About to do something stupid...

Makefile

@@ -1,6 +1,6 @@
 CC=gcc
 COMMON=-Wall `pkg-config --cflags opencv`
-CFLAGS= $(COMMON) -O3 -ffast-math -flto
+CFLAGS= $(COMMON) -Ofast -ffast-math -flto
 UNAME = $(shell uname)
 ifeq ($(UNAME), Darwin)
 COMMON += -isystem /usr/local/Cellar/opencv/2.4.6.1/include/opencv -isystem /usr/local/Cellar/opencv/2.4.6.1/include
@@ -10,12 +10,13 @@ endif
 #CFLAGS= $(COMMON) -O0 -g 
 LDFLAGS=`pkg-config --libs opencv` -lm
 VPATH=./src/
+EXEC=cnn
 
-OBJ=network.o image.o tests.o convolutional_layer.o connected_layer.o maxpool_layer.o activations.o list.o option_list.o parser.o utils.o data.o matrix.o softmax_layer.o mini_blas.o
+OBJ=network.o image.o tests.o connected_layer.o maxpool_layer.o activations.o list.o option_list.o parser.o utils.o data.o matrix.o softmax_layer.o mini_blas.o convolutional_layer.o
 
-all: cnn
+all: $(EXEC)
 
-cnn: $(OBJ)
+$(EXEC): $(OBJ)
 	$(CC) $(CFLAGS) $(LDFLAGS) $^ -o $@
 
 %.o: %.c 
@@ -24,5 +25,5 @@ cnn: $(OBJ)
 .PHONY: clean
 
 clean:
-	rm -rf $(OBJ) cnn
+	rm -rf $(OBJ) $(EXEC)
 

nist_basic.cfg

@@ -1,7 +1,11 @@
-[conn]
-input=784
-output = 100
-activation=ramp
+[conv]
+width=28
+height=28
+channels=1
+filters=20
+size=5
+stride=1
+activation=linear
 
 [conn]
 output = 10

src/convolutional_layer.c

@@ -1,17 +1,13 @@
 #include "convolutional_layer.h"
 #include "utils.h"
+#include "mini_blas.h"
 #include <stdio.h>
 
 image get_convolutional_image(convolutional_layer layer)
 {
     int h,w,c;
-    if(layer.edge){
-        h = (layer.h-1)/layer.stride + 1;
-        w = (layer.w-1)/layer.stride + 1;
-    }else{
-        h = (layer.h - layer.size)/layer.stride+1;
-        w = (layer.h - layer.size)/layer.stride+1;
-    }
+    h = layer.out_h;
+    w = layer.out_w;
     c = layer.n;
     return double_to_image(h,w,c,layer.output);
 }
@@ -19,13 +15,8 @@ image get_convolutional_image(convolutional_layer layer)
 image get_convolutional_delta(convolutional_layer layer)
 {
     int h,w,c;
-    if(layer.edge){
-        h = (layer.h-1)/layer.stride + 1;
-        w = (layer.w-1)/layer.stride + 1;
-    }else{
-        h = (layer.h - layer.size)/layer.stride+1;
-        w = (layer.h - layer.size)/layer.stride+1;
-    }
+    h = layer.out_h;
+    w = layer.out_w;
     c = layer.n;
     return double_to_image(h,w,c,layer.delta);
 }
@@ -34,73 +25,113 @@ convolutional_layer *make_convolutional_layer(int h, int w, int c, int n, int si
 {
     int i;
     int out_h,out_w;
+    size = 2*(size/2)+1; //HA! And you thought you'd use an even sized filter...
     convolutional_layer *layer = calloc(1, sizeof(convolutional_layer));
     layer->h = h;
     layer->w = w;
     layer->c = c;
     layer->n = n;
-    layer->edge = 0;
     layer->stride = stride;
-    layer->kernels = calloc(n, sizeof(image));
-    layer->kernel_updates = calloc(n, sizeof(image));
-    layer->kernel_momentum = calloc(n, sizeof(image));
+    layer->size = size;
+
+    layer->filters = calloc(c*n*size*size, sizeof(double));
+    layer->filter_updates = calloc(c*n*size*size, sizeof(double));
+    layer->filter_momentum = calloc(c*n*size*size, sizeof(double));
+
     layer->biases = calloc(n, sizeof(double));
     layer->bias_updates = calloc(n, sizeof(double));
     layer->bias_momentum = calloc(n, sizeof(double));
     double scale = 2./(size*size);
+    for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = rand_normal()*scale;
     for(i = 0; i < n; ++i){
         //layer->biases[i] = rand_normal()*scale + scale;
         layer->biases[i] = 0;
-        layer->kernels[i] = make_random_kernel(size, c, scale);
-        layer->kernel_updates[i] = make_random_kernel(size, c, 0);
-        layer->kernel_momentum[i] = make_random_kernel(size, c, 0);
     }
-    layer->size = 2*(size/2)+1;
-    if(layer->edge){
-        out_h = (layer->h-1)/layer->stride + 1;
-        out_w = (layer->w-1)/layer->stride + 1;
-    }else{
-        out_h = (layer->h - layer->size)/layer->stride+1;
-        out_w = (layer->h - layer->size)/layer->stride+1;
-    }
-    fprintf(stderr, "Convolutional Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);
+    out_h = (h-size)/stride + 1;
+    out_w = (w-size)/stride + 1;
+
+    layer->col_image = calloc(out_h*out_w*size*size*c, sizeof(double));
     layer->output = calloc(out_h * out_w * n, sizeof(double));
     layer->delta  = calloc(out_h * out_w * n, sizeof(double));
-    layer->upsampled = make_image(h,w,n);
     layer->activation = activation;
+    layer->out_h = out_h;
+    layer->out_w = out_w;
+
+    fprintf(stderr, "Convolutional Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);
+    srand(0);
 
