tf.keras.losses.poisson

TensorFlow 1 version View source on GitHub

Computes the Poisson loss between y_true and y_pred.

tf.keras.losses.poisson(
    y_true, y_pred
)

The Poisson loss is the mean of the elements of the Tensor y_pred - y_true * log(y_pred).

Usage:

y_true = np.random.randint(0, 2, size=(2, 3)) 
y_pred = np.random.random(size=(2, 3)) 
loss = tf.keras.losses.poisson(y_true, y_pred) 
assert loss.shape == (2,) 
y_pred = y_pred + 1e-7 
assert np.allclose( 
    loss.numpy(), np.mean(y_pred - y_true * np.log(y_pred), axis=-1), 
    atol=1e-5) 

Args:

  • y_true: Ground truth values. shape = [batch_size, d0, .. dN].
  • y_pred: The predicted values. shape = [batch_size, d0, .. dN].

Returns:

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

Raises:

  • InvalidArgumentError: If y_true and y_pred have incompatible shapes.