tf.keras.losses.SparseCategoricalCrossentropy

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Computes the crossentropy loss between the labels and predictions.

tf.keras.losses.SparseCategoricalCrossentropy(
    from_logits=False, reduction=losses_utils.ReductionV2.AUTO,
    name='sparse_categorical_crossentropy'
)

Use this crossentropy loss function when there are two or more label classes. We expect labels to be provided as integers. If you want to provide labels using one-hot representation, please use CategoricalCrossentropy loss. There should be # classes floating point values per feature for y_pred and a single floating point value per feature for y_true.

In the snippet below, there is a single floating point value per example for y_true and # classes floating pointing values per example for y_pred. The shape of y_true is [batch_size] and the shape of y_pred is [batch_size, num_classes].

Usage:

cce = tf.keras.losses.SparseCategoricalCrossentropy()
loss = cce(
  tf.convert_to_tensor([0, 1, 2]),
  tf.convert_to_tensor([[.9, .05, .05], [.5, .89, .6], [.05, .01, .94]]))
print('Loss: ', loss.numpy())  # Loss: 0.3239

Usage with the compile API:

model = tf.keras.Model(inputs, outputs)
model.compile('sgd', loss=tf.keras.losses.SparseCategoricalCrossentropy())

Args:

• from_logits: Whether y_pred is expected to be a logits tensor. By default, we assume that y_pred encodes a probability distribution. Note: Using from_logits=True may be more numerically stable.
• reduction: (Optional) Type of tf.keras.losses.Reduction to apply to loss. Default value is AUTO. AUTO indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to SUM_OVER_BATCH_SIZE. When used with tf.distribute.Strategy, outside of built-in training loops such as tf.keras compile and fit, using AUTO or SUM_OVER_BATCH_SIZE will raise an error. Please see https://www.tensorflow.org/tutorials/distribute/custom_training for more details on this.
• name: Optional name for the op.

Methods

__call__

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__call__(
    y_true, y_pred, sample_weight=None
)

Invokes the Loss instance.

Args:

• y_true: Ground truth values. shape = [batch_size, d0, .. dN]
• y_pred: The predicted values. shape = [batch_size, d0, .. dN]
• sample_weight: Optional sample_weight acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If sample_weight 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 sample_weight vector. If the shape of sample_weight is [batch_size, d0, .. dN-1] (or can be broadcasted to this shape), then each loss element of y_pred is scaled by the corresponding value of sample_weight. (Note ondN-1: all loss functions reduce by 1 dimension, usually axis=-1.)

Returns:

Weighted loss float Tensor. If reduction is NONE, this has shape [batch_size, d0, .. dN-1]; otherwise, it is scalar. (Note dN-1 because all loss functions reduce by 1 dimension, usually axis=-1.)

Raises:

• ValueError: If the shape of sample_weight is invalid.

from_config

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@classmethod
from_config(
    config
)

Instantiates a Loss from its config (output of get_config()).

Args:

• config: Output of get_config().

Returns:

A Loss instance.

get_config

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get_config()