Update to opus-HEAD-66611f1.

src/mlp_train.c

 /* Copyright (c) 2008-2011 Octasic Inc.
    Written by Jean-Marc Valin */
 /*
    Redistribution and use in source and binary forms, with or without
    modification, are permitted provided that the following conditions
    are met:
 
    - Redistributions of source code must retain the above copyright
    notice, this list of conditions and the following disclaimer.
 
@@ skipping 21 matching lines @@
 #include <string.h>
 #include <semaphore.h>
 #include <pthread.h>
 #include <time.h>
 #include <signal.h>
 
 int stopped = 0;
 
 void handler(int sig)
 {
-        stopped = 1;
-        signal(sig, handler);
+    stopped = 1;
+    signal(sig, handler);
 }
 
 MLPTrain * mlp_init(int *topo, int nbLayers, float *inputs, float *outputs, int nbSamples)
 {
-        int i, j, k;
-        MLPTrain *net;
-        int inDim, outDim;
-        net = malloc(sizeof(*net));
-        net->topo = malloc(nbLayers*sizeof(net->topo[0]));
-        for (i=0;i<nbLayers;i++)
-                net->topo[i] = topo[i];
-        inDim = topo[0];
-        outDim = topo[nbLayers-1];
-        net->in_rate = malloc((inDim+1)*sizeof(net->in_rate[0]));
-        net->weights = malloc((nbLayers-1)*sizeof(net->weights));
-        net->best_weights = malloc((nbLayers-1)*sizeof(net->weights));
-        for (i=0;i<nbLayers-1;i++)
-        {
-                net->weights[i] = malloc((topo[i]+1)*topo[i+1]*sizeof(net->weights[0][0]));
-                net->best_weights[i] = malloc((topo[i]+1)*topo[i+1]*sizeof(net->weights[0][0]));
-        }
-        double inMean[inDim];
-        for (j=0;j<inDim;j++)
-        {
-                double std=0;
-                inMean[j] = 0;
-                for (i=0;i<nbSamples;i++)
-                {
-                        inMean[j] += inputs[i*inDim+j];
-                        std += inputs[i*inDim+j]*inputs[i*inDim+j];
-                }
-                inMean[j] /= nbSamples;
-                std /= nbSamples;
-                net->in_rate[1+j] = .5/(.0001+std);
-                std = std-inMean[j]*inMean[j];
-                if (std<.001)
-                        std = .001;
-                std = 1/sqrt(inDim*std);
-                for (k=0;k<topo[1];k++)
-                        net->weights[0][k*(topo[0]+1)+j+1] = randn(std);
-        }
-        net->in_rate[0] = 1;
-        for (j=0;j<topo[1];j++)
-        {
-                double sum = 0;
-                for (k=0;k<inDim;k++)
-                        sum += inMean[k]*net->weights[0][j*(topo[0]+1)+k+1];
-                net->weights[0][j*(topo[0]+1)] = -sum;
-        }
-        for (j=0;j<outDim;j++)
-        {
-                double mean = 0;
-                double std;
-                for (i=0;i<nbSamples;i++)
-                        mean += outputs[i*outDim+j];
-                mean /= nbSamples;
-                std = 1/sqrt(topo[nbLayers-2]);
-                net->weights[nbLayers-2][j*(topo[nbLayers-2]+1)] = mean;
-                for (k=0;k<topo[nbLayers-2];k++)
-                        net->weights[nbLayers-2][j*(topo[nbLayers-2]+1)+k+1] = randn(std);
-        }
-        return net;
+    int i, j, k;
+    MLPTrain *net;
+    int inDim, outDim;
+    net = malloc(sizeof(*net));
+    net->topo = malloc(nbLayers*sizeof(net->topo[0]));
+    for (i=0;i<nbLayers;i++)
+        net->topo[i] = topo[i];
+    inDim = topo[0];
+    outDim = topo[nbLayers-1];
+    net->in_rate = malloc((inDim+1)*sizeof(net->in_rate[0]));
+    net->weights = malloc((nbLayers-1)*sizeof(net->weights));
+    net->best_weights = malloc((nbLayers-1)*sizeof(net->weights));
+    for (i=0;i<nbLayers-1;i++)
+    {
+        net->weights[i] = malloc((topo[i]+1)*topo[i+1]*sizeof(net->weights[0][0]));
+        net->best_weights[i] = malloc((topo[i]+1)*topo[i+1]*sizeof(net->weights[0][0]));
+    }
+    double inMean[inDim];
+    for (j=0;j<inDim;j++)
+    {
+        double std=0;
+        inMean[j] = 0;
+        for (i=0;i<nbSamples;i++)
+        {
+            inMean[j] += inputs[i*inDim+j];
+            std += inputs[i*inDim+j]*inputs[i*inDim+j];
+        }
+        inMean[j] /= nbSamples;
+        std /= nbSamples;
+        net->in_rate[1+j] = .5/(.0001+std);
+        std = std-inMean[j]*inMean[j];
+        if (std<.001)
+            std = .001;
+        std = 1/sqrt(inDim*std);
+        for (k=0;k<topo[1];k++)
+            net->weights[0][k*(topo[0]+1)+j+1] = randn(std);
+    }
+    net->in_rate[0] = 1;
+    for (j=0;j<topo[1];j++)
+    {
+        double sum = 0;
+        for (k=0;k<inDim;k++)
+            sum += inMean[k]*net->weights[0][j*(topo[0]+1)+k+1];
+        net->weights[0][j*(topo[0]+1)] = -sum;
+    }
+    for (j=0;j<outDim;j++)
+    {
+        double mean = 0;
+        double std;
+        for (i=0;i<nbSamples;i++)
+            mean += outputs[i*outDim+j];
+        mean /= nbSamples;
+        std = 1/sqrt(topo[nbLayers-2]);
+        net->weights[nbLayers-2][j*(topo[nbLayers-2]+1)] = mean;
+        for (k=0;k<topo[nbLayers-2];k++)
+            net->weights[nbLayers-2][j*(topo[nbLayers-2]+1)+k+1] = randn(std);
+    }
+    return net;
 }
 
