chessfenbot/dataset.py

import tensorflow as tf
# From https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/examples/tutorials/mnist/input_data.py
class DataSet(object):
  def __init__(self, images, labels, dtype=tf.float32):
    """Construct a DataSet. 
    `dtype` can be either
    `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
    `[0, 1]`.
    """
    dtype = tf.as_dtype(dtype).base_dtype
    
    if dtype not in (tf.uint8, tf.float32):
      raise TypeError('Invalid image dtype %r, expected uint8 or float32' %
              dtype)
    assert images.shape[0] == labels.shape[0], (
      'images.shape: %s labels.shape: %s' % (images.shape,
                           labels.shape))
    self._num_examples = images.shape[0]
    # Convert shape from [num examples, rows, columns, depth]
    # to [num examples, rows*columns] (assuming depth == 1)
    assert images.shape[3] == 1
    images = images.reshape(images.shape[0], images.shape[1] * images.shape[2])
    if dtype == tf.float32:
      # Convert from [0, 255] -> [0.0, 1.0].
      images = images.astype(np.float32)
      images = np.multiply(images, 1.0 / 255.0)
    
    self._images = images
    self._labels = labels
    self._epochs_completed = 0
    self._index_in_epoch = 0
  @property
  def images(self):
    return self._images
  @property
  def labels(self):
    return self._labels
  @property
  def num_examples(self):
    return self._num_examples
  @property
  def epochs_completed(self):
    return self._epochs_completed
  def next_batch(self, batch_size):
    """Return the next `batch_size` examples from this data set."""
    start = self._index_in_epoch
    self._index_in_epoch += batch_size
    if self._index_in_epoch > self._num_examples:
      # Finished epoch
      self._epochs_completed += 1
      # Shuffle the data
      perm = np.arange(self._num_examples)
      np.random.shuffle(perm)
      self._images = self._images[perm]
      self._labels = self._labels[perm]
      # Start next epoch
      start = 0
      self._index_in_epoch = batch_size
      assert batch_size <= self._num_examples
    end = self._index_in_epoch
    return self._images[start:end], self._labels[start:end]