Fixes a few training issues

src/mlp_train.c

@@ -149,7 +149,7 @@ double compute_gradient(MLPTrain *net, float *inputs, float *outputs, int nbSamp
 			netOut[i] = tansig_approx(sum);
 			error[i] = out[i] - netOut[i];
 			rms += error[i]*error[i];
-                        *error_rate += fabs(error[i])>.5;
+			*error_rate += fabs(error[i])>1;
 		}
 		/* Back-propagate error */
 		for (i=0;i<outDim;i++)
@@ -301,22 +301,22 @@ float mlp_train_backprop(MLPTrain *net, float *inputs, float *outputs, int nbSam
 		}
 
 		float mean_rate = 0, min_rate = 1e10;
-		rms = sqrt(rms/(outDim*nbSamples));
+		rms = (rms/(outDim*nbSamples));
 		error_rate = (error_rate/(outDim*nbSamples));
 		fprintf (stderr, "%f (%f) ", error_rate, last_rms);
 		for (i=0;i<W0_size;i++)
 		{
-			if (W0_oldgrad[i]*W0_grad[i] >= 0)
+			if (W0_oldgrad[i]*W0_grad[i] > 0)
 				W0_rate[i] *= 1.01;
-			else
+			else if (W0_oldgrad[i]*W0_grad[i] < 0)
 				W0_rate[i] *= .9;
 			mean_rate += W0_rate[i];
 			if (W0_rate[i] < min_rate)
 				min_rate = W0_rate[i];
 			if (W0_rate[i] < 1e-15)
 				W0_rate[i] = 1e-15;
-			if (W0_rate[i] > 1)
-				W0_rate[i] = 1;
+			if (W0_rate[i] > .01)
+				W0_rate[i] = .01;
 			W0_oldgrad[i] = W0_grad[i];
 			W0_old2[i] = W0_old[i];
 			W0_old[i] = W0[i];