tf.nn.fused_batch_norm( x, scale, offset, mean=None, variance=None, epsilon=0.001, data_format='NHWC', is_training=True, name=None )
Defined in tensorflow/python/ops/nn_impl.py
.
See the guide: Neural Network > Normalization
Batch normalization.
As described in http://arxiv.org/abs/1502.03167.
x
: Input Tensor
of 4 dimensions.scale
: A Tensor
of 1 dimension for scaling.offset
: A Tensor
of 1 dimension for bias.mean
: A Tensor
of 1 dimension for population mean used for inference.variance
: A Tensor
of 1 dimension for population variance used for inference.epsilon
: A small float number added to the variance of x.data_format
: The data format for x. Either "NHWC" (default) or "NCHW".is_training
: A bool value to specify if the operation is used for training or inference.name
: A name for this operation (optional).y
: A 4D Tensor for the normalized, scaled, offsetted x.batch_mean
: A 1D Tensor for the mean of x.batch_var
: A 1D Tensor for the variance of x.ValueError
: If mean or variance is not None when is_training is True.
© 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/fused_batch_norm