
        for batch in range(num_batches):
            yield x_epoch[batch], y_epoch[batch]
        epoch += 1