Training now stops when stuck in a minimumexp_detection

1 files changed, 19 insertions, 10 deletions

diff --git a/src/mlp_train.c b/src/mlp_train.c
index 1c1fa28e..a9d548b1 100644
--- a/src/mlp_train.c
+++ b/src/mlp_train.c
@@ -80,7 +80,7 @@ MLPTrain * mlp_init(int *topo, int nbLayers, float *inputs, float *outputs, int
 			std = .001;
 		std = 1/sqrt(inDim*std);
 		for (k=0;k<topo[1];k++)
-			net->weights[0][k*(topo[0]+1)+j+1] = randn(4*std);
+			net->weights[0][k*(topo[0]+1)+j+1] = randn(std);
 	}
 	net->in_rate[0] = 1;
 	for (j=0;j<topo[1];j++)
@@ -223,7 +223,7 @@ float mlp_train_backprop(MLPTrain *net, float *inputs, float *outputs, int nbSam
 {
 	int i, j;
 	int e;
-	float last_rms = 1e10;
+	float best_rms = 1e10;
 	int inDim, outDim, hiddenDim;
 	int *topo;
 	double *W0, *W1, *best_W0, *best_W1;
@@ -241,6 +241,8 @@ float mlp_train_backprop(MLPTrain *net, float *inputs, float *outputs, int nbSam
 	pthread_t thread[NB_THREADS];
 	int samplePerPart = nbSamples/NB_THREADS;
 	int count_worse=0;
+	int count_retries=0;
+
 	topo = net->topo;
 	inDim = net->topo[0];
 	hiddenDim = net->topo[1];
@@ -313,10 +315,10 @@ float mlp_train_backprop(MLPTrain *net, float *inputs, float *outputs, int nbSam
 		float mean_rate = 0, min_rate = 1e10;
 		rms = (rms/(outDim*nbSamples));
 		error_rate = (error_rate/(outDim*nbSamples));
-		fprintf (stderr, "%f (%f %f) ", error_rate, rms, last_rms);
-		if (rms < last_rms)
+		fprintf (stderr, "%f (%f %f) ", error_rate, rms, best_rms);
+		if (rms < best_rms)
 		{
-			last_rms = rms;
+			best_rms = rms;
 			for (i=0;i<W0_size;i++)
 			{
 				best_W0[i] = W0[i];
@@ -328,10 +330,12 @@ float mlp_train_backprop(MLPTrain *net, float *inputs, float *outputs, int nbSam
 				best_W1_rate[i] = W1_rate[i];
 			}
 			count_worse=0;
-		} else if (rms > last_rms) {
+			count_retries=0;
+		} else {
 			count_worse++;
-			if (count_worse>20)
+			if (count_worse>30)
 			{
+			    count_retries++;
 				count_worse=0;
 				for (i=0;i<W0_size;i++)
 				{
@@ -344,13 +348,15 @@ float mlp_train_backprop(MLPTrain *net, float *inputs, float *outputs, int nbSam
 				for (i=0;i<W1_size;i++)
 				{
 					W1[i] = best_W1[i];
-					best_W1_rate[i] *= .7;
+					best_W1_rate[i] *= .8;
 					if (best_W1_rate[i]<1e-15) best_W1_rate[i]=1e-15;
 					W1_rate[i] = best_W1_rate[i];
 					W1_grad[i] = 0;
 				}
 			}
 		}
+		if (count_retries>10)
+		    break;
 		for (i=0;i<W0_size;i++)
 		{
 			if (W0_oldgrad[i]*W0_grad[i] > 0)
@@ -386,7 +392,10 @@ float mlp_train_backprop(MLPTrain *net, float *inputs, float *outputs, int nbSam
 			W1[i] += W1_grad[i]*W1_rate[i];
 		}
 		mean_rate /= (topo[0]+1)*topo[1] + (topo[1]+1)*topo[2];
-		fprintf (stderr, "%g (min %g) %d\n", mean_rate, min_rate, e);
+		fprintf (stderr, "%g %d", mean_rate, e);
+		if (count_retries)
+		    fprintf(stderr, " %d", count_retries);
+		fprintf(stderr, "\n");
 		if (stopped)
 			break;
 	}
@@ -403,7 +412,7 @@ float mlp_train_backprop(MLPTrain *net, float *inputs, float *outputs, int nbSam
 	free(W1_grad);
 	free(W0_rate);
 	free(W1_rate);
-	return last_rms;
+	return best_rms;
 }
 
 int main(int argc, char **argv)