tf.keras.losses.binary_crossentropy
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. |
| Returns | |
|---|---|
Binary crossentropy loss value. shape = [batch_size, d0, .. dN-1]. |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/keras/losses/binary_crossentropy