登录
View source on GitHub
|
He normal initializer.
Inherits From: VarianceScaling, Initializer
tf.keras.initializers.HeNormal(
seed=None
)
Also available via the shortcut function
tf.keras.initializers.he_normal.
It draws samples from a truncated normal distribution centered on 0 with
stddev = sqrt(2 / fan_in) where fan_in is the number of input units in the
weight tensor.
Examples:
# Standalone usage:initializer = tf.keras.initializers.HeNormal()values = initializer(shape=(2, 2))
# Usage in a Keras layer:initializer = tf.keras.initializers.HeNormal()layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)
Arguments | |
|---|---|
seed
|
A Python integer. An initializer created with a given seed will always produce the same random tensor for a given shape and dtype. |
References:
Methods
from_config
@classmethodfrom_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.
It will typically be the output of get_config.
|
| Returns | |
|---|---|
| An Initializer instance. |
get_config
get_config()
Returns the configuration of the initializer 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. |