tf.keras.initializers.HeNormal
He normal initializer.
Inherits From: VarianceScaling
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
@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. 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
)
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)) |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/keras/initializers/HeNormal