tensorflow_probability/examples/vq_vae.py
def load_bernoulli_mnist_dataset(directory, split_name):
  """Returns Hugo Larochelle's binary static MNIST tf.data.Dataset."""
  amat_file = download(directory, FILE_TEMPLATE.format(split=split_name))
  dataset = tf.data.TextLineDataset(amat_file)
  str_to_arr = lambda string: np.array([c == b"1" for c in string.split()])

  def _parser(s):
    booltensor = tf.compat.v1.py_func(str_to_arr, [s], tf.bool)
    reshaped = tf.reshape(booltensor, [28, 28, 1])
    return tf.cast(reshaped, dtype=tf.float32), tf.constant(0, tf.int32)

  return dataset.map(_parser)
def load_bernoulli_mnist_dataset(directory, split_name):
  """Returns Hugo Larochelle's binary static MNIST tf.data.Dataset."""
  amat_file = download(directory, FILE_TEMPLATE.format(split=split_name))
  dataset = tf.data.TextLineDataset(amat_file)
  str_to_arr = lambda string: np.array([c == b"1" for c in string.split()])

  def _parser(s):
    booltensor = tf.compat.v1.py_func(str_to_arr, [s], tf.bool)
    reshaped = tf.reshape(booltensor, [28, 28, 1])
    return tf.cast(reshaped, dtype=tf.float32), tf.constant(0, tf.int32)

  return dataset.map(_parser)
def load_bernoulli_mnist_dataset ( directory , split_name ) : amat_file = download ( directory , FILE_TEMPLATE . format ( split = split_name ) ) dataset = tf . data . TextLineDataset ( amat_file ) str_to_arr = lambda string : np . array ( [ c == b"1" for c in string . split ( ) ] ) def _parser ( s ) : booltensor = tf . compat . v1 . py_func ( str_to_arr , [ s ] , tf . bool ) reshaped = tf . reshape ( booltensor , [ 28 , 28 , 1 ] ) return tf . cast ( reshaped , dtype = tf . float32 ) , tf . constant ( 0 , tf . int32 ) return dataset . map ( _parser )
Returns Hugo Larochelle's binary static MNIST tf.data.Dataset.
Returns Hugo Larochelle s binary static MNIST tf . data . Dataset .