     return layer;
 }
 
 void forward_convolutional_layer(const convolutional_layer layer, double *in)
 {
-    image input = double_to_image(layer.h, layer.w, layer.c, in);
-    image output = get_convolutional_image(layer);
+    int m = layer.n;
+    int k = layer.size*layer.size*layer.c;
+    int n = ((layer.h-layer.size)/layer.stride + 1)*
+            ((layer.w-layer.size)/layer.stride + 1);
+
+    memset(layer.output, 0, m*n*sizeof(double));
+
+    double *a = layer.filters;
+    double *b = layer.col_image;
+    double *c = layer.output;
+
+    im2col_cpu(in,  layer.c,  layer.h,  layer.w,  layer.size,  layer.stride, b);
+    gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
+
+}
+
+void gradient_delta_convolutional_layer(convolutional_layer layer)
+{
+    int i;
+    for(i = 0; i < layer.out_h*layer.out_w*layer.n; ++i){
+        layer.delta[i] *= gradient(layer.output[i], layer.activation);
+    }
+}
+
+void learn_bias_convolutional_layer(convolutional_layer layer)
+{
     int i,j;
+    int size = layer.out_h*layer.out_w;
     for(i = 0; i < layer.n; ++i){
-        convolve(input, layer.kernels[i], layer.stride, i, output, layer.edge);
-    }
-    for(i = 0; i < output.c; ++i){
-        for(j = 0; j < output.h*output.w; ++j){
-            int index = i*output.h*output.w + j;
-            output.data[index] += layer.biases[i];
-            output.data[index] = activate(output.data[index], layer.activation);
+        double sum = 0;
+        for(j = 0; j < size; ++j){
+            sum += layer.delta[j+i*size];
         }
+        layer.bias_updates[i] += sum/size;
     }
 }
 
-void backward_convolutional_layer(convolutional_layer layer, double *input, double *delta)
+void learn_convolutional_layer(convolutional_layer layer)
 {
-    int i;
+    gradient_delta_convolutional_layer(layer);
+    learn_bias_convolutional_layer(layer);
+    int m = layer.n;
+    int n = layer.size*layer.size*layer.c;
+    int k = ((layer.h-layer.size)/layer.stride + 1)*
+            ((layer.w-layer.size)/layer.stride + 1);
 
-    image in_delta = double_to_image(layer.h, layer.w, layer.c, delta);
-    image out_delta = get_convolutional_delta(layer);
-    zero_image(in_delta);
+    double *a = layer.delta;
+    double *b = layer.col_image;
+    double *c = layer.filter_updates;
+
+    gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
+}
 
+void update_convolutional_layer(convolutional_layer layer, double step, double momentum, double decay)
+{
+    int i;
+    int size = layer.size*layer.size*layer.c*layer.n;
     for(i = 0; i < layer.n; ++i){
-        back_convolve(in_delta, layer.kernels[i], layer.stride, i, out_delta, layer.edge);
+        layer.biases[i] += step*layer.bias_updates[i];
+        layer.bias_updates[i] *= momentum;
+    }
+    for(i = 0; i < size; ++i){
+        layer.filters[i] += step*(layer.filter_updates[i] - decay*layer.filters[i]);
+        layer.filter_updates[i] *= momentum;
     }
 }
+/*
 
 void backward_convolutional_layer2(convolutional_layer layer, double *input, double *delta)
 {
@@ -124,15 +155,6 @@ void backward_convolutional_layer2(convolutional_layer layer, double *input, dou
     }
 }
 
