public class
BinaryCrossentropy
Computes the cross-entropy loss between true labels and predicted labels.
Use this cross-entropy loss when there are only two label classes (assumed to be 0 and 1). For each example, there should be a single floating-point value per prediction.
Standalone usage:
Operand<TFloat32> labels =
tf.constant(new float[][] { {0.f, 1.f}, {0.f, 0.f} });
Operand<TFloat32> predictions =
tf.constant(new float[][] { {0.6f, 0.4f}, {0.4f, 0.6f} });
BinaryCrossentropy bce = new BinaryCrossentropy(tf);
Operand<TFloat32> result = bce.call(labels, predictions);
// produces 0.815
Calling with sample weight:
Operand<TFloat32> sampleWeight = tf.constant(new float[] {1.f, 0.f});
Operand<TFloat32> result = bce.call(labels, predictions, sampleWeight);
// produces 0.458f
Using SUM reduction type:
BinaryCrossentropy bce = new BinaryCrossentropy(tf, Reduction.SUM);
Operand<TFloat32> result = bce.call(labels, predictions);
// produces 1.630f
Using NONE reduction type:
BinaryCrossentropy bce = new BinaryCrossentropy(tf, Reduction.NONE);
Operand<TFloat32> result = bce.call(labels, predictions);
// produces [0.916f, 0.714f]
Constants
| boolean | FROM_LOGITS_DEFAULT | |
| float | LABEL_SMOOTHING_DEFAULT |
Inherited Fields
Public Constructors
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BinaryCrossentropy(Ops tf)
Creates a Binary Crossentropy Loss using
getSimpleName() as the loss name, FROM_LOGITS_DEFAULT for fromLogits, LABEL_SMOOTHING_DEFAULT for labelSmoothing and a
Loss Reduction of REDUCTION_DEFAULT |
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BinaryCrossentropy(Ops tf, Reduction reduction)
Creates a Binary Crossentropy loss using
getSimpleName() as the loss name, FROM_LOGITS_DEFAULT for fromLogits, and LABEL_SMOOTHING_DEFAULT for labelSmoothing |
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BinaryCrossentropy(Ops tf, boolean fromLogits)
Creates a Binary Crossentropy loss using using
getSimpleName() as the loss name,
labelSmoothing of LABEL_SMOOTHING_DEFAULT, a reduction of REDUCTION_DEFAULT, |
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BinaryCrossentropy(Ops tf, String name, boolean fromLogits)
Creates a Binary Crossentropy loss using labelSmoothing of
LABEL_SMOOTHING_DEFAULT a
reduction of REDUCTION_DEFAULT. |
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BinaryCrossentropy(Ops tf, boolean fromLogits, float labelSmoothing)
Creates a Binary Crossentropy loss using using
getSimpleName() as the loss name,
and a reduction of REDUCTION_DEFAULT. |
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BinaryCrossentropy(Ops tf, String name, boolean fromLogits, float labelSmoothing)
Creates a Binary Crossentropy loss using a reduction of
REDUCTION_DEFAULT. |
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BinaryCrossentropy(Ops tf, boolean fromLogits, float labelSmoothing, Reduction reduction)
Creates a Binary Crossentropy loss
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BinaryCrossentropy(Ops tf, String name, boolean fromLogits, float labelSmoothing, Reduction reduction)
Creates a Binary Crossentropy loss
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Public Methods
| <T extends TNumber> Operand<T> |
Inherited Methods
Constants
public static final boolean FROM_LOGITS_DEFAULT
Constant Value:
false
public static final float LABEL_SMOOTHING_DEFAULT
Constant Value:
0.0
Public Constructors
public BinaryCrossentropy (Ops tf)
Creates a Binary Crossentropy Loss using getSimpleName() as the loss name, FROM_LOGITS_DEFAULT for fromLogits, LABEL_SMOOTHING_DEFAULT for labelSmoothing and a
Loss Reduction of REDUCTION_DEFAULT
Parameters
| tf | the TensorFlow Ops |
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public BinaryCrossentropy (Ops tf, Reduction reduction)
Creates a Binary Crossentropy loss using getSimpleName() as the loss name, FROM_LOGITS_DEFAULT for fromLogits, and LABEL_SMOOTHING_DEFAULT for labelSmoothing
Parameters
| tf | the TensorFlow Ops |
|---|---|
| reduction | Type of Reduction to apply to the loss. |
public BinaryCrossentropy (Ops tf, boolean fromLogits)
Creates a Binary Crossentropy loss using using getSimpleName() as the loss name,
labelSmoothing of LABEL_SMOOTHING_DEFAULT, a reduction of REDUCTION_DEFAULT,
Parameters
| tf | the TensorFlow Ops |
|---|---|
| fromLogits | Whether to interpret predictions as a tensor of logit values |
public BinaryCrossentropy (Ops tf, String name, boolean fromLogits)
Creates a Binary Crossentropy loss using labelSmoothing of LABEL_SMOOTHING_DEFAULT a
reduction of REDUCTION_DEFAULT.
