View source on GitHub |
Initializer that generates a truncated normal distribution.
Inherits From: Initializer
tf.keras.initializers.TruncatedNormal(
mean=0.0, stddev=0.05, seed=None
)
Used in the notebooks
Used in the tutorials |
---|
The values generated are similar to values from a
RandomNormal
initializer, except that values more
than two standard deviations from the mean are
discarded and re-drawn.
Examples:
# Standalone usage:
initializer = TruncatedNormal(mean=0., stddev=1.)
values = initializer(shape=(2, 2))
# Usage in a Keras layer:
initializer = TruncatedNormal(mean=0., stddev=1.)
layer = Dense(3, kernel_initializer=initializer)
Methods
clone
clone()
from_config
@classmethod
from_config( config )
Instantiates an initializer from a configuration dictionary.
Example:
initializer = RandomUniform(-1, 1)
config = initializer.get_config()
initializer = RandomUniform.from_config(config)
Args | |
---|---|
config
|
A Python dictionary, the output of get_config() .
|
Returns | |
---|---|
An Initializer instance.
|
get_config
get_config()
Returns the initializer's configuration as a JSON-serializable dict.
Returns | |
---|---|
A JSON-serializable Python dict. |
__call__
__call__(
shape, dtype=None
)
Returns a tensor object initialized as specified by the initializer.
Args | |
---|---|
shape
|
Shape of the tensor. |
dtype
|
Optional dtype of the tensor. |