# tf.keras.losses.Poisson

Computes the Poisson loss between `y_true` and `y_pred`.

``````tf.keras.losses.Poisson(
reduction=losses_utils.ReductionV2.AUTO, name='poisson'
)
``````

`loss = y_pred - y_true * log(y_pred)`

#### Usage:

``````p = tf.keras.losses.Poisson()
loss = p([1., 9., 2.], [4., 8., 12.])
print('Loss: ', loss.numpy())  # Loss: -0.35702705
``````

Usage with the `compile` API:

``````model = tf.keras.Model(inputs, outputs)
model.compile('sgd', loss=tf.keras.losses.Poisson())
``````

## Methods

### `__call__`

View source

``````__call__(
y_true, y_pred, sample_weight=None
)
``````

Invokes the `Loss` instance.

#### Args:

• `y_true`: Ground truth values. shape = `[batch_size, d0, .. dN]`
• `y_pred`: The predicted values. shape = `[batch_size, d0, .. dN]`
• `sample_weight`: Optional `sample_weight` acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If `sample_weight` is a tensor of size `[batch_size]`, then the total loss for each sample of the batch is rescaled by the corresponding element in the `sample_weight` vector. If the shape of `sample_weight` is `[batch_size, d0, .. dN-1]` (or can be broadcasted to this shape), then each loss element of `y_pred` is scaled by the corresponding value of `sample_weight`. (Note on`dN-1`: all loss functions reduce by 1 dimension, usually axis=-1.)

#### Returns:

Weighted loss float `Tensor`. If `reduction` is `NONE`, this has shape `[batch_size, d0, .. dN-1]`; otherwise, it is scalar. (Note `dN-1` because all loss functions reduce by 1 dimension, usually axis=-1.)

#### Raises:

• `ValueError`: If the shape of `sample_weight` is invalid.

### `from_config`

View source

``````@classmethod
from_config(
config
)
``````

Instantiates a `Loss` from its config (output of `get_config()`).

#### Args:

• `config`: Output of `get_config()`.

#### Returns:

A `Loss` instance.

### `get_config`

View source

``````get_config()
``````