-void gradient_delta_convolutional_layer(convolutional_layer layer)
-{
-    int i;
-    image out_delta = get_convolutional_delta(layer);
-    image out_image = get_convolutional_image(layer);
-    for(i = 0; i < out_image.h*out_image.w*out_image.c; ++i){
-        out_delta.data[i] *= gradient(out_image.data[i], layer.activation);
-    }
-}
 
 void learn_convolutional_layer(convolutional_layer layer, double *input)
 {
@@ -163,8 +185,37 @@ void update_convolutional_layer(convolutional_layer layer, double step, double m
         zero_image(layer.kernel_updates[i]);
     }
 }
+*/
 
-void visualize_convolutional_filters(convolutional_layer layer, char *window)
+void test_convolutional_layer()
+{
+    convolutional_layer l = *make_convolutional_layer(4,4,1,1,3,1,LINEAR);
+    double input[] =    {1,2,3,4,
+                        5,6,7,8,
+                        9,10,11,12,
+                        13,14,15,16};
+    double filter[] =   {.5, 0, .3,
+                        0  , 1,  0,
+                        .2 , 0,  1};
+    double delta[] =    {1, 2,
+                        3,  4};
+    l.filters = filter;
+    forward_convolutional_layer(l, input);
+    l.delta = delta;
+    learn_convolutional_layer(l);
+    image filter_updates = double_to_image(3,3,1,l.filter_updates);
+    print_image(filter_updates);
+}
+
+image get_convolutional_filter(convolutional_layer layer, int i)
+{
+    int h = layer.size;
+    int w = layer.size;
+    int c = layer.c;
+    return double_to_image(h,w,c,layer.filters+i*h*w*c);
+}
+
+void visualize_convolutional_layer(convolutional_layer layer, char *window)
 {
     int color = 1;
     int border = 1;
@@ -172,7 +223,7 @@ void visualize_convolutional_filters(convolutional_layer layer, char *window)
     int size = layer.size;
     h = size;
     w = (size + border) * layer.n - border;
-    c = layer.kernels[0].c;
+    c = layer.c;
     if(c != 3 || !color){
         h = (h+border)*c - border;
         c = 1;
@@ -182,11 +233,13 @@ void visualize_convolutional_filters(convolutional_layer layer, char *window)
     int i,j;
     for(i = 0; i < layer.n; ++i){
         int w_offset = i*(size+border);
-        image k = layer.kernels[i];
+        image k = get_convolutional_filter(layer, i);
+        //printf("%f ** ", layer.biases[i]);
+        //print_image(k);
         image copy = copy_image(k);
         normalize_image(copy);
         for(j = 0; j < k.c; ++j){
-            set_pixel(copy,0,0,j,layer.biases[i]);
+            //set_pixel(copy,0,0,j,layer.biases[i]);
         }
         if(c == 3 && color){
             embed_image(copy, filters, 0, w_offset);
@@ -211,15 +264,3 @@ void visualize_convolutional_filters(convolutional_layer layer, char *window)
     free_image(filters);
 }
 
-void visualize_convolutional_layer(convolutional_layer layer)
-{
-    int i;
-    char buff[256];
-    for(i = 0; i < layer.n; ++i){
-        image k = layer.kernels[i];
-        sprintf(buff, "Kernel %d", i);
-        if(k.c <= 3) show_image(k, buff);
-        else show_image_layers(k, buff);
-    }
-}
-

src/convolutional_layer.h

@@ -6,36 +6,40 @@
 
 typedef struct {
     int h,w,c;
+    int out_h, out_w, out_c;
     int n;
     int size;
     int stride;
-    image *kernels;
-    image *kernel_updates;
-    image *kernel_momentum;
+    double *filters;
+    double *filter_updates;
+    double *filter_momentum;
+
     double *biases;
     double *bias_updates;
     double *bias_momentum;
-    image upsampled;
+
+    double *col_image;
     double *delta;
     double *output;
 
