Merge pull request tensorflow#13056 from maspwr/mnist-doc-1

Update mnist beginners softmax variables

tensorflow/docs_src/get_started/mnist/beginners.md

@@ -180,11 +180,11 @@ You can think of it as converting tallies
 of evidence into probabilities of our input being in each class.
 It's defined as:
 
-$$\text{softmax}(x) = \text{normalize}(\exp(x))$$
+$$\text{softmax}(evidence) = \text{normalize}(\exp(evidence))$$
 
 If you expand that equation out, you get:
 
-$$\text{softmax}(x)_i = \frac{\exp(x_i)}{\sum_j \exp(x_j)}$$
+$$\text{softmax}(evidence)_i = \frac{\exp(evidence_i)}{\sum_j \exp(evidence_j)}$$
 
 But it's often more helpful to think of softmax the first way: exponentiating
 its inputs and then normalizing them.  The exponentiation means that one more