 #define MAX_NEURONS 100
 #define MAX_OUT 10
 
 double compute_gradient(MLPTrain *net, float *inputs, float *outputs, int nbSamples, double *W0_grad, double *W1_grad, double *error_rate)
 {
-        int i,j;
-        int s;
-        int inDim, outDim, hiddenDim;
-        int *topo;
-        double *W0, *W1;
-        double rms=0;
-        int W0_size, W1_size;
-        double hidden[MAX_NEURONS];
-        double netOut[MAX_NEURONS];
-        double error[MAX_NEURONS];
+    int i,j;
+    int s;
+    int inDim, outDim, hiddenDim;
+    int *topo;
+    double *W0, *W1;
+    double rms=0;
+    int W0_size, W1_size;
+    double hidden[MAX_NEURONS];
+    double netOut[MAX_NEURONS];
+    double error[MAX_NEURONS];
 
-        for (i=0;i<outDim;i++)
-           error_rate[i] = 0;
-        topo = net->topo;
-        inDim = net->topo[0];
-        hiddenDim = net->topo[1];
-        outDim = net->topo[2];
-        W0_size = (topo[0]+1)*topo[1];
-        W1_size = (topo[1]+1)*topo[2];
-        W0 = net->weights[0];
-        W1 = net->weights[1];
-        memset(W0_grad, 0, W0_size*sizeof(double));
-        memset(W1_grad, 0, W1_size*sizeof(double));
-        for (i=0;i<outDim;i++)
-                netOut[i] = outputs[i];
-        for (s=0;s<nbSamples;s++)
-        {
-                float *in, *out;
-                in = inputs+s*inDim;
-                out = outputs + s*outDim;
-                for (i=0;i<hiddenDim;i++)
-                {
-                        double sum = W0[i*(inDim+1)];
-                        for (j=0;j<inDim;j++)
-                                sum += W0[i*(inDim+1)+j+1]*in[j];
-                        hidden[i] = tansig_approx(sum);
-                }
-                for (i=0;i<outDim;i++)
-                {
-                        double sum = W1[i*(hiddenDim+1)];
-                        for (j=0;j<hiddenDim;j++)
-                                sum += W1[i*(hiddenDim+1)+j+1]*hidden[j];
-                        netOut[i] = tansig_approx(sum);
-                        error[i] = out[i] - netOut[i];
-                        rms += error[i]*error[i];
-                        error_rate[i] += fabs(error[i])>1;
-                        /*error[i] = error[i]/(1+fabs(error[i]));*/
-                }
-                /* Back-propagate error */
-                for (i=0;i<outDim;i++)
-                {
-                        float grad = 1-netOut[i]*netOut[i];
-                        W1_grad[i*(hiddenDim+1)] += error[i]*grad;
-                        for (j=0;j<hiddenDim;j++)
-                                W1_grad[i*(hiddenDim+1)+j+1] += grad*error[i]*hidden[j];
-                }
-                for (i=0;i<hiddenDim;i++)
-                {
-                        double grad;
-                        grad = 0;
-                        for (j=0;j<outDim;j++)
