tf.keras.metrics.CategoricalCrossentropy

View source on GitHub

Computes the crossentropy metric between the labels and predictions.

View aliases

Compat aliases for migration

See Migration guide for more details.

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

tf.keras.metrics.CategoricalCrossentropy(
    name='categorical_crossentropy', dtype=None, from_logits=False,
    label_smoothing=0
)

This is the crossentropy metric class to be used when there are multiple label classes (2 or more). Here we assume that labels are given as a one_hot representation. eg., When labels values are [2, 0, 1], y_true = [[0, 0, 1], [1, 0, 0], [0, 1, 0]].

Usage:

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

# EPSILON = 1e-7, y = y_true, y` = y_pred
# y` = clip_ops.clip_by_value(output, EPSILON, 1. - EPSILON)
# y` = [[0.05, 0.95, EPSILON], [0.1, 0.8, 0.1]]

# xent = -sum(y * log(y'), axis = -1)
#      = -((log 0.95), (log 0.1))
#      = [0.051, 2.302]
# Reduced xent = (0.051 + 2.302) / 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.CategoricalCrossentropy()])

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.
label_smoothing	Float in [0, 1]. When > 0, label values are smoothed, meaning the confidence on label values are relaxed. e.g. label_smoothing=0.2 means that we will use a value of 0.1 for label 0 and 0.9 for label 1"

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

View source

reset_states()

Resets all of the metric state variables.

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

result

View source

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.