tf.nn.fused_batch_norm
Batch normalization.
tf.nn.fused_batch_norm( x, scale, offset, mean=None, variance=None, epsilon=0.001, data_format='NHWC', is_training=True, name=None )
See Source: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift; S. Ioffe, C. Szegedy.
Args | |
---|---|
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). |
Returns | |
---|---|
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. |
Raises | |
---|---|
ValueError |
If mean or variance is not None when is_training is True. |
© 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/r1.15/api_docs/python/tf/nn/fused_batch_norm