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tf.keras.metrics.binary_crossentropy

Computes the binary crossentropy loss.

Used in the notebooks

Used in the tutorials

Standalone usage:

y_true = [[0, 1], [0, 0]]
y_pred = [[0.6, 0.4], [0.4, 0.6]]
loss = tf.keras.losses.binary_crossentropy(y_true, y_pred)
assert loss.shape == (2,)
loss.numpy()
array([0.916 , 0.714], dtype=float32)

y_true Ground truth values. shape = [batch_size, d0, .. dN].
y_pred The predicted values. shape = [batch_size, d0, .. dN].
from_logits Whether y_pred is expected to be a logits tensor. By default, we assume that y_pred encodes a probability distribution.
label_smoothing Float in [0, 1]. If > 0 then smooth the labels by squeezing them towards 0.5 That is, using 1. - 0.5 * label_smoothing for the target class and 0.5 * label_smoothing for the non-target class.
axis The axis along which the mean is computed. Defaults to -1.

Binary crossentropy loss value. shape = [batch_size, d0, .. dN-1].