-                                grad += error[j]*W1[j*(hiddenDim+1)+i+1];
-                        grad *= 1-hidden[i]*hidden[i];
-                        W0_grad[i*(inDim+1)] += grad;
-                        for (j=0;j<inDim;j++)
-                                W0_grad[i*(inDim+1)+j+1] += grad*in[j];
-                }
-        }
-        return rms;
+    topo = net->topo;
+    inDim = net->topo[0];
+    hiddenDim = net->topo[1];
+    outDim = net->topo[2];
+    W0_size = (topo[0]+1)*topo[1];
+    W1_size = (topo[1]+1)*topo[2];
+    W0 = net->weights[0];
+    W1 = net->weights[1];
+    memset(W0_grad, 0, W0_size*sizeof(double));
+    memset(W1_grad, 0, W1_size*sizeof(double));
+    for (i=0;i<outDim;i++)
+        netOut[i] = outputs[i];
+    for (i=0;i<outDim;i++)
+        error_rate[i] = 0;
+    for (s=0;s<nbSamples;s++)
+    {
+        float *in, *out;
+        in = inputs+s*inDim;
+        out = outputs + s*outDim;
+        for (i=0;i<hiddenDim;i++)
+        {
+            double sum = W0[i*(inDim+1)];
+            for (j=0;j<inDim;j++)
+                sum += W0[i*(inDim+1)+j+1]*in[j];
+            hidden[i] = tansig_approx(sum);
+        }
+        for (i=0;i<outDim;i++)
+        {
+            double sum = W1[i*(hiddenDim+1)];
+            for (j=0;j<hiddenDim;j++)
+                sum += W1[i*(hiddenDim+1)+j+1]*hidden[j];
+            netOut[i] = tansig_approx(sum);
+            error[i] = out[i] - netOut[i];
+            rms += error[i]*error[i];
+            error_rate[i] += fabs(error[i])>1;
+            /*error[i] = error[i]/(1+fabs(error[i]));*/
+        }
+        /* Back-propagate error */
+        for (i=0;i<outDim;i++)
+        {
+            float grad = 1-netOut[i]*netOut[i];
+            W1_grad[i*(hiddenDim+1)] += error[i]*grad;
+            for (j=0;j<hiddenDim;j++)
+                W1_grad[i*(hiddenDim+1)+j+1] += grad*error[i]*hidden[j];
+        }
+        for (i=0;i<hiddenDim;i++)
+        {
+            double grad;
+            grad = 0;
+            for (j=0;j<outDim;j++)
+                grad += error[j]*W1[j*(hiddenDim+1)+i+1];
+            grad *= 1-hidden[i]*hidden[i];
+            W0_grad[i*(inDim+1)] += grad;
+            for (j=0;j<inDim;j++)
+                W0_grad[i*(inDim+1)+j+1] += grad*in[j];
+        }
+    }
+    return rms;
 }
 
 #define NB_THREADS 8
 
 sem_t sem_begin[NB_THREADS];
 sem_t sem_end[NB_THREADS];
 
 struct GradientArg {
-        int id;
-        int done;
-        MLPTrain *net;
-        float *inputs;
-        float *outputs;
-        int nbSamples;
-        double *W0_grad;
-        double *W1_grad;
-        double rms;
-        double error_rate[MAX_OUT];
+    int id;
+    int done;
+    MLPTrain *net;
+    float *inputs;
+    float *outputs;
+    int nbSamples;
+    double *W0_grad;
+    double *W1_grad;
+    double rms;
+    double error_rate[MAX_OUT];
 };
 
