# tf.keras.losses.BinaryCrossentropy

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

### Used in the notebooks

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].

#### Standalone usage:

y_true = [[0., 1.], [0., 0.]]
y_pred = [[0.6, 0.4], [0.4, 0.6]]
# Using 'auto'/'sum_over_batch_size' reduction type.
bce = tf.keras.losses.BinaryCrossentropy()
bce(y_true, y_pred).numpy()
0.815
# Calling with 'sample_weight'.
bce(y_true, y_pred, sample_weight=[1, 0]).numpy()
0.458
# Using 'sum' reduction type.
bce = tf.keras.losses.BinaryCrossentropy(
reduction=tf.keras.losses.Reduction.SUM)
bce(y_true, y_pred).numpy()
1.630

# Using 'none' reduction type.
bce = tf.keras.losses.BinaryCrossentropy(
reduction=tf.keras.losses.Reduction.NONE)
bce(y_true, y_pred).numpy()
array([0.916 , 0.714], dtype=float32)

Usage with the tf.keras API:

model.compile(optimizer='sgd', loss=tf.keras.losses.BinaryCrossentropy())

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]). **Note - Using from_logits=True may be more numerically stable.
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 this custom training tutorial for more details.
name (Optional) Name for the op. Defaults to 'binary_crossentropy'.

## Methods

### from_config

View source

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

Args
config Output of get_config().

Returns
A Loss instance.

### get_config

View source

Returns the config dictionary for a Loss instance.

### __call__

View source

Invokes the Loss instance.

Args
y_true Ground truth values. shape = [batch_size, d0, .. dN], except sparse loss functions such as sparse categorical crossentropy where shape = [batch_size, d0, .. dN-1]
y_pred The predicted values. shape = [batch_size, d0, .. dN]
sample_weight Optional 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 is [batch_size, d0, .. dN-1] (or can be broadcasted to this shape), then each loss element of y_pred is scaled by the corresponding value of sample_weight. (Note ondN-1: all loss functions reduce by 1 dimension, usually axis=-1.)

Returns
Weighted loss float Tensor. If reduction is NONE, this has shape [batch_size, d0, .. dN-1]; otherwise, it is scalar. (Note dN-1 because all loss functions reduce by 1 dimension, usually axis=-1.)

Raises
ValueError If the shape of sample_weight is invalid.