tf.random.stateless_parameterized_truncated_normal

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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])

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).

A tensor of the specified shape filled with random truncated normal values.