comments

test.c

@@ -126,14 +126,17 @@ static Data build(const char* path, const int nips, const int nops)
 
 int main()
 {
-    // Input and output size is harded coded here,
-    // so make sure the training data sizes match.
+    // Input and output size is harded coded here as machine learning
+    // repositories usually don't include the input and output size in the data itself.
     const int nips = 256;
     const int nops = 10;
     // Hyper Parameters.
     // Learning rate is annealed and thus not constant.
+    // It can be fine tuned along with the number of hidden layers.
+    // Feel free to modify the anneal rate as well.
     const int nhid = 32;
     double rate = 1.0;
+    const double anneal = 0.9;
     // Load the training set.
     const Data data = build("semeion.data", nips, nops);
     // Train, baby, train.
@@ -149,21 +152,22 @@ int main()
             error += xttrain(tinn, in, tg, rate);
         }
         printf("error %.12f :: rate %f\n", error / data.rows, rate);
-        rate *= 0.9;
+        rate *= anneal;
     }
     // This is how you save the neural network to disk.
     xtsave(tinn, "saved.tinn");
     xtfree(tinn);
     // This is how you load the neural network from disk.
     const Tinn loaded = xtload("saved.tinn");
-    // Ideally, you would load a testing set for predictions,
-    // but for the sake of brevity the training set is reused.
+    // Now we do a prediction with the neural network we loaded from disk.
+    // Ideally, we would also load a testing set to make the prediction with,
+    // but for the sake of brevity here we just reuse the training set from earlier.
     const double* const in = data.in[0];
     const double* const tg = data.tg[0];
     const double* const pd = xpredict(loaded, in);
     for(int i = 0; i < data.nops; i++) { printf("%f ", tg[i]); } printf("\n");
     for(int i = 0; i < data.nops; i++) { printf("%f ", pd[i]); } printf("\n");
-    // Cleanup.
+    // All done. Let's clean up.
     xtfree(loaded);
     dfree(data);
     return 0;