Outputs random values from a truncated normal distribution.
tf.random.truncated_normal(
shape,
mean=0.0,
stddev=1.0,
dtype=tf.dtypes.float32
,
seed=None,
name=None
)
The values are drawn from a normal distribution with specified mean and
standard deviation, discarding and re-drawing any samples that are more than
two standard deviations from the mean.
Examples:
tf.random.truncated_normal(shape=[2])
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([..., ...], dtype=float32)>
tf.random.truncated_normal(shape=[2], mean=3, stddev=1, dtype=tf.float32)
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([..., ...], dtype=float32)>
Args |
shape
|
A 1-D integer Tensor or Python array. The shape of the output tensor.
|
mean
|
A 0-D Tensor or Python value of type dtype . The mean of the
truncated normal distribution.
|
stddev
|
A 0-D Tensor or Python value of type dtype . The standard deviation
of the normal distribution, before truncation.
|
dtype
|
The type of the output. Restricted to floating-point types:
tf.half , tf.float , tf.double , etc.
|
seed
|
A Python integer. Used to create a random seed for the distribution.
See tf.random.set_seed for more information.
|
name
|
A name for the operation (optional).
|
Returns |
A tensor of the specified shape filled with random truncated normal values.
|