data_generator.py

# Function to generate a training batch for the LSTM model.
import numpy as np

from util import vocabulary_size, char2id


class BatchGenerator(object):
    def __init__(self, text, batch_size, num_unrollings):
        self._text = text
        self._text_size = len(text)
        self._batch_size = batch_size
        self._num_unrollings = num_unrollings
        segment = self._text_size // batch_size
        self._cursor = [offset * segment for offset in range(batch_size)]
        self._last_batch = self._next_batch()

    def _next_batch(self):
        """Generate a single batch from the current cursor position in the data."""
        batch = np.zeros(shape=(self._batch_size, vocabulary_size), dtype=np.float)
        for b in range(self._batch_size):
            batch[b, char2id(self._text[self._cursor[b]])] = 1.0
            self._cursor[b] = (self._cursor[b] + 1) % self._text_size
        return batch

    def next(self):
        """Generate the next array of batches from the data. The array consists of
        the last batch of the previous array, followed by num_unrollings new ones.
        """
        batches = [self._last_batch]
        for step in range(self._num_unrollings):
            batches.append(self._next_batch())
        self._last_batch = batches[-1]
        return batches