# tf.keras.metrics.MeanSquaredError

Computes the mean squared error between `y_true` and `y_pred`.

``````tf.keras.metrics.MeanSquaredError(
name='mean_squared_error', dtype=None
)
``````

#### Usage:

````m = tf.keras.metrics.MeanSquaredError() `
`_ = m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]]) `
`m.result().numpy() `
`0.25 `
```
````m.reset_states() `
`_ = m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]], `
`                   sample_weight=[1, 0]) `
`m.result().numpy() `
`0.5 `
```

Usage with tf.keras API:

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

#### 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`: 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 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)).

Update op.