tf.nn.batch_norm_with_global_normalization
tf.nn.batch_norm_with_global_normalization
tf.nn.batch_norm_with_global_normalization
batch_norm_with_global_normalization( t, m, v, beta, gamma, variance_epsilon, scale_after_normalization, name=None )
Defined in tensorflow/python/ops/nn_impl.py
.
See the guide: Neural Network > Normalization
Batch normalization.
This op is deprecated. See tf.nn.batch_normalization
.
Args:
-
t
: A 4D input Tensor. -
m
: A 1D mean Tensor with size matching the last dimension of t. This is the first output from tf.nn.moments, or a saved moving average thereof. -
v
: A 1D variance Tensor with size matching the last dimension of t. This is the second output from tf.nn.moments, or a saved moving average thereof. -
beta
: A 1D beta Tensor with size matching the last dimension of t. An offset to be added to the normalized tensor. -
gamma
: A 1D gamma Tensor with size matching the last dimension of t. If "scale_after_normalization" is true, this tensor will be multiplied with the normalized tensor. -
variance_epsilon
: A small float number to avoid dividing by 0. -
scale_after_normalization
: A bool indicating whether the resulted tensor needs to be multiplied with gamma. -
name
: A name for this operation (optional).
Returns:
A batch-normalized t
.
© 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/nn/batch_norm_with_global_normalization