# tf.keras.losses.BinaryCrossentropy

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

Inherits From: Loss

### Used in the notebooks

Use this cross-entropy loss for binary (0 or 1) classification applications. The loss function requires the following inputs:

• y_true (true label): This is either 0 or 1.
• y_pred (predicted value): This is the model's prediction, i.e, a single floating-point value which either represents a logit, (i.e, value in [-inf, inf] when from_logits=True) or a probability (i.e, value in [0., 1.] when from_logits=False).

Recommended Usage: (set from_logits=True)

With tf.keras API:

model.compile(
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
....
)

As a standalone function:

# Example 1: (batch_size = 1, number of samples = 4)
y_true = [0, 1, 0, 0]
y_pred = [-18.6, 0.51, 2.94, -12.8]
bce = tf.keras.losses.BinaryCrossentropy(from_logits=True)
bce(y_true, y_pred).numpy()
0.865
# Example 2: (batch_size = 2, number of samples = 4)
y_true = [[0, 1], [0, 0]]
y_pred = [[-18.6, 0.51], [2.94, -12.8]]
# Using default 'auto'/'sum_over_batch_size' reduction type.
bce = tf.keras.losses.BinaryCrossentropy(from_logits=True)
bce(y_true, y_pred).numpy()
0.865
# Using 'sample_weight' attribute
bce(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy()
0.243
# Using 'sum' reduction` type.
bce = tf.keras.losses.BinaryCrossentropy(from_logits=True,
reduction=tf.keras.losses.Reduction.SUM)
bce(y_true, y_pred).numpy()
1.730
# Using 'none' reduction type.
bce = tf.keras.losses.BinaryCrossentropy(from_logits=True,
reduction=tf.keras.losses.Reduction.NONE)
bce(y_true, y_pred).numpy()
array([0.235, 1.496], dtype=float32)

Default Usage: (set from_logits=False)

# Make the following updates to the above "Recommended Usage" section
# 1. Set `from_logits=False`
tf.keras.losses.BinaryCrossentropy() # OR ...('from_logits=False')
# 2. Update `y_pred` to use probabilities instead of logits
y_pred = [0.6, 0.3, 0.2, 0.8] # OR [[0.6, 0.3], [0.2, 0.8]]

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.