Method uniform

tensorflow/python/ops/stateful_random_ops.py:460–511

Outputs random values from a uniform distribution. The generated values follow a uniform distribution in the range `[minval, maxval)`. The lower bound `minval` is included in the range, while the upper bound `maxval` is excluded. (For float numbers especially low-precision types lik

(self, shape, minval=0, maxval=None,
              dtype=dtypes.float32, name=None)

        self.state.handle, self.algorithm, shape=shape, dtype=dtype)

  def uniform(self, shape, minval=0, maxval=None,
              dtype=dtypes.float32, name=None):
    """Outputs random values from a uniform distribution.

    The generated values follow a uniform distribution in the range
    `[minval, maxval)`. The lower bound `minval` is included in the range, while
    the upper bound `maxval` is excluded. (For float numbers especially
    low-precision types like bfloat16, because of
    rounding, the result may sometimes include `maxval`.)

    For floats, the default range is `[0, 1)`.  For ints, at least `maxval` must
    be specified explicitly.

    In the integer case, the random integers are slightly biased unless
    `maxval - minval` is an exact power of two.  The bias is small for values of
    `maxval - minval` significantly smaller than the range of the output (either
    `2**32` or `2**64`).

    Args:
      shape: A 1-D integer Tensor or Python array. The shape of the output
        tensor.
      minval: A 0-D Tensor or Python value of type `dtype`. The lower bound on
        the range of random values to generate.  Defaults to 0.
      maxval: A 0-D Tensor or Python value of type `dtype`. The upper bound on
        the range of random values to generate.  Defaults to 1 if `dtype` is
        floating point.
      dtype: The type of the output.
      name: A name for the operation (optional).

    Returns:
      A tensor of the specified shape filled with random uniform values.

    Raises:
      ValueError: If `dtype` is integral and `maxval` is not specified.
    """
    dtype = dtypes.as_dtype(dtype)
    if maxval is None:
      if dtype.is_integer:
        raise ValueError("Must specify maxval for integer dtype %r" % dtype)
      maxval = 1
    with ops.name_scope(name, "stateful_uniform",
                        [shape, minval, maxval]) as name:
      shape = _shape_tensor(shape)
      minval = ops.convert_to_tensor(minval, dtype=dtype, name="min")
      maxval = ops.convert_to_tensor(maxval, dtype=dtype, name="max")
      if dtype.is_integer:
        return gen_stateful_random_ops.stateful_uniform_int(
            self.state.handle, self.algorithm, shape=shape,
            minval=minval, maxval=maxval, name=name)
      else:
        rnd = self._uniform(shape=shape, dtype=dtype)
        return math_ops.add(rnd * (maxval - minval), minval, name=name)

  def uniform_full_int(self, shape, dtype=dtypes.uint64, name=None):
    """Uniform distribution on an integer type's entire range.