parameters: (self, images, labels, fake_data=False, one_hot=False,
                 dtype=tf.float32)
docstring: 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]`.
docstring_summary: Construct a DataSet.
docstring_tokens: Construct a DataSet .
function: 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]
  
        # 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(numpy.float32)
          images = numpy.multiply(images, 1.0 / 255.0)
      self._images = images
      self._labels = labels
      self._epochs_completed = 0
      self._index_in_epoch = 0
function_tokens: def __init__ ( self , images , labels , fake_data = False , one_hot = False , dtype = tf . float32 ) : 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 ] # 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 ( numpy . float32 ) images = numpy . multiply ( images , 1.0 / 255.0 ) self . _images = images self . _labels = labels self . _epochs_completed = 0 self . _index_in_epoch = 0