如何在tensorflow会话中使用mean_squared_error损失

Question

I am new to tensorflow

In a part of a code for a tensorflow session, there is :

 loss = tf.nn.softmax_cross_entropy_with_logits_v2(
                    logits=net, labels=self.out_placeholder, name='cross_entropy')
                self.loss = tf.reduce_mean(loss, name='mean_squared_error')

I want to use mean_squared_error loss function for this purpose. I found this loss function in tensorflow website:

tf.losses.mean_squared_error(
labels,
predictions,
weights=1.0,
scope=None,
loss_collection=tf.GraphKeys.LOSSES,
reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
)

I need this loss function for a regression problem.

I tried:

loss = tf.losses.mean_squared_error(predictions=net, labels=self.out_placeholder)
self.loss = tf.reduce_mean(loss, name='mean_squared_error')

Where net = tf.matmul(input_tensor, weights) + biases

However, I'm not sure if it's the correct way.

Answer 1

First of all keep in mind that cross-entropy is mainly used for classification, while MSE is used for regression.

In your case cross entropy measures the difference between two distributions (the real occurences, called labels - and your predictions)

So while the first loss functions works on the result of the softmax layer (which can be seen as a probability distribution), the second one works directly on the floating point output of your network (which is no probability distribution) - therefore they cannot be simply exchanged.