def __init__(self, images, labels, fake_data=False, one_hot=False,
dtype=tf.float32):
"""Construct a DataSet.
one_hot arg is used only if fake_data is true. `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)
if fake_data:
self._num_examples = 10000
self.one_hot = one_hot
else:
assert images.shape[0] == labels.shape[0], (
'images.shape: %s labels.shape: %s' % (images.shape,
labels.shape))
self._num_examples = images.shape[0]
self._width = images.shape[1]
self._height = images.shape[2]
self._depth = images.shape[3]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns*depth]
assert images.shape[3] == IMAGE_DEPTH
images = images.reshape(
images.shape[0],
images.shape[1] * images.shape[2] * images.shape[3])
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
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