tf.keras.metrics.BinaryCrossentropy

Computes the crossentropy metric between the labels and predictions.

Main aliases

tf.metrics.BinaryCrossentropy

Compat aliases for migration

See Migration guide for more details.

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

This is the crossentropy metric class to be used when there are only two label classes (0 and 1).

Args
`name`	(Optional) string name of the metric instance.
`dtype`	(Optional) data type of the metric result.
`from_logits`	(Optional )Whether output is expected to be a logits tensor. By default, we consider that output encodes a probability distribution.
`label_smoothing`	(Optional) 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`".

m = tf.keras.metrics.BinaryCrossentropy()
m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
m.result().numpy()
0.81492424

m.reset_states()
m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
               sample_weight=[1, 0])
m.result().numpy()
0.9162905

Usage with compile() API:

model.compile(
    optimizer='sgd',
    loss='mse',
    metrics=[tf.keras.metrics.BinaryCrossentropy()])

Methods

reset_states()

Resets all of the metric state variables.

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

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(
    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.