# tf.keras.metrics.MeanAbsolutePercentageError

Computes the mean absolute percentage error between `y_true` and `y_pred`.

``````tf.keras.metrics.MeanAbsolutePercentageError(
name='mean_absolute_percentage_error', dtype=None
)
``````

### Used in the notebooks

Used in the guide

For example, if `y_true` is [0., 0., 1., 1.], and `y_pred` is [1., 1., 1., 0.] the mean absolute percentage error is 5e+08.

#### Usage:

````m = tf.keras.metrics.MeanAbsolutePercentageError() `
`_ = m.update_state([0., 0., 1., 1.], [1., 1., 1., 0.]) `
`m.result().numpy() `
`500000000.0 `
```

Usage with tf.keras API:

``````model = tf.keras.Model(inputs, outputs)
model.compile('sgd', metrics=[tf.keras.metrics.MeanAbsolutePercentageError()])
``````

#### Args:

• `fn`: The metric function to wrap, with signature `fn(y_true, y_pred, **kwargs)`.
• `name`: (Optional) string name of the metric instance.
• `dtype`: (Optional) data type of the metric result.
• `**kwargs`: The keyword arguments that are passed on to `fn`.

## Methods

### `reset_states`

View source

``````reset_states()
``````

Resets all of the metric state variables.

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

### `result`

View source

``````result()
``````

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

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

Accumulates metric statistics.

`y_true` and `y_pred` should have the same shape.

#### Args:

• `y_true`: The ground truth values.
• `y_pred`: The predicted values.
• `sample_weight`: Optional weighting of each example. Defaults to 1. Can be a `Tensor` whose rank is either 0, or the same rank as `y_true`, and must be broadcastable to `y_true`.

Update op.