tf.nn.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
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See the guide: Neural Network > Normalization
Batch normalization.
This op is deprecated. See tf.nn.batch_normalization
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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).A batch-normalized t
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© 2018 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