Missed TensorFlow World? Check out the recap. Learn more

tf.keras.metrics.SparseCategoricalCrossentropy

View source on GitHub

Class SparseCategoricalCrossentropy

Computes the crossentropy metric between the labels and predictions.

Aliases:

  • Class tf.compat.v1.keras.metrics.SparseCategoricalCrossentropy
  • Class tf.compat.v2.keras.metrics.SparseCategoricalCrossentropy
  • Class tf.compat.v2.metrics.SparseCategoricalCrossentropy
  • Class tf.metrics.SparseCategoricalCrossentropy

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.

__init__

View source

__init__(
    name='sparse_categorical_crossentropy',
    dtype=None,
    from_logits=False,
    axis=-1
)

Creates a MeanMetricWrapper instance.

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.

__new__

View source

__new__(
    cls,
    *args,
    **kwargs
)

Create and return a new object. See help(type) for accurate signature.

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.