     ACTIVATION activation;
-    int edge;
 } convolutional_layer;
 
 convolutional_layer *make_convolutional_layer(int h, int w, int c, int n, int size, int stride, ACTIVATION activation);
 void forward_convolutional_layer(const convolutional_layer layer, double *in);
-void backward_convolutional_layer(convolutional_layer layer, double *input, double *delta);
-void learn_convolutional_layer(convolutional_layer layer, double *input);
-
+void learn_convolutional_layer(convolutional_layer layer);
 void update_convolutional_layer(convolutional_layer layer, double step, double momentum, double decay);
+void visualize_convolutional_layer(convolutional_layer layer, char *window);
+
+//void backward_convolutional_layer(convolutional_layer layer, double *input, double *delta);
 
-void backpropagate_convolutional_layer_convolve(image input, convolutional_layer layer);
-void visualize_convolutional_filters(convolutional_layer layer, char *window);
-void visualize_convolutional_layer(convolutional_layer layer);
+//void backpropagate_convolutional_layer_convolve(image input, convolutional_layer layer);
+//void visualize_convolutional_filters(convolutional_layer layer, char *window);
+//void visualize_convolutional_layer(convolutional_layer layer);
 
 image get_convolutional_image(convolutional_layer layer);
 image get_convolutional_delta(convolutional_layer layer);
+image get_convolutional_filter(convolutional_layer layer, int i);
 
 #endif
 

src/mini_blas.c

@@ -1,16 +1,44 @@
 
+#include <stdlib.h>
+#include <math.h>
+
+void pm(int M, int N, double *A)
+{
+    int i,j;
+    for(i =0 ; i < M; ++i){
+        for(j = 0; j < N; ++j){
+            printf("%10.6f, ", A[i*N+j]);
+        }
+        printf("\n");
+    }
+    printf("\n");
+}
+
 void gemm(int TA, int TB, int M, int N, int K, double ALPHA, 
                     double *A, int lda, 
                     double *B, int ldb,
                     double BETA,
                     double *C, int ldc)
 {
-    // Assume TA = TB = 0, beta = 1 LULZ
+    // Assume TA = 0, beta = 1 LULZ
     int i,j,k;
-    for(i = 0; i < M; ++i){
-        for(k = 0; k < K; ++k){
+    if(TB && !TA){
+        for(i = 0; i < M; ++i){
             for(j = 0; j < N; ++j){
-                C[i*ldc+j] += ALPHA*A[i*lda+k]*B[k*ldb+j];
+                register double sum = 0;
+                for(k = 0; k < K; ++k){
+                    sum += ALPHA*A[i*lda+k]*B[k+j*ldb];
+                }
+                C[i*ldc+j] += sum;
+            }
+        }
+    }else{
+        for(i = 0; i < M; ++i){
+            for(k = 0; k < K; ++k){
+                register double A_PART = ALPHA*A[i*lda+k];
+                for(j = 0; j < N; ++j){
+                    C[i*ldc+j] += A_PART*B[k*ldb+j];
+                }
             }
         }
     }
@@ -59,7 +87,7 @@ void im2col(double *image, int h, int w, int c, int size, int stride, double *ma
 void im2col_cpu(double* data_im, const int channels,
         const int height, const int width, const int ksize, const int stride,
         double* data_col) 
- {
+{
     int c,h,w;
     int height_col = (height - ksize) / stride + 1;
     int width_col = (width - ksize) / stride + 1;

src/mini_blas.h

@@ -1,3 +1,4 @@
+void pm(int M, int N, double *A);
 void gemm(int TA, int TB, int M, int N, int K, double ALPHA, 
                     double *A, int lda, 
                     double *B, int ldb,

src/network.c

@@ -6,6 +6,7 @@
 
 #include "connected_layer.h"
 #include "convolutional_layer.h"
+//#include "old_conv.h"
 #include "maxpool_layer.h"
 #include "softmax_layer.h"
 
@@ -113,14 +114,17 @@ double *get_network_delta(network net)
     return get_network_delta_layer(net, net.n-1);
 }
 
-void calculate_error_network(network net, double *truth)
+double calculate_error_network(network net, double *truth)
 {
+    double sum = 0;
     double *delta = get_network_delta(net);
     double *out = get_network_output(net);
     int i, k = get_network_output_size(net);
     for(i = 0; i < k; ++i){
         delta[i] = truth[i] - out[i];
+        sum += delta[i]*delta[i];
     }
+    return sum;
 }
 
 int get_predicted_class_network(network net)
@@ -130,9 +134,9 @@ int get_predicted_class_network(network net)
     return max_index(out, k);
 }
 
