contrib.keras.initializers.VarianceScaling
tf.contrib.keras.initializers.VarianceScaling
class tf.contrib.keras.initializers.VarianceScaling
Defined in tensorflow/python/ops/init_ops.py
.
Initializer capable of adapting its scale to the shape of weights tensors.
With distribution="normal"
, samples are drawn from a truncated normal distribution centered on zero, with stddev = sqrt(scale / n)
where n is: - number of input units in the weight tensor, if mode = "fan_in" - number of output units, if mode = "fan_out" - average of the numbers of input and output units, if mode = "fan_avg"
With distribution="uniform"
, samples are drawn from a uniform distribution within [-limit, limit], with limit = sqrt(3 * scale / n)
.
Arguments:
-
scale
: Scaling factor (positive float). -
mode
: One of "fan_in", "fan_out", "fan_avg". -
distribution
: Random distribution to use. One of "normal", "uniform". -
seed
: A Python integer. Used to create random seeds. Seetf.set_random_seed
for behavior. -
dtype
: The data type. Only floating point types are supported.
Raises:
-
ValueError
: In case of an invalid value for the "scale", mode" or "distribution" arguments.
Methods
__init__
__init__( scale=1.0, mode='fan_in', distribution='normal', seed=None, dtype=tf.float32 )
__call__
__call__( shape, dtype=None, partition_info=None )
from_config
from_config( cls, config )
Instantiates an initializer from a configuration dictionary.
Example:
initializer = RandomUniform(-1, 1) config = initializer.get_config() initializer = RandomUniform.from_config(config)
Arguments:
-
config
: A Python dictionary. It will typically be the output ofget_config
.
Returns:
An Initializer instance.
get_config
get_config()
© 2017 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/api_docs/python/tf/contrib/keras/initializers/VarianceScaling