Computes the crossentropy loss between the labels and predictions.
Use this crossentropy loss function when there are two or more label classes. We expect labels
to be provided in a one_hot representation. If you want to provide labels as integers, please use
SparseCategoricalCrossentropy loss. There should be # classes floating point
values per feature.
Standalone usage:
Operand<TFloat32> labels =
tf.constant(new float[][] { {0, 1, 0}, {0, 0, 1} });
Operand<TFloat32> predictions =
tf.constant(new float[][] { {0.05f, 0.95f, 0f}, {0.1f, 0.8f, 0.1f} });
CategoricalCrossentropy cce = new CategoricalCrossentropy(tf);
Operand<TFloat32> result = cce.call(labels, predictions);
// produces 1.177
Calling with sample weight:
Operand<TFloat32> sampleWeight = tf.constant(new float[] {0.3f, 0.7f});
Operand<TFloat32> result = cce.call(labels, predictions, sampleWeight);
// produces 0.814f
Using SUM reduction type:
CategoricalCrossentropy cce = new CategoricalCrossentropy(tf, Reduction.SUM);
Operand<TFloat32> result = cce.call(labels, predictions);
// produces 2.354f
Using NONE reduction type:
CategoricalCrossentropy cce =
new CategoricalCrossentropy(tf, Reduction.NONE);
Operand<TFloat32> result = cce.call(labels, predictions);
// produces [0.0513f, 2.303f]
Constants
| int | DEFAULT_AXIS | |
| boolean | FROM_LOGITS_DEFAULT | |
| float | LABEL_SMOOTHING_DEFAULT |
Inherited Fields
Public Constructors
|
CategoricalCrossentropy(Ops tf)
Creates a categorical cross entropy Loss using
getSimpleName() as the loss name,
FROM_LOGITS_DEFAULT for fromLogits, LABEL_SMOOTHING_DEFAULT for
labelSmoothing, a Loss Reduction of REDUCTION_DEFAULT, and an axis of DEFAULT_AXIS |
|
|
CategoricalCrossentropy(Ops tf, String name)
Creates a categorical cross entropy Loss using
FROM_LOGITS_DEFAULT for fromLogits,
LABEL_SMOOTHING_DEFAULT for labelSmoothing, a Loss Reduction of REDUCTION_DEFAULT, and an axis of DEFAULT_AXIS |
|
|
CategoricalCrossentropy(Ops tf, Reduction reduction)
Creates a categorical cross entropy Loss using
getSimpleName() as the loss name,
FROM_LOGITS_DEFAULT for fromLogits, LABEL_SMOOTHING_DEFAULT for
labelSmoothing and an axis of DEFAULT_AXIS |
|
|
CategoricalCrossentropy(Ops tf, String name, Reduction reduction)
Creates a categorical cross entropy Loss
FROM_LOGITS_DEFAULT for fromLogits, LABEL_SMOOTHING_DEFAULT for labelSmoothing, and an axis of DEFAULT_AXIS |
|
|
CategoricalCrossentropy(Ops tf, boolean fromLogits)
Creates a categorical cross entropy Loss using
getSimpleName() as the loss name,
LABEL_SMOOTHING_DEFAULT for labelSmoothing, a Loss Reduction of REDUCTION_DEFAULT, and an axis of DEFAULT_AXIS |
|
|
CategoricalCrossentropy(Ops tf, String name, boolean fromLogits)
Creates a categorical cross entropy Loss using
LABEL_SMOOTHING_DEFAULT for
labelSmoothing, a Loss Reduction of REDUCTION_DEFAULT, and a channel axis of
DEFAULT_AXIS |
|
|
CategoricalCrossentropy(Ops tf, boolean fromLogits, float labelSmoothing)
Creates a categorical cross entropy Loss using
getSimpleName() as the loss name,
a Loss Reduction of REDUCTION_DEFAULT, and a channel axis of DEFAULT_AXIS |
|
|
CategoricalCrossentropy(Ops tf, String name, boolean fromLogits, float labelSmoothing)
Creates a categorical cross entropy Loss using a Loss Reduction of
REDUCTION_DEFAULT, and a channel axis of DEFAULT_AXIS |
|
|
CategoricalCrossentropy(Ops tf, boolean fromLogits, float labelSmoothing, Reduction reduction)
Creates a categorical cross entropy Loss using
getSimpleName() as the loss name
and a channel axis of DEFAULT_AXIS |
|
|
CategoricalCrossentropy(Ops tf, String name, boolean fromLogits, float labelSmoothing, Reduction reduction, int axis)
Creates a categorical cross entropy Loss
|
Public Methods
| <T extends TNumber> Operand<T> |
Inherited Methods
Constants
public static final int DEFAULT_AXIS
public static final boolean FROM_LOGITS_DEFAULT
public static final float LABEL_SMOOTHING_DEFAULT
Public Constructors
public CategoricalCrossentropy (Ops tf)
Creates a categorical cross entropy Loss using getSimpleName() as the loss name,
FROM_LOGITS_DEFAULT for fromLogits, LABEL_SMOOTHING_DEFAULT for
labelSmoothing, a Loss Reduction of REDUCTION_DEFAULT, and an axis of DEFAULT_AXIS
Parameters
| tf | the TensorFlow Ops |
|---|
public CategoricalCrossentropy (Ops tf, String name)
Creates a categorical cross entropy Loss using FROM_LOGITS_DEFAULT for fromLogits,