-void backward_network(network net, double *input, double *truth)
+double backward_network(network net, double *input, double *truth)
 {
-    calculate_error_network(net, truth);
+    double error = calculate_error_network(net, truth);
     int i;
     double *prev_input;
     double *prev_delta;
@@ -146,8 +150,9 @@ void backward_network(network net, double *input, double *truth)
         }
         if(net.types[i] == CONVOLUTIONAL){
             convolutional_layer layer = *(convolutional_layer *)net.layers[i];
-            learn_convolutional_layer(layer, prev_input);
-            if(i != 0) backward_convolutional_layer(layer, prev_input, prev_delta);
+            learn_convolutional_layer(layer);
+            //learn_convolutional_layer(layer);
+            //if(i != 0) backward_convolutional_layer(layer, prev_input, prev_delta);
         }
         else if(net.types[i] == MAXPOOL){
             maxpool_layer layer = *(maxpool_layer *)net.layers[i];
@@ -163,29 +168,31 @@ void backward_network(network net, double *input, double *truth)
             if(i != 0) backward_connected_layer(layer, prev_input, prev_delta);
         }
     }
+    return error;
 }
 
-int train_network_datum(network net, double *x, double *y, double step, double momentum, double decay)
+double train_network_datum(network net, double *x, double *y, double step, double momentum, double decay)
 {
         forward_network(net, x);
         int class = get_predicted_class_network(net);
-        backward_network(net, x, y);
+        double error = backward_network(net, x, y);
         update_network(net, step, momentum, decay);
-        return (y[class]?1:0);
+        //return (y[class]?1:0);
+        return error;
 }
 
 double train_network_sgd(network net, data d, int n, double step, double momentum,double decay)
 {
     int i;
-    int correct = 0;
+    double error = 0;
     for(i = 0; i < n; ++i){
         int index = rand()%d.X.rows;
-        correct += train_network_datum(net, d.X.vals[index], d.y.vals[index], step, momentum, decay);
+        error += train_network_datum(net, d.X.vals[index], d.y.vals[index], step, momentum, decay);
         //if((i+1)%10 == 0){
         //    printf("%d: %f\n", (i+1), (double)correct/(i+1));
         //}
     }
-    return (double)correct/n;
+    return error/n;
 }
 double train_network_batch(network net, data d, int n, double step, double momentum,double decay)
 {
@@ -282,7 +289,7 @@ void visualize_network(network net)
         sprintf(buff, "Layer %d", i);
         if(net.types[i] == CONVOLUTIONAL){
             convolutional_layer layer = *(convolutional_layer *)net.layers[i];
-            visualize_convolutional_filters(layer, buff);
+            visualize_convolutional_layer(layer, buff);
         }
     } 
 }

src/network.h

@@ -22,7 +22,7 @@ typedef struct {
 
 network make_network(int n);
 void forward_network(network net, double *input);
-void backward_network(network net, double *input, double *truth);
+double backward_network(network net, double *input, double *truth);
 void update_network(network net, double step, double momentum, double decay);
 double train_network_sgd(network net, data d, int n, double step, double momentum,double decay);
 double train_network_batch(network net, data d, int n, double step, double momentum,double decay);

src/tests.c

@@ -1,4 +1,5 @@
 #include "connected_layer.h"
+//#include "old_conv.h"
 #include "convolutional_layer.h"
 #include "maxpool_layer.h"
 #include "network.h"
@@ -35,7 +36,7 @@ void test_convolve_matrix()
     printf("dog channels %d\n", dog.c);
     
     int size = 11;
-    int stride = 1;
+    int stride = 4;
     int n = 40;
     double *filters = make_random_image(size, size, dog.c*n).data;
 
@@ -64,29 +65,6 @@ void test_color()
     show_image_layers(dog, "Test Color");
 }
 
