tf.keras.losses.BinaryCrossentropy

Class BinaryCrossentropy

Computes the cross-entropy loss between true labels and predicted labels.

Aliases:

  • Class tf.compat.v1.keras.losses.BinaryCrossentropy
  • Class tf.compat.v2.keras.losses.BinaryCrossentropy
  • Class tf.compat.v2.losses.BinaryCrossentropy
  • Class tf.keras.losses.BinaryCrossentropy

Defined in python/keras/losses.py.

Use this cross-entropy loss when there are only two label classes (assumed to be 0 and 1). For each example, there should be a single floating-point value per prediction.

In the snippet below, each of the four examples has only a single floating-pointing value, and both y_pred and y_true have the shape [batch_size].

Usage:

bce = tf.keras.losses.BinaryCrossentropy()
loss = bce([0., 0., 1., 1.], [1., 1., 1., 0.])
print('Loss: ', loss.numpy())  # Loss: 11.522857

Usage with the tf.keras API:

model = tf.keras.Model(inputs, outputs)
model.compile('sgd', loss=tf.keras.losses.BinaryCrossentropy())

Args:

  • from_logits: Whether to interpret y_pred as a tensor of logit values. By default, we assume that y_pred contains probabilities (i.e., values in [0, 1]).
  • label_smoothing: Float in [0, 1]. When 0, no smoothing occurs. When > 0, we compute the loss between the predicted labels and a smoothed version of the true labels, where the smoothing squeezes the labels towards 0.5. Larger values of label_smoothing correspond to heavier smoothing.
  • reduction: (Optional) Type of tf.keras.losses.Reduction to apply to loss. Default value is AUTO. AUTO indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to SUM_OVER_BATCH_SIZE. When used with tf.distribute.Strategy, outside of built-in training loops such as tf.keras compile and fit, using AUTO or SUM_OVER_BATCH_SIZE will raise an error. Please see https://www.tensorflow.org/alpha/tutorials/distribute/training_loops for more details on this.
  • name: (Optional) Name for the op.

__init__

__init__(
    from_logits=False,
    label_smoothing=0,
    reduction=losses_utils.ReductionV2.AUTO,
    name='binary_crossentropy'
)

Methods

__call__

__call__(
    y_true,
    y_pred,
    sample_weight=None
)

Invokes the Loss instance.

Args:

  • y_true: Ground truth values.
  • y_pred: The predicted values.
  • sample_weight: Optional Tensor whose rank is either 0, or the same rank as y_true, or is broadcastable to y_true. sample_weight acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If sample_weight is a tensor of size [batch_size], then the total loss for each sample of the batch is rescaled by the corresponding element in the sample_weight vector. If the shape of sample_weight matches the shape of y_pred, then the loss of each measurable element of y_pred is scaled by the corresponding value of sample_weight.

Returns:

Weighted loss float Tensor. If reduction is NONE, this has the same shape as y_true; otherwise, it is scalar.

Raises:

  • ValueError: If the shape of sample_weight is invalid.

from_config

from_config(
    cls,
    config
)

Instantiates a Loss from its config (output of get_config()).

Args:

  • config: Output of get_config().

Returns:

A Loss instance.

get_config

get_config()