View source on GitHub |
Lecun normal initializer.
Inherits From: VarianceScaling
, Initializer
tf.keras.initializers.LecunNormal(
seed=None
)
Also available via the shortcut function
tf.keras.initializers.lecun_normal
.
Initializers allow you to pre-specify an initialization strategy, encoded in the Initializer object, without knowing the shape and dtype of the variable being initialized.
Draws samples from a truncated normal distribution centered on 0 with
stddev = sqrt(1 / fan_in)
where fan_in
is the number of input units in
the weight tensor.
Examples:
# Standalone usage:
initializer = tf.keras.initializers.LecunNormal()
values = initializer(shape=(2, 2))
# Usage in a Keras layer:
initializer = tf.keras.initializers.LecunNormal()
layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)
References | |
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Methods
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, **kwargs
)
Returns a tensor object initialized as specified by the initializer.
Args | |
---|---|
shape
|
Shape of the tensor. |
dtype
|
Optional dtype of the tensor. Only floating point types are
supported. If not specified, tf.keras.backend.floatx() is used,
which default to float32 unless you configured it otherwise (via
tf.keras.backend.set_floatx(float_dtype) )
|
**kwargs
|
Additional keyword arguments. |