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
andx1
must have the same dimensions. Note that the error is not scaled by 1/2.- Parameters
x0 (
Variable
or N-dimensional array) – Input variable.x1 (
Variable
or N-dimensional array) – Input variable.
- 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)