# tf.keras.metrics.Poisson

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

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

`name` (Optional) string name of the metric instance.
`dtype` (Optional) data type of the metric result.

#### Standalone usage:

````m = tf.keras.metrics.Poisson()`
`m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])`
`m.result().numpy()`
`0.49999997`
```
````m.reset_state()`
`m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],`
`               sample_weight=[1, 0])`
`m.result().numpy()`
`0.99999994`
```

Usage with `compile()` API:

``````model.compile(optimizer='sgd',
loss='mse',
metrics=[tf.keras.metrics.Poisson()])
``````

## Methods

### `reset_state`

View source

Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

### `result`

View source

Computes and returns the metric value tensor.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.

### `update_state`

View source

Accumulates metric statistics.

For sparse categorical metrics, the shapes of `y_true` and `y_pred` are different.

Args
`y_true` Ground truth label values. shape = `[batch_size, d0, .. dN-1]` or shape = `[batch_size, d0, .. dN-1, 1]`.
`y_pred` The predicted probability values. shape = `[batch_size, d0, .. dN]`.
`sample_weight` Optional `sample_weight` acts as a coefficient for the metric. If a scalar is provided, then the metric is simply scaled by the given value. If `sample_weight` is a tensor of size `[batch_size]`, then the metric 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 metric element of `y_pred` is scaled by the corresponding value of `sample_weight`. (Note on `dN-1`: all metric functions reduce by 1 dimension, usually the last axis (-1)).

Returns
Update op.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]