Computes the Poisson loss between y_true and y_pred.
tf.keras.metrics.poisson(
y_true, y_pred
)
The Poisson loss is the mean of the elements of the Tensor
y_pred - y_true * log(y_pred)
.
Standalone 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.
|