Computes the binary crossentropy loss.
tf.keras.losses.binary_crossentropy(
y_true, y_pred, from_logits=False, label_smoothing=0
)
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)
Args |
|---|
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.
|
Returns |
|---|
Binary crossentropy loss value. shape = [batch_size, d0, .. dN-1].
|
TensorFlow 1 version
View source on GitHub