loss_poisson {keras3} | R Documentation |
Computes the Poisson loss between y_true
& y_pred
.
Description
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
)
Arguments
y_true |
Ground truth values. shape = |
y_pred |
The predicted values. shape = |
... |
For forward/backward compatability. |
reduction |
Type of reduction to apply to the loss. In almost all cases
this should be |
name |
Optional name for the loss instance. |
dtype |
The dtype of the loss's computations. Defaults to |
Value
Poisson loss values with shape = [batch_size, d0, .. dN-1]
.
Examples
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([2.5907533 0.66836613], shape=(2), dtype=float32)
See Also
Other losses:
Loss()
loss_binary_crossentropy()
loss_binary_focal_crossentropy()
loss_categorical_crossentropy()
loss_categorical_focal_crossentropy()
loss_categorical_hinge()
loss_cosine_similarity()
loss_ctc()
loss_dice()
loss_hinge()
loss_huber()
loss_kl_divergence()
loss_log_cosh()
loss_mean_absolute_error()
loss_mean_absolute_percentage_error()
loss_mean_squared_error()
loss_mean_squared_logarithmic_error()
loss_sparse_categorical_crossentropy()
loss_squared_hinge()
loss_tversky()
metric_binary_crossentropy()
metric_binary_focal_crossentropy()
metric_categorical_crossentropy()
metric_categorical_focal_crossentropy()
metric_categorical_hinge()
metric_hinge()
metric_huber()
metric_kl_divergence()
metric_log_cosh()
metric_mean_absolute_error()
metric_mean_absolute_percentage_error()
metric_mean_squared_error()
metric_mean_squared_logarithmic_error()
metric_poisson()
metric_sparse_categorical_crossentropy()
metric_squared_hinge()