tensorflow:: ops:: SparseSoftmaxCrossEntropyWithLogits
#include <nn_ops.h>
Computes softmax cross entropy cost and gradients to backpropagate.
Summary
Unlike SoftmaxCrossEntropyWithLogits
, this operation does not accept a matrix of label probabilities, but rather a single label per row of features. This label is considered to have probability 1.0 for the given row.
Inputs are the logits, not probabilities.
Args:
- scope: A Scope object
- features: batch_size x num_classes matrix
- labels: batch_size vector with values in [0, num_classes). This is the label for the given minibatch entry.
Returns:
Output
loss: Per example loss (batch_size vector).Output
backprop: backpropagated gradients (batch_size x num_classes matrix).
Constructors and Destructors |
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SparseSoftmaxCrossEntropyWithLogits(const ::tensorflow::Scope & scope, ::tensorflow::Input features, ::tensorflow::Input labels)
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Public attributes |
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backprop
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loss
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operation
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Public attributes
backprop
::tensorflow::Output backprop
loss
::tensorflow::Output loss
operation
Operation operation
Public functions
SparseSoftmaxCrossEntropyWithLogits
SparseSoftmaxCrossEntropyWithLogits( const ::tensorflow::Scope & scope, ::tensorflow::Input features, ::tensorflow::Input labels )