Small inconsistency between comment and code in mnist_1.0_softmax.py lines 65,66 about tensorflow-mnist-tutorial HOT 1 CLOSED

indeed, this scaling factor is not important, and mainly used to display the training and test loss on the same scale.

reduce_mean computes an average. The population is the number of images in the batch * the number of elements in the tensor, that's 100 * 10 = 1000. So reduce_mean will divide by 1000. I multiply by 1000 to counter that effect and I get the sum of the components * 100, i.e. the loss for 100 images.
At test time, the population for the average is 10000*10 so that is what reduce_mean will divide by. I multiply by 10, to get the average loss per image and then 100 (total: 1000) to get the loss for 100 images.
Since both training and test loss are for 100 images, putting them on the same graph makes sense.

from tensorflow-mnist-tutorial.