def parameterized_truncated_normal(shape,
means=0.0,
stddevs=1.0,
minvals=-2.0,
maxvals=2.0,
dtype=dtypes.float32,
seed=None,
name=None):
"""Outputs random values from a truncated normal distribution.
The generated values follow a normal distribution with specified mean and
standard deviation, except that values whose magnitude is more than 2 standard
deviations from the mean are dropped and re-picked.
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
means: A 0-D Tensor or Python value of type `dtype`. The mean of the
truncated normal distribution.
stddevs: A 0-D Tensor or Python value of type `dtype`. The standard
deviation of the truncated normal distribution.
minvals: A 0-D Tensor or Python value of type `dtype`. The minimum value of
the truncated normal distribution.
maxvals: A 0-D Tensor or Python value of type `dtype`. The maximum value of
the truncated normal distribution.
dtype: The type of the output.
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 truncated normal values.
"""
with ops.name_scope(name, "parameterized_truncated_normal",
[shape, means, stddevs, minvals, maxvals]) as name:
shape_tensor = _ShapeTensor(shape)
means_tensor = ops.convert_to_tensor(means, dtype=dtype, name="means")
stddevs_tensor = ops.convert_to_tensor(stddevs, dtype=dtype, name="stddevs")
minvals_tensor = ops.convert_to_tensor(minvals, dtype=dtype, name="minvals")
maxvals_tensor = ops.convert_to_tensor(maxvals, dtype=dtype, name="maxvals")
seed1, seed2 = random_seed.get_seed(seed)
rnd = gen_random_ops._parameterized_truncated_normal(
shape_tensor,
means_tensor,
stddevs_tensor,
minvals_tensor,
maxvals_tensor,
seed=seed1,
seed2=seed2)
return rnd
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