tf.losses.mean_squared_error

tf源代码中：

def mean_squared_error(

labels, predictions, weights=1.0, scope=None,

    loss_collection=ops.GraphKeys.LOSSES,

    reduction=Reduction.SUM_BY_NONZERO_WEIGHTS):

"""Adds a Sum-of-Squares loss to the training procedure.

`weights` acts as a coefficient for the loss. If a scalar is provided, then

the loss is simply scaled by the given value. If `weights` is a tensor of size

`[batch_size]`, then the total loss for each sample of the batch is rescaled

by the corresponding element in the `weights` vector. If the shape of

`weights` matches the shape of `predictions`, then the loss of each

measurable element of `predictions` is scaled by the corresponding value of

`weights`.

Args:

labels: The ground truth output tensor, same dimensions as 'predictions'.

predictions: The predicted outputs.

weights: Optional `Tensor` whose rank is either 0, or the same rank as

`labels`, and must be broadcastable to `labels` (i.e., all dimensions must

be either `1`, or the same as the corresponding `losses` dimension).

scope: The scope for the operations performed in computing the loss.

loss_collection: collection to which the loss will be added.

reduction: Type of reduction to apply to loss.

Returns:

Weighted loss float `Tensor`. If `reduction` is `NONE`, this has the same

    shape as `labels`; otherwise, it is scalar.

这里的参数weights十分有用，可以对需要求差异的输入部分截取，即只计算部分labels和predictions的MSE，最终除数也是真实的计算元素个数。

import tensorflowas tf

a = tf.constant([[1,2],[3,4]])

b = tf.constant([[2,1],[5,6]])

mask = tf.constant([[1,0],[1,0]])

# mse loss :

recon_loss = tf.losses.mean_squared_error(a, b, mask)

result = tf.Session().run(recon_loss)

print(result)

输出：[（1-2）^2+(3-5)^2]/2 = 2.5