tf.keras.metrics.CategoricalCrossentropy

TensorFlow 1 version View source on GitHub

Computes the crossentropy metric between the labels and predictions.

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:

# 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
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]])
m.result().numpy()
1.1769392
 
m.reset_states()
_ = m.update_state([[0, 1, 0], [0, 0, 1]],
                   [[0.05, 0.95, 0], [0.1, 0.8, 0.1]],
                   sample_weight=tf.constant([0.3, 0.7]))
m.result().numpy()
1.6271976

Usage with tf.keras API:

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

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"

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 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 metric. If a scalar is provided, then the metric is simply scaled by the given value. If sample_weight is a tensor of size [batch_size], then the metric 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 metric element of y_pred is scaled by the corresponding value of sample_weight. (Note on dN-1: all metric functions reduce by 1 dimension, usually the last axis (-1)).

Returns
Update op.