SparseSoftmaxCrossEntropyWithLogits
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Computes softmax cross entropy cost and gradients to backpropagate.
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.
Constants
String |
OP_NAME |
The name of this op, as known by TensorFlow core engine |
Inherited Methods
From class
java.lang.Object
boolean
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equals(Object arg0)
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final
Class<?>
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getClass()
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int
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hashCode()
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final
void
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notify()
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final
void
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notifyAll()
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String
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toString()
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final
void
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wait(long arg0, int arg1)
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final
void
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wait(long arg0)
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final
void
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wait()
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Constants
public
static
final
String
OP_NAME
The name of this op, as known by TensorFlow core engine
Constant Value:
"SparseSoftmaxCrossEntropyWithLogits"
Public Methods
public
Output<T>
backprop
()
backpropagated gradients (batch_size x num_classes matrix).
Factory method to create a class wrapping a new SparseSoftmaxCrossEntropyWithLogits operation.
Parameters
scope |
current scope |
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
- a new instance of SparseSoftmaxCrossEntropyWithLogits
public
Output<T>
loss
()
Per example loss (batch_size vector).
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Last updated 2021-11-29 UTC.
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