https://www.tensorflow.org/api_docs/python/tf/keras/losses/BinaryCrossentropy
The example in tensorflow site.
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
as far as i know BinaryCrossentropy is
loss = y_true * log(y_pred) + (1 – y_true) * log(1 – y_pred)
0 * log(2.94) + (1 – 0) * log(1 – 2.94)
log(1 – 2.94)
log(-1.94) ????
it is impossible to calculate
>Solution :
Yes, you are right, but notice in the source code, y_pred or output in that case is clipped to be between a very small epsilon value and 1 minus this epsilon value 1e-7:
...
output = tf.clip_by_value(output, epsilon_, 1. - epsilon_)
# Compute cross entropy from probabilities.
bce = target * tf.math.log(output + epsilon())
bce += (1 - target) * tf.math.log(1 - output + epsilon())
...