 void *gradient_thread_process(void *_arg)
 {
-        int W0_size, W1_size;
-        struct GradientArg *arg = _arg;
-        int *topo = arg->net->topo;
-        W0_size = (topo[0]+1)*topo[1];
-        W1_size = (topo[1]+1)*topo[2];
-        double W0_grad[W0_size];
-        double W1_grad[W1_size];
-        arg->W0_grad = W0_grad;
-        arg->W1_grad = W1_grad;
-        while (1)
-        {
-                sem_wait(&sem_begin[arg->id]);
-                if (arg->done)
-                        break;
-                arg->rms = compute_gradient(arg->net, arg->inputs, arg->outputs, arg->nbSamples, arg->W0_grad, arg->W1_grad, arg->error_rate);
-                sem_post(&sem_end[arg->id]);
-        }
-        fprintf(stderr, "done\n");
-        return NULL;
+    int W0_size, W1_size;
+    struct GradientArg *arg = _arg;
+    int *topo = arg->net->topo;
+    W0_size = (topo[0]+1)*topo[1];
+    W1_size = (topo[1]+1)*topo[2];
+    double W0_grad[W0_size];
+    double W1_grad[W1_size];
+    arg->W0_grad = W0_grad;
+    arg->W1_grad = W1_grad;
+    while (1)
+    {
+        sem_wait(&sem_begin[arg->id]);
+        if (arg->done)
+            break;
+        arg->rms = compute_gradient(arg->net, arg->inputs, arg->outputs, arg->nbSamples, arg->W0_grad, arg->W1_grad, arg->error_rate);
+        sem_post(&sem_end[arg->id]);
+    }
+    fprintf(stderr, "done\n");
+    return NULL;
 }
 
