tf_rand_ops.py 文件源码

def random_uniform(shape,
                   minval=0,
                   maxval=None,
                   dtype=dtypes.float32,
                   seed=None,
                   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 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: `float32`, `float64`, `int32`, or `int64`.
    seed: A Python integer. Used to create a random seed for the distribution.
      See @{tf.set_random_seed}
      for behavior.
    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, "random_uniform", [shape, minval, maxval]) as name:
    shape = _ShapeTensor(shape)
    minval = ops.convert_to_tensor(minval, dtype=dtype, name="min")
    maxval = ops.convert_to_tensor(maxval, dtype=dtype, name="max")
    seed1, seed2 = random_seed.get_seed(seed)
    if dtype.is_integer:
      return gen_random_ops._random_uniform_int(
          shape, minval, maxval, seed=seed1, seed2=seed2, name=name)
    else:
      rnd = gen_random_ops._random_uniform(
          shape, dtype, seed=seed1, seed2=seed2)
      return math_ops.add(rnd * (maxval - minval), minval, name=name)