tensorflow_probability/python/math/random_ops.py
def random_rademacher(shape, dtype=tf.float32, seed=None, name=None):
  """Generates `Tensor` consisting of `-1` or `+1`, chosen uniformly at random.

  For more details, see [Rademacher distribution](
  https://en.wikipedia.org/wiki/Rademacher_distribution).

  Args:
    shape: Vector-shaped, `int` `Tensor` representing shape of output.
    dtype: (Optional) TF `dtype` representing `dtype` of output.
    seed: (Optional) Python integer to seed the random number generator.
    name: Python `str` name prefixed to Ops created by this function.
      Default value: `None` (i.e., 'random_rademacher').

  Returns:
    rademacher: `Tensor` with specified `shape` and `dtype` consisting of `-1`
      or `+1` chosen uniformly-at-random.
  """
  with tf.compat.v1.name_scope(name, 'random_rademacher', [shape, seed]):
    # Choose the dtype to cause `2 * random_bernoulli - 1` to run in the same
    # memory (host or device) as the downstream cast will want to put it.  The
    # convention on GPU is that int32 are in host memory and int64 are in device
    # memory.
    generation_dtype = tf.int64 if tf.as_dtype(dtype) != tf.int32 else tf.int32
    random_bernoulli = tf.random.uniform(
        shape, minval=0, maxval=2, dtype=generation_dtype, seed=seed)
    return tf.cast(2 * random_bernoulli - 1, dtype)
def random_rademacher(shape, dtype=tf.float32, seed=None, name=None):
  with tf.compat.v1.name_scope(name, 'random_rademacher', [shape, seed]):
    # Choose the dtype to cause `2 * random_bernoulli - 1` to run in the same
    # memory (host or device) as the downstream cast will want to put it.  The
    # convention on GPU is that int32 are in host memory and int64 are in device
    # memory.
    generation_dtype = tf.int64 if tf.as_dtype(dtype) != tf.int32 else tf.int32
    random_bernoulli = tf.random.uniform(
        shape, minval=0, maxval=2, dtype=generation_dtype, seed=seed)
    return tf.cast(2 * random_bernoulli - 1, dtype)
def random_rademacher ( shape , dtype = tf . float32 , seed = None , name = None ) : with tf . compat . v1 . name_scope ( name , 'random_rademacher' , [ shape , seed ] ) : # Choose the dtype to cause `2 * random_bernoulli - 1` to run in the same # memory (host or device) as the downstream cast will want to put it.  The # convention on GPU is that int32 are in host memory and int64 are in device # memory. generation_dtype = tf . int64 if tf . as_dtype ( dtype ) != tf . int32 else tf . int32 random_bernoulli = tf . random . uniform ( shape , minval = 0 , maxval = 2 , dtype = generation_dtype , seed = seed ) return tf . cast ( 2 * random_bernoulli - 1 , dtype )
Generates `Tensor` consisting of `-1` or `+1`, chosen uniformly at random.

  For more details, see [Rademacher distribution](
  https://en.wikipedia.org/wiki/Rademacher_distribution).

  Args:
    shape: Vector-shaped, `int` `Tensor` representing shape of output.
    dtype: (Optional) TF `dtype` representing `dtype` of output.
    seed: (Optional) Python integer to seed the random number generator.
    name: Python `str` name prefixed to Ops created by this function.
      Default value: `None` (i.e., 'random_rademacher').

  Returns:
    rademacher: `Tensor` with specified `shape` and `dtype` consisting of `-1`
      or `+1` chosen uniformly-at-random.
Generates Tensor consisting of - 1 or + 1 chosen uniformly at random .