-void test_convolutional_layer()
-{
-    srand(0);
-    image dog = load_image("dog.jpg");
-    int i;
-    int n = 3;
-    int stride = 1;
-    int size = 3;
-    convolutional_layer layer = *make_convolutional_layer(dog.h, dog.w, dog.c, n, size, stride, RELU);
-    char buff[256];
-    for(i = 0; i < n; ++i) {
-        sprintf(buff, "Kernel %d", i);
-        show_image(layer.kernels[i], buff);
-    }
-    forward_convolutional_layer(layer, dog.data);
-    
-    image output = get_convolutional_image(layer);
-    maxpool_layer mlayer = *make_maxpool_layer(output.h, output.w, output.c, 2);
-    forward_maxpool_layer(mlayer, layer.output);
-
-    show_image_layers(get_maxpool_image(mlayer), "Test Maxpool Layer");
-}
-
 void verify_convolutional_layer()
 {
     srand(0);
@@ -117,7 +95,7 @@ void verify_convolutional_layer()
     image out_delta = get_convolutional_delta(layer);
     for(i = 0; i < out.h*out.w*out.c; ++i){
         out_delta.data[i] = 1;
-        backward_convolutional_layer(layer, test.data, in_delta.data);
+        //backward_convolutional_layer(layer, test.data, in_delta.data);
         image partial = copy_image(in_delta);
         jacobian2[i] = partial.data;
         out_delta.data[i] = 0;
@@ -240,16 +218,16 @@ void test_nist()
     double momentum = .9;
     double decay = 0.01;
     clock_t start = clock(), end;
-    while(++count <= 1000){
-        double acc = train_network_sgd(net, train, 6400, lr, momentum, decay);
-        printf("%5d Training Loss: %lf, Params: %f %f %f, ",count*100, 1.-acc, lr, momentum, decay);
+    while(++count <= 100){
+        visualize_network(net);
+        double loss = train_network_sgd(net, train, 10000, lr, momentum, decay);
+        printf("%5d Training Loss: %lf, Params: %f %f %f, ",count*100, loss, lr, momentum, decay);
         end = clock();
         printf("Time: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
         start=end;
-        //visualize_network(net);
-        //cvWaitKey(100);
+        cvWaitKey(100);
         //lr /= 2; 
-        if(count%5 == 0 && 0){
+        if(count%5 == 0){
             double train_acc = network_accuracy(net, train);
             fprintf(stderr, "\nTRAIN: %f\n", train_acc);
             double test_acc = network_accuracy(net, test);
@@ -268,11 +246,9 @@ void test_ensemble()
     data test = load_categorical_data_csv("mnist/mnist_test.csv", 0,10);
     normalize_data_rows(test);
     data train = d;
-    /*
-       data *split = split_data(d, 1, 10);
-       data train = split[0];
-       data test = split[1];
-     */
+    //   data *split = split_data(d, 1, 10);
+    //   data train = split[0];
+    //   data test = split[1];
     matrix prediction = make_matrix(test.y.rows, test.y.cols);
     int n = 30;
     for(i = 0; i < n; ++i){
@@ -298,22 +274,6 @@ void test_ensemble()
     printf("Full Ensemble Accuracy: %lf\n", acc);
 }
 
-void test_kernel_update()
-{
-    srand(0);
-    double delta[] = {.1};
-    double input[] = {.3, .5, .3, .5, .5, .5, .5, .0, .5};
-    double kernel[] = {1,2,3,4,5,6,7,8,9};
-    convolutional_layer layer = *make_convolutional_layer(3, 3, 1, 1, 3, 1, LINEAR);
-    layer.kernels[0].data = kernel;
-    layer.delta = delta;
-    learn_convolutional_layer(layer, input);
-    print_image(layer.kernels[0]);
-    print_image(get_convolutional_delta(layer));
-    print_image(layer.kernel_updates[0]);
-
-}
-
 void test_random_classify()
 {
     network net = parse_network_cfg("connected.cfg");
@@ -380,7 +340,7 @@ double *random_matrix(int rows, int cols)
 
 void test_blas()
 {
-    int m = 6025, n = 20, k = 11*11*3;
+    int m = 1000, n = 1000, k = 1000;
     double *a = random_matrix(m,k);
     double *b = random_matrix(k,n);
     double *c = random_matrix(m,n);
@@ -405,17 +365,16 @@ void test_im2row()
     double *matrix = calloc(msize, sizeof(double));
     int i;
     for(i = 0; i < 1000; ++i){
-    im2col_cpu(test.data,  c,  h,  w,  size,  stride, matrix);
-    image render = double_to_image(mh, mw, mc, matrix);
+        im2col_cpu(test.data,  c,  h,  w,  size,  stride, matrix);
+        image render = double_to_image(mh, mw, mc, matrix);
     }
 }
 
 int main()
 {
     //test_blas();
- //test_convolve_matrix();
-//    test_im2row();
-    //test_kernel_update();
+    //test_convolve_matrix();
+    //    test_im2row();
     //test_split();
     //test_ensemble();
     test_nist();