 float mlp_train_backprop(MLPTrain *net, float *inputs, float *outputs, int nbSamples, int nbEpoch, float rate)
 {
-        int i, j;
-        int e;
-        float best_rms = 1e10;
-        int inDim, outDim, hiddenDim;
-        int *topo;
-        double *W0, *W1, *best_W0, *best_W1;
-        double *W0_old, *W1_old;
-        double *W0_old2, *W1_old2;
-        double *W0_grad, *W1_grad;
-        double *W0_oldgrad, *W1_oldgrad;
-        double *W0_rate, *W1_rate;
-        double *best_W0_rate, *best_W1_rate;
-        int W0_size, W1_size;
-        topo = net->topo;
-        W0_size = (topo[0]+1)*topo[1];
-        W1_size = (topo[1]+1)*topo[2];
-        struct GradientArg args[NB_THREADS];
-        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];
-        outDim = net->topo[2];
-        W0 = net->weights[0];
-        W1 = net->weights[1];
-        best_W0 = net->best_weights[0];
-        best_W1 = net->best_weights[1];
-        W0_old = malloc(W0_size*sizeof(double));
-        W1_old = malloc(W1_size*sizeof(double));
-        W0_old2 = malloc(W0_size*sizeof(double));
-        W1_old2 = malloc(W1_size*sizeof(double));
-        W0_grad = malloc(W0_size*sizeof(double));
-        W1_grad = malloc(W1_size*sizeof(double));
-        W0_oldgrad = malloc(W0_size*sizeof(double));
-        W1_oldgrad = malloc(W1_size*sizeof(double));
-        W0_rate = malloc(W0_size*sizeof(double));
-        W1_rate = malloc(W1_size*sizeof(double));
-        best_W0_rate = malloc(W0_size*sizeof(double));
-        best_W1_rate = malloc(W1_size*sizeof(double));
-        memcpy(W0_old, W0, W0_size*sizeof(double));
-        memcpy(W0_old2, W0, W0_size*sizeof(double));
-        memset(W0_grad, 0, W0_size*sizeof(double));
-        memset(W0_oldgrad, 0, W0_size*sizeof(double));
-        memcpy(W1_old, W1, W1_size*sizeof(double));
-        memcpy(W1_old2, W1, W1_size*sizeof(double));
-        memset(W1_grad, 0, W1_size*sizeof(double));
-        memset(W1_oldgrad, 0, W1_size*sizeof(double));
-        
-        rate /= nbSamples;
-        for (i=0;i<hiddenDim;i++)
-                for (j=0;j<inDim+1;j++)
-                        W0_rate[i*(inDim+1)+j] = rate*net->in_rate[j];
-        for (i=0;i<W1_size;i++)
-                W1_rate[i] = rate;
-        
-        for (i=0;i<NB_THREADS;i++)
-        {
-                args[i].net = net;
-                args[i].inputs = inputs+i*samplePerPart*inDim;
-                args[i].outputs = outputs+i*samplePerPart*outDim;
-                args[i].nbSamples = samplePerPart;
-                args[i].id = i;
-                args[i].done = 0;
-                sem_init(&sem_begin[i], 0, 0);
-                sem_init(&sem_end[i], 0, 0);
-                pthread_create(&thread[i], NULL, gradient_thread_process, &args[i]);
-        }
-        for (e=0;e<nbEpoch;e++)
-        {
-                double rms=0;
-                double error_rate[2] = {0,0};
-                for (i=0;i<NB_THREADS;i++)
-                {
-                        sem_post(&sem_begin[i]);
-                }
-                memset(W0_grad, 0, W0_size*sizeof(double));
-                memset(W1_grad, 0, W1_size*sizeof(double));
-                for (i=0;i<NB_THREADS;i++)
-                {
-                        sem_wait(&sem_end[i]);
-                        rms += args[i].rms;
-                        error_rate[0] += args[i].error_rate[0];
+    int i, j;
+    int e;
+    float best_rms = 1e10;
+    int inDim, outDim, hiddenDim;
+    int *topo;
+    double *W0, *W1, *best_W0, *best_W1;
+    double *W0_old, *W1_old;
+    double *W0_old2, *W1_old2;
+    double *W0_grad, *W1_grad;
+    double *W0_oldgrad, *W1_oldgrad;
+    double *W0_rate, *W1_rate;
+    double *best_W0_rate, *best_W1_rate;
+    int W0_size, W1_size;
+    topo = net->topo;
+    W0_size = (topo[0]+1)*topo[1];
+    W1_size = (topo[1]+1)*topo[2];
+    struct GradientArg args[NB_THREADS];
+    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];
+    outDim = net->topo[2];
+    W0 = net->weights[0];
+    W1 = net->weights[1];
+    best_W0 = net->best_weights[0];
+    best_W1 = net->best_weights[1];
+    W0_old = malloc(W0_size*sizeof(double));
+    W1_old = malloc(W1_size*sizeof(double));
+    W0_old2 = malloc(W0_size*sizeof(double));
+    W1_old2 = malloc(W1_size*sizeof(double));
+    W0_grad = malloc(W0_size*sizeof(double));
+    W1_grad = malloc(W1_size*sizeof(double));
+    W0_oldgrad = malloc(W0_size*sizeof(double));
