Type CategoricalCrossentropy
Namespace tensorflow.keras.losses
Parent LossFunctionWrapper
Interfaces ICategoricalCrossentropy
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. In the snippet below, there is `# classes` floating pointing values per
example. The shape of both `y_pred` and `y_true` are
`[batch_size, num_classes]`. Usage:
Usage with the `compile` API:
Show Example
cce = tf.keras.losses.CategoricalCrossentropy()
loss = cce(
[[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]],
[[.9,.05,.05], [.05,.89,.06], [.05,.01,.94]])
print('Loss: ', loss.numpy()) # Loss: 0.0945
Methods
Properties
Public static methods
CategoricalCrossentropy NewDyn(ImplicitContainer<T> from_logits, ImplicitContainer<T> label_smoothing, ImplicitContainer<T> reduction, ImplicitContainer<T> name)
Initialize self. See help(type(self)) for accurate signature.