Parameters
| tf | the TensorFlow Ops |
|---|---|
| name | the name of the loss |
| fromLogits | Whether to interpret predictions as a tensor of logit values |
public BinaryCrossentropy (Ops tf, boolean fromLogits, float labelSmoothing)
Creates a Binary Crossentropy loss using using getSimpleName() as the loss name,
and a reduction of REDUCTION_DEFAULT.
Parameters
| tf | the TensorFlow Ops |
|---|---|
| fromLogits | Whether to interpret predictions as a tensor of logit values |
| labelSmoothing | A number in the range, [0, 1]. When 0, no smoothing occurs. When > 0, compute the loss between the predicted labels and a smoothed version of the true labels, where the smoothing squeezes the labels towards 0.5. Larger values of labelSmoothing correspond to heavier smoothing. |
public BinaryCrossentropy (Ops tf, String name, boolean fromLogits, float labelSmoothing)
Creates a Binary Crossentropy loss using a reduction of REDUCTION_DEFAULT.
Parameters
| tf | the TensorFlow Ops |
|---|---|
| name | the name of the loss |
| fromLogits | Whether to interpret predictions as a tensor of logit values |
| labelSmoothing | A number in the range, [0, 1]. When 0, no smoothing occurs. When > 0, compute the loss between the predicted labels and a smoothed version of the true labels, where the smoothing squeezes the labels towards 0.5. Larger values of labelSmoothing correspond to heavier smoothing. |
public BinaryCrossentropy (Ops tf, boolean fromLogits, float labelSmoothing, Reduction reduction)
Creates a Binary Crossentropy loss
Parameters
| tf | the TensorFlow Ops |
|---|---|
| fromLogits | Whether to interpret predictions as a tensor of logit values |
| labelSmoothing | A number in the range, [0, 1]. When 0, no smoothing occurs. When > 0, compute the loss between the predicted labels and a smoothed version of the true labels, where the smoothing squeezes the labels towards 0.5. Larger values of labelSmoothing correspond to heavier smoothing. |
| reduction | Type of Reduction to apply to the loss. |
public BinaryCrossentropy (Ops tf, String name, boolean fromLogits, float labelSmoothing, Reduction reduction)
Creates a Binary Crossentropy loss
Parameters
| tf | the TensorFlow Ops |
|---|---|
| name | the name of the loss |
| fromLogits | Whether to interpret predictions as a tensor of logit values |
| labelSmoothing | A number in the range, [0, 1]. When 0, no smoothing occurs. When > 0, compute the loss between the predicted labels and a smoothed version of the true labels, where the smoothing squeezes the labels towards 0.5. Larger values of labelSmoothing correspond to heavier smoothing. |
| reduction | Type of Reduction to apply to the loss. |
Throws
| IllegalArgumentException | if labelSmoothing is not in the inclusive range of 0. - 1. |
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Public Methods
public Operand<T> call (Operand<? extends TNumber> labels, Operand<T> predictions, Operand<T> sampleWeights)
Generates an Operand that calculates the loss.
If run in Graph mode, the computation will throw TFInvalidArgumentException if the predictions values are outside the
range o [0. to 1.]. In Eager Mode, this call will throw IllegalArgumentException, if
the predictions values are outside the range o [0. to 1.]
Parameters
| labels | the truth values or labels |
|---|---|
| predictions | the predictions, values must be in the range [0. to 1.] inclusive. |
| sampleWeights | Optional SampleWeights acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If SampleWeights is a tensor of size [batch_size], then the total loss for each sample of the batch is rescaled by the corresponding element in the SampleWeights vector. If the shape of SampleWeights is [batch_size, d0, .. dN-1] (or can be broadcast to this shape), then each loss element of predictions is scaled by the corresponding value of SampleWeights. (Note on dN-1: all loss functions reduce by 1 dimension, usually axis=-1.) |
Returns
- the loss
Throws
| IllegalArgumentException | if the predictions are outside the range [0.-1.]. |
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