Computes the Poisson loss between y_true & y_pred.

Formula:

loss <- y_pred - y_true * log(y_pred)

Usage

loss_poisson(
  y_true,
  y_pred,
  ...,
  reduction = "sum_over_batch_size",
  name = "poisson",
  dtype = NULL
)

y_true: Ground truth values. shape = [batch_size, d0, .. dN].
y_pred: The predicted values. shape = [batch_size, d0, .. dN].
...: For forward/backward compatability.
reduction: Type of reduction to apply to the loss. In almost all cases this should be "sum_over_batch_size". Supported options are "sum", "sum_over_batch_size" or NULL.
name: Optional name for the loss instance.
dtype: The dtype of the loss's computations. Defaults to NULL, which means using config_floatx(). config_floatx() is a "float32" unless set to different value (via config_set_floatx()). If a keras$DTypePolicy is provided, then the compute_dtype will be utilized.

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

y_true <- random_uniform(c(2, 3), 0, 2)
y_pred <- random_uniform(c(2, 3))
loss <- loss_poisson(y_true, y_pred)
loss

## tf.Tensor([1.6422468  0.81166863], shape=(2), dtype=float32)