FusedBatchNorm

tensorflow C++ API

tensorflow::ops::FusedBatchNorm

Batch normalization.

Summary

Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors.

Arguments:

• scope: A Scope object
• x: A 4D Tensor for input data.
• scale: A 1D Tensor for scaling factor, to scale the normalized x.
• offset: A 1D Tensor for offset, to shift to the normalized x.
• mean: A 1D Tensor for population mean. Used for inference only; must be empty for training.
• variance: A 1D Tensor for population variance. Used for inference only; must be empty for training.

Optional attributes (seeAttrs):

• epsilon: A small float number added to the variance of x.
• data_format: The data format for x and y. Either "NHWC" (default) or "NCHW".
• is_training: A bool value to indicate the operation is for training (default) or inference.

Returns:

• Output y: A 4D Tensor for output data.
• Outputbatch_mean: A 1D Tensor for the computed batch mean, to be used by TensorFlow to compute the running mean.
• Outputbatch_variance: A 1D Tensor for the computed batch variance, to be used by TensorFlow to compute the running variance.
• Outputreserve_space_1: A 1D Tensor for the computed batch mean, to be reused in the gradient computation.
• Outputreserve_space_2: A 1D Tensor for the computed batch variance (inverted variance in the cuDNN case), to be used in the gradient computation.

FusedBatchNorm block

Source link : https://github.com/EXPNUNI/enuSpaceTensorflow/blob/master/enuSpaceTensorflow/tf_nn.cpp

Argument:

• Scope scope : A Scope object (A scope is generated automatically each page. A scope is not connected.)
• Input x: connect Input node.
• Input scale: connect Input node.
• Input offset: connect Input node.
• Input mean: connect Input node.
• Input variance: connect Input node.
• FusedBatchNorm ::Attrs attrs : Input attrs in value. ex) pseudo_random_ = false;overlapping_ = false;deterministic_ = false;seed_ = 0;seed2_ = 0;

Return:

• Output y: Output object of FractionalAvgPool class object.
• Output batch_mean: Output object of FractionalAvgPool class object.
• Output batch_variance: Output object of FractionalAvgPool class object.
• Output reserve_space_1: Output object of FractionalAvgPool class object.
• Output reserve_space_2: Output object of FractionalAvgPool class object.

Result:

• std::vector(Tensor) result_y : Returned object of executed result by calling session.
• std::vector(Tensor) result_batch_variance : Returned object of executed result by calling session.
• std::vector(Tensor) result_batch_mean : Returned object of executed result by calling session.
• std::vector(Tensor) result_reserve_space_1 : Returned object of executed result by calling session.
• std::vector(Tensor) result_reserve_space_2 : Returned object of executed result by calling session.

Using Method