LABEL_SMOOTHING_DEFAULT for labelSmoothing, a Loss Reduction of REDUCTION_DEFAULT, and an axis of DEFAULT_AXIS
Parameters
| tf | the TensorFlow Ops |
|---|---|
| name | the name of this loss |
public CategoricalCrossentropy (Ops tf, Reduction reduction)
Creates a categorical cross entropy Loss using getSimpleName() as the loss name,
FROM_LOGITS_DEFAULT for fromLogits, LABEL_SMOOTHING_DEFAULT for
labelSmoothing and an axis of DEFAULT_AXIS
Parameters
| tf | the TensorFlow Ops |
|---|---|
| reduction | Type of Reduction to apply to loss. |
public CategoricalCrossentropy (Ops tf, String name, Reduction reduction)
Creates a categorical cross entropy Loss FROM_LOGITS_DEFAULT for fromLogits, LABEL_SMOOTHING_DEFAULT for labelSmoothing, and an axis of DEFAULT_AXIS
Parameters
| tf | the TensorFlow Ops |
|---|---|
| name | the name of this loss |
| reduction | Type of Reduction to apply to loss. |
public CategoricalCrossentropy (Ops tf, boolean fromLogits)
Creates a categorical cross entropy Loss using getSimpleName() as the loss name,
LABEL_SMOOTHING_DEFAULT for labelSmoothing, a Loss Reduction of REDUCTION_DEFAULT, and an axis of DEFAULT_AXIS
Parameters
| tf | the TensorFlow Ops |
|---|---|
| fromLogits | Whether to interpret predictions as a tensor of logit values |
public CategoricalCrossentropy (Ops tf, String name, boolean fromLogits)
Creates a categorical cross entropy Loss using LABEL_SMOOTHING_DEFAULT for
labelSmoothing, a Loss Reduction of REDUCTION_DEFAULT, and a channel axis of
DEFAULT_AXIS
Parameters
| tf | the TensorFlow Ops |
|---|---|
| name | the name of this loss |
| fromLogits | Whether to interpret predictions as a tensor of logit values |
public CategoricalCrossentropy (Ops tf, boolean fromLogits, float labelSmoothing)
Creates a categorical cross entropy Loss using getSimpleName() as the loss name,
a Loss Reduction of REDUCTION_DEFAULT, and a channel axis of DEFAULT_AXIS
Parameters
| tf | the TensorFlow Ops |
|---|---|
| fromLogits | Whether to interpret predictions as a tensor of logit values |
| labelSmoothing | Float in [0, 1]. When > 0, label values are
smoothed, meaning the confidence on label values are relaxed. e.g. labelSmoothing=0.2
means that we will use a value of 0.1 for label 0 and
0.9 for label 1
|
public CategoricalCrossentropy (Ops tf, String name, boolean fromLogits, float labelSmoothing)
Creates a categorical cross entropy Loss using a Loss Reduction of REDUCTION_DEFAULT, and a channel axis of DEFAULT_AXIS
Parameters
| tf | the TensorFlow Ops |
|---|---|
| name | the name of this loss |
| fromLogits | Whether to interpret predictions as a tensor of logit values |
| labelSmoothing | Float in [0, 1]. When > 0, label values are
smoothed, meaning the confidence on label values are relaxed. e.g. labelSmoothing=0.2
means that we will use a value of 0.1 for label 0 and
0.9 for label 1
|
public CategoricalCrossentropy (Ops tf, boolean fromLogits, float labelSmoothing, Reduction reduction)
Creates a categorical cross entropy Loss using getSimpleName() as the loss name
and a channel axis of DEFAULT_AXIS
Parameters
| tf | the TensorFlow Ops |
|---|---|
| fromLogits | Whether to interpret predictions as a tensor of logit values |
| labelSmoothing | Float in [0, 1]. When > 0, label values are
smoothed, meaning the confidence on label values are relaxed. e.g. x=0.2 means
that we will use a value of 0.1 for label 0 and 0.9
for label 1 |
| reduction | Type of Reduction to apply to loss. |
public CategoricalCrossentropy (Ops tf, String name, boolean fromLogits, float labelSmoothing, Reduction reduction, int axis)
Creates a categorical cross entropy Loss
Parameters
| tf | the TensorFlow Ops |
|---|---|
| name | the name of this loss |
| fromLogits | Whether to interpret predictions as a tensor of logit values |
| labelSmoothing | Float in [0, 1]. When > 0, label values are
smoothed, meaning the confidence on label values are relaxed. e.g. labelSmoothing=0.2
means that we will use a value of 0.1 for label 0 and
0.9 for label 1 |
| reduction | Type of Reduction to apply to loss. |
| axis | The channels axis. axis=-1 corresponds to data format "Channels Last"
and axis=1 corresponds to data format "Channels First". CHANNELS_LAST and CHANNELS_FIRST |
Throws
| IllegalArgumentException | if labelSmoothing is not in the inclusive range of 0. - 1. |
|---|
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.]. |
|---|