Outputs random values from a truncated normal distribution.
tf.random.stateless_parameterized_truncated_normal(
shape, seed, means=0.0, stddevs=1.0, minvals=-2.0, maxvals=2.0, name=None
)
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.
Examples:
Sample from a Truncated normal, with deferring shape parameters that
broadcast.
means = 0.
stddevs = tf.math.exp(tf.random.uniform(shape=[2, 3]))
minvals = [-1., -2., -1000.]
maxvals = [[10000.], [1.]]
y = tf.random.stateless_parameterized_truncated_normal(
shape=[10, 2, 3], seed=[7, 17],
means=means, stddevs=stddevs, minvals=minvals, maxvals=maxvals)
y.shape
TensorShape([10, 2, 3])
Args |
shape
|
A 1-D integer Tensor or Python array. The shape of the output
tensor.
|
seed
|
A shape [2] Tensor, the seed to the random number generator. Must have
dtype int32 or int64 . (When using XLA, only int32 is allowed.)
|
means
|
A Tensor or Python value of type dtype . The mean of the truncated
normal distribution. This must broadcast with stddevs , minvals and
maxvals , and the broadcasted shape must be dominated by shape .
|
stddevs
|
A Tensor or Python value of type dtype . The standard deviation
of the truncated normal distribution. This must broadcast with means ,
minvals and maxvals , and the broadcasted shape must be dominated by
shape .
|
minvals
|
A Tensor or Python value of type dtype . The minimum value of
the truncated normal distribution. This must broadcast with means ,
stddevs and maxvals , and the broadcasted shape must be dominated by
shape .
|
maxvals
|
A Tensor or Python value of type dtype . The maximum value of
the truncated normal distribution. This must broadcast with means ,
stddevs and minvals , and the broadcasted shape must be dominated by
shape .
|
name
|
A name for the operation (optional).
|
Returns |
A tensor of the specified shape filled with random truncated normal values.
|