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op {
graph_op_name: "SparseSoftmaxCrossEntropyWithLogits"
in_arg {
name: "features"
description: <<END
batch_size x num_classes matrix
END
}
in_arg {
name: "labels"
description: <<END
batch_size vector with values in [0, num_classes).
This is the label for the given minibatch entry.
END
}
out_arg {
name: "loss"
description: <<END
Per example loss (batch_size vector).
END
}
out_arg {
name: "backprop"
description: <<END
backpropagated gradients (batch_size x num_classes matrix).
END
}
summary: "Computes softmax cross entropy cost and gradients to backpropagate."
description: <<END
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.
END
}
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