Initializer capable of adapting its scale to the shape of weights tensors.
Inherits From: Initializer
tf.keras.initializers.VarianceScaling( scale=1.0, mode='fan_in', distribution='truncated_normal', seed=None )
Also available via the shortcut function tf.keras.initializers.variance_scaling
.
With distribution="truncated_normal" or "untruncated_normal"
, samples are drawn from a truncated/untruncated normal distribution with a mean of zero and a standard deviation (after truncation, if used) stddev = sqrt(scale / n)
, where n
is:
mode="fan_in"
mode="fan_out"
mode="fan_avg"
With distribution="uniform"
, samples are drawn from a uniform distribution within [-limit, limit]
, where limit = sqrt(3 * scale / n)
.
# Standalone usage: initializer = tf.keras.initializers.VarianceScaling( scale=0.1, mode='fan_in', distribution='uniform') values = initializer(shape=(2, 2))
# Usage in a Keras layer: initializer = tf.keras.initializers.VarianceScaling( scale=0.1, mode='fan_in', distribution='uniform') layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)
Args | |
---|---|
scale | Scaling factor (positive float). |
mode | One of "fan_in", "fan_out", "fan_avg". |
distribution | Random distribution to use. One of "truncated_normal", "untruncated_normal" and "uniform". |
seed | A Python integer. An initializer created with a given seed will always produce the same random tensor for a given shape and dtype. |
from_config
@classmethod from_config( config )
Instantiates an initializer from a configuration dictionary.
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. |
© 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/r2.4/api_docs/python/tf/keras/initializers/VarianceScaling