# chainer.functions.mean_squared_error¶

chainer.functions.mean_squared_error(x0, x1)[source]

Mean squared error function.

The function computes the mean squared error between two variables. The mean is taken over the minibatch. Args `x0` and `x1` must have the same dimensions. Note that the error is not scaled by 1/2.

Parameters
Returns

A variable holding an array representing the mean squared error of two inputs.

Return type

~chainer.Variable

Example

1D array examples:

```>>> x = np.array([1, 2, 3, 4]).astype(np.float32)
>>> y = np.array([0, 0, 0, 0]).astype(np.float32)
>>> F.mean_squared_error(x, y)
variable(7.5)
>>> x = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32)
>>> y = np.array([7, 8, 9, 10, 11, 12]).astype(np.float32)
>>> F.mean_squared_error(x, y)
variable(36.)
```

2D array example:

In this example, there are 4 elements, and thus 4 errors >>> x = np.array([[1, 2], [3, 4]]).astype(np.float32) >>> y = np.array([[8, 8], [8, 8]]).astype(np.float32) >>> F.mean_squared_error(x, y) variable(31.5)

3D array example:

In this example, there are 8 elements, and thus 8 errors >>> x = np.reshape(np.array([1, 2, 3, 4, 5, 6, 7, 8]), (2, 2, 2)) >>> y = np.reshape(np.array([8, 8, 8, 8, 8, 8, 8, 8]), (2, 2, 2)) >>> x = x.astype(np.float32) >>> y = y.astype(np.float32) >>> F.mean_squared_error(x, y) variable(17.5)