+    W1_oldgrad = malloc(W1_size*sizeof(double));
+    W0_rate = malloc(W0_size*sizeof(double));
+    W1_rate = malloc(W1_size*sizeof(double));
+    best_W0_rate = malloc(W0_size*sizeof(double));
+    best_W1_rate = malloc(W1_size*sizeof(double));
+    memcpy(W0_old, W0, W0_size*sizeof(double));
+    memcpy(W0_old2, W0, W0_size*sizeof(double));
+    memset(W0_grad, 0, W0_size*sizeof(double));
+    memset(W0_oldgrad, 0, W0_size*sizeof(double));
+    memcpy(W1_old, W1, W1_size*sizeof(double));
+    memcpy(W1_old2, W1, W1_size*sizeof(double));
+    memset(W1_grad, 0, W1_size*sizeof(double));
+    memset(W1_oldgrad, 0, W1_size*sizeof(double));
+
+    rate /= nbSamples;
+    for (i=0;i<hiddenDim;i++)
+        for (j=0;j<inDim+1;j++)
+            W0_rate[i*(inDim+1)+j] = rate*net->in_rate[j];
+    for (i=0;i<W1_size;i++)
+        W1_rate[i] = rate;
+
+    for (i=0;i<NB_THREADS;i++)
+    {
+        args[i].net = net;
+        args[i].inputs = inputs+i*samplePerPart*inDim;
+        args[i].outputs = outputs+i*samplePerPart*outDim;
+        args[i].nbSamples = samplePerPart;
+        args[i].id = i;
+        args[i].done = 0;
+        sem_init(&sem_begin[i], 0, 0);
+        sem_init(&sem_end[i], 0, 0);
+        pthread_create(&thread[i], NULL, gradient_thread_process, &args[i]);
+    }
+    for (e=0;e<nbEpoch;e++)
+    {
+        double rms=0;
+        double error_rate[2] = {0,0};
+        for (i=0;i<NB_THREADS;i++)
+        {
+            sem_post(&sem_begin[i]);
+        }
+        memset(W0_grad, 0, W0_size*sizeof(double));
+        memset(W1_grad, 0, W1_size*sizeof(double));
+        for (i=0;i<NB_THREADS;i++)
+        {
+            sem_wait(&sem_end[i]);
+            rms += args[i].rms;
+            error_rate[0] += args[i].error_rate[0];
             error_rate[1] += args[i].error_rate[1];
-                        for (j=0;j<W0_size;j++)
-                                W0_grad[j] += args[i].W0_grad[j];
-                        for (j=0;j<W1_size;j++)
-                                W1_grad[j] += args[i].W1_grad[j];
-                }
-
-                float mean_rate = 0, min_rate = 1e10;
-                rms = (rms/(outDim*nbSamples));
-                error_rate[0] = (error_rate[0]/(nbSamples));
+            for (j=0;j<W0_size;j++)
+                W0_grad[j] += args[i].W0_grad[j];
+            for (j=0;j<W1_size;j++)
+                W1_grad[j] += args[i].W1_grad[j];
+        }
+
+        float mean_rate = 0, min_rate = 1e10;
+        rms = (rms/(outDim*nbSamples));
+        error_rate[0] = (error_rate[0]/(nbSamples));
         error_rate[1] = (error_rate[1]/(nbSamples));
-                fprintf (stderr, "%f %f (%f %f) ", error_rate[0], error_rate[1], rms, best_rms);
-                if (rms < best_rms)
-                {
-                        best_rms = rms;
-                        for (i=0;i<W0_size;i++)
-                        {
-                                best_W0[i] = W0[i];
-                                best_W0_rate[i] = W0_rate[i];
-                        }
-                        for (i=0;i<W1_size;i++)
-                        {
-                                best_W1[i] = W1[i];
-                                best_W1_rate[i] = W1_rate[i];
-                        }
-                        count_worse=0;
-                        count_retries=0;
-                } else {
-                        count_worse++;
-                        if (count_worse>30)
-                        {
-                            count_retries++;
-                                count_worse=0;
-                                for (i=0;i<W0_size;i++)
-                                {
-                                        W0[i] = best_W0[i];
-                                        best_W0_rate[i] *= .7;
-                                        if (best_W0_rate[i]<1e-15) best_W0_rate[i]=1e-15;
-                                        W0_rate[i] = best_W0_rate[i];
-                                        W0_grad[i] = 0;
-                                }
-                                for (i=0;i<W1_size;i++)
-                                {
-                                        W1[i] = best_W1[i];
-                                        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)
-                                W0_rate[i] *= 1.01;
-                        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] > .01)
