tf.nn.fused_batch_norm(x, scale, offset, mean=None, variance=None, epsilon=0.001, data_format='NHWC', is_training=True, name=None)

tf.nn.fused_batch_norm(x, scale, offset, mean=None, variance=None, epsilon=0.001, data_format='NHWC', is_training=True, name=None)

See the guide: Neural Network > Normalization

Batch normalization.

As described in http://arxiv.org/abs/1502.03167.

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.

Defined in tensorflow/python/ops/nn_impl.py.