tf.keras.losses.binary_crossentropy

TensorFlow 1 version View source on GitHub

Computes the binary crossentropy loss.

tf.keras.losses.binary_crossentropy(
    y_true, y_pred, from_logits=False, label_smoothing=0
)

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].