tf.keras.metrics.SparseCategoricalCrossentropy

TensorFlow 1 version View source on GitHub

Computes the crossentropy metric between the labels and predictions.

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:

# 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 
m = tf.keras.metrics.SparseCategoricalCrossentropy() 
_ = m.update_state([1, 2], 
                   [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]) 
m.result().numpy() 
1.1769392 
m.reset_states() 
_ = m.update_state([1, 2], 
                   [[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.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

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.