-                                W0_rate[i] = .01;*/
-                        W0_oldgrad[i] = W0_grad[i];
-                        W0_old2[i] = W0_old[i];
-                        W0_old[i] = W0[i];
-                        W0[i] += W0_grad[i]*W0_rate[i];
-                }
-                for (i=0;i<W1_size;i++)
-                {
-                        if (W1_oldgrad[i]*W1_grad[i] > 0)
-                                W1_rate[i] *= 1.01;
-                        else if (W1_oldgrad[i]*W1_grad[i] < 0)
-                                W1_rate[i] *= .9;
-                        mean_rate += W1_rate[i];
-                        if (W1_rate[i] < min_rate)
-                                min_rate = W1_rate[i];
-                        if (W1_rate[i] < 1e-15)
-                                W1_rate[i] = 1e-15;
-                        W1_oldgrad[i] = W1_grad[i];
-                        W1_old2[i] = W1_old[i];
-                        W1_old[i] = W1[i];
-                        W1[i] += W1_grad[i]*W1_rate[i];
-                }
-                mean_rate /= (topo[0]+1)*topo[1] + (topo[1]+1)*topo[2];
-                fprintf (stderr, "%g %d", mean_rate, e);
-                if (count_retries)
-                    fprintf(stderr, " %d", count_retries);
-                fprintf(stderr, "\n");
-                if (stopped)
-                        break;
-        }
-        for (i=0;i<NB_THREADS;i++)
-        {
-                args[i].done = 1;
-                sem_post(&sem_begin[i]);
-                pthread_join(thread[i], NULL);
-                fprintf (stderr, "joined %d\n", i);
-        }
-        free(W0_old);
-        free(W1_old);
-        free(W0_grad);
-        free(W1_grad);
-        free(W0_rate);
-        free(W1_rate);
-        return best_rms;
+        fprintf (stderr, "%f %f (%f %f) ", error_rate[0], error_rate[1], rms, best_rms);
+        if (rms < best_rms)
+        {
+            best_rms = rms;
+            for (i=0;i<W0_size;i++)
+            {
+                best_W0[i] = W0[i];
+                best_W0_rate[i] = W0_rate[i];
+            }
+            for (i=0;i<W1_size;i++)
+            {
+                best_W1[i] = W1[i];
+                best_W1_rate[i] = W1_rate[i];
+            }
+            count_worse=0;
+            count_retries=0;
+        } else {
+            count_worse++;
+            if (count_worse>30)
+            {
+                count_retries++;
+                count_worse=0;
+                for (i=0;i<W0_size;i++)
+                {
+                    W0[i] = best_W0[i];
+                    best_W0_rate[i] *= .7;
+                    if (best_W0_rate[i]<1e-15) best_W0_rate[i]=1e-15;
+                    W0_rate[i] = best_W0_rate[i];
+                    W0_grad[i] = 0;
+                }
+                for (i=0;i<W1_size;i++)
+                {
+                    W1[i] = best_W1[i];
+                    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)
+                W0_rate[i] *= 1.01;
+            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] > .01)
+                W0_rate[i] = .01;*/
+            W0_oldgrad[i] = W0_grad[i];
+            W0_old2[i] = W0_old[i];
+            W0_old[i] = W0[i];
+            W0[i] += W0_grad[i]*W0_rate[i];
+        }
+        for (i=0;i<W1_size;i++)
+        {
+            if (W1_oldgrad[i]*W1_grad[i] > 0)
+                W1_rate[i] *= 1.01;
+            else if (W1_oldgrad[i]*W1_grad[i] < 0)
+                W1_rate[i] *= .9;
+            mean_rate += W1_rate[i];
+            if (W1_rate[i] < min_rate)
+                min_rate = W1_rate[i];
+            if (W1_rate[i] < 1e-15)
+                W1_rate[i] = 1e-15;
+            W1_oldgrad[i] = W1_grad[i];
+            W1_old2[i] = W1_old[i];
+            W1_old[i] = W1[i];
+            W1[i] += W1_grad[i]*W1_rate[i];
+        }
+        mean_rate /= (topo[0]+1)*topo[1] + (topo[1]+1)*topo[2];
+        fprintf (stderr, "%g %d", mean_rate, e);
+        if (count_retries)
+            fprintf(stderr, " %d", count_retries);
+        fprintf(stderr, "\n");
+        if (stopped)
+            break;
+    }
+    for (i=0;i<NB_THREADS;i++)
+    {
+        args[i].done = 1;
+        sem_post(&sem_begin[i]);
+        pthread_join(thread[i], NULL);
+        fprintf (stderr, "joined %d\n", i);
+    }
+    free(W0_old);
+    free(W1_old);
+    free(W0_grad);
+    free(W1_grad);
+    free(W0_rate);
+    free(W1_rate);
+    return best_rms;
 }
 
