tf.keras.metrics.SparseCategoricalCrossentropy

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Computes the crossentropy metric between the labels and predictions.

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.metrics.SparseCategoricalCrossentropy, `tf.compat.v2.keras.metrics.SparseCategoricalCrossentropy`, `tf.compat.v2.metrics.SparseCategoricalCrossentropy`

tf.keras.metrics.SparseCategoricalCrossentropy(
    name='sparse_categorical_crossentropy', dtype=None, from_logits=False, axis=-1
)

Use this crossentropy metric 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 metric. 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:

m = tf.keras.metrics.SparseCategoricalCrossentropy()
m.update_state(
  [1, 2],
  [[0.05, 0.95, 0], [0.1, 0.8, 0.1]])

# y_true = one_hot(y_true) = [[0, 1, 0], [0, 0, 1]]
# logits = log(y_pred)
# softmax = exp(logits) / sum(exp(logits), axis=-1)
# softmax = [[0.05, 0.95, EPSILON], [0.1, 0.8, 0.1]]

# xent = -sum(y * log(softmax), 1)
# log(softmax) = [[-2.9957, -0.0513, -16.1181], [-2.3026, -0.2231, -2.3026]]
# y_true * log(softmax) = [[0, -0.0513, 0], [0, 0, -2.3026]]

# xent = [0.0513, 2.3026]
# Reduced xent = (0.0513 + 2.3026) / 2

print('Final result: ', m.result().numpy())  # Final result: 1.176

Usage with tf.keras API:

model = tf.keras.Model(inputs, outputs)
model.compile(
  'sgd',
  loss='mse',
  metrics=[tf.keras.metrics.SparseCategoricalCrossentropy()])

Args
name	(Optional) string name of the metric instance.
dtype	(Optional) data type of the metric result.
from_logits	(Optional ) Whether y_pred is expected to be a logits tensor. By default, we assume that y_pred encodes a probability distribution.
axis	(Optional) Defaults to -1. The dimension along which the metric is computed.

Args
fn	The metric function to wrap, with signature fn(y_true, y_pred, **kwargs).
name	(Optional) string name of the metric instance.
dtype	(Optional) data type of the metric result.
**kwargs	The keyword arguments that are passed on to fn.

Methods

reset_states

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reset_states()

Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

result

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result()

Computes and returns the metric value tensor.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.

update_state

View source

update_state(
    y_true, y_pred, sample_weight=None
)

Accumulates metric statistics.

y_true and y_pred should have the same shape.

Args
y_true	The ground truth values.
y_pred	The predicted values.
sample_weight	Optional weighting of each example. Defaults to 1. Can be a Tensor whose rank is either 0, or the same rank as y_true, and must be broadcastable to y_true.

Returns
Update op.