 int main(int argc, char **argv)
 {
-        int i, j;
-        int nbInputs;
-        int nbOutputs;
-        int nbHidden;
-        int nbSamples;
-        int nbEpoch;
-        int nbRealInputs;
-        unsigned int seed;
-        int ret;
-        float rms;
-        float *inputs;
-        float *outputs;
-        if (argc!=6)
-        {
-                fprintf (stderr, "usage: mlp_train <inputs> <hidden> <outputs> <nb samples> <nb epoch>\n");
-                return 1;
-        }
-        nbInputs = atoi(argv[1]);
-        nbHidden = atoi(argv[2]);
-        nbOutputs = atoi(argv[3]);
-        nbSamples = atoi(argv[4]);
-        nbEpoch = atoi(argv[5]);
-        nbRealInputs = nbInputs;
-        inputs = malloc(nbInputs*nbSamples*sizeof(*inputs));
-        outputs = malloc(nbOutputs*nbSamples*sizeof(*outputs));
-        
-        seed = time(NULL);
+    int i, j;
+    int nbInputs;
+    int nbOutputs;
+    int nbHidden;
+    int nbSamples;
+    int nbEpoch;
+    int nbRealInputs;
+    unsigned int seed;
+    int ret;
+    float rms;
+    float *inputs;
+    float *outputs;
+    if (argc!=6)
+    {
+        fprintf (stderr, "usage: mlp_train <inputs> <hidden> <outputs> <nb samples> <nb epoch>\n");
+        return 1;
+    }
+    nbInputs = atoi(argv[1]);
+    nbHidden = atoi(argv[2]);
+    nbOutputs = atoi(argv[3]);
+    nbSamples = atoi(argv[4]);
+    nbEpoch = atoi(argv[5]);
+    nbRealInputs = nbInputs;
+    inputs = malloc(nbInputs*nbSamples*sizeof(*inputs));
+    outputs = malloc(nbOutputs*nbSamples*sizeof(*outputs));
+
+    seed = time(NULL);
     /*seed = 1361480659;*/
-        fprintf (stderr, "Seed is %u\n", seed);
-        srand(seed);
-        build_tansig_table();
-        signal(SIGTERM, handler);
-        signal(SIGINT, handler);
-        signal(SIGHUP, handler);
-        for (i=0;i<nbSamples;i++)
-        {
-                for (j=0;j<nbRealInputs;j++)
-                        ret = scanf(" %f", &inputs[i*nbInputs+j]);
-                for (j=0;j<nbOutputs;j++)
-                        ret = scanf(" %f", &outputs[i*nbOutputs+j]);
-                if (feof(stdin))
-                {
-                        nbSamples = i;
-                        break;
-                }
-        }
-        int topo[3] = {nbInputs, nbHidden, nbOutputs};
-        MLPTrain *net;
-
-        fprintf (stderr, "Got %d samples\n", nbSamples);
-        net = mlp_init(topo, 3, inputs, outputs, nbSamples);
-        rms = mlp_train_backprop(net, inputs, outputs, nbSamples, nbEpoch, 1);
-        printf ("#include \"mlp.h\"\n\n");
-        printf ("/* RMS error was %f, seed was %u */\n\n", rms, seed);
-        printf ("static const float weights[%d] = {\n", (topo[0]+1)*topo[1] + (topo[1]+1)*topo[2]);
-        printf ("\n/* hidden layer */\n");
-        for (i=0;i<(topo[0]+1)*topo[1];i++)
-        {
-                printf ("%gf, ", net->weights[0][i]);
-                if (i%5==4)
-                        printf("\n");
-        }
-        printf ("\n/* output layer */\n");
-        for (i=0;i<(topo[1]+1)*topo[2];i++)
-        {
-                printf ("%g, ", net->weights[1][i]);
-                if (i%5==4)
-                        printf("\n");
-        }
-        printf ("};\n\n");
-        printf ("static const int topo[3] = {%d, %d, %d};\n\n", topo[0], topo[1], topo[2]);
-        printf ("const MLP net = {\n");
-        printf ("\t3,\n");
-        printf ("\ttopo,\n");
-        printf ("\tweights\n};\n");
-        return 0;
+    fprintf (stderr, "Seed is %u\n", seed);
+    srand(seed);
+    build_tansig_table();
+    signal(SIGTERM, handler);
+    signal(SIGINT, handler);
+    signal(SIGHUP, handler);
+    for (i=0;i<nbSamples;i++)
+    {
+        for (j=0;j<nbRealInputs;j++)
+            ret = scanf(" %f", &inputs[i*nbInputs+j]);
+        for (j=0;j<nbOutputs;j++)
+            ret = scanf(" %f", &outputs[i*nbOutputs+j]);
+        if (feof(stdin))
+        {
+            nbSamples = i;
+            break;
+        }
+    }
+    int topo[3] = {nbInputs, nbHidden, nbOutputs};
+    MLPTrain *net;
+
+    fprintf (stderr, "Got %d samples\n", nbSamples);
+    net = mlp_init(topo, 3, inputs, outputs, nbSamples);
+    rms = mlp_train_backprop(net, inputs, outputs, nbSamples, nbEpoch, 1);
+    printf ("#include \"mlp.h\"\n\n");
+    printf ("/* RMS error was %f, seed was %u */\n\n", rms, seed);
+    printf ("static const float weights[%d] = {\n", (topo[0]+1)*topo[1] + (topo[1]+1)*topo[2]);
+    printf ("\n/* hidden layer */\n");
+    for (i=0;i<(topo[0]+1)*topo[1];i++)
+    {
+        printf ("%gf, ", net->weights[0][i]);
+        if (i%5==4)
+            printf("\n");
+    }
+    printf ("\n/* output layer */\n");
+    for (i=0;i<(topo[1]+1)*topo[2];i++)
+    {
+        printf ("%g, ", net->weights[1][i]);
+        if (i%5==4)
+            printf("\n");
+    }
+    printf ("};\n\n");
+    printf ("static const int topo[3] = {%d, %d, %d};\n\n", topo[0], topo[1], topo[2]);
+    printf ("const MLP net = {\n");
+    printf ("\t3,\n");
+    printf ("\ttopo,\n");
+    printf ("\tweights\n};\n");
+    return 0;
 }