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maxpool fixed, good on mnist

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This commit is contained in:
Joseph Redmon 2014-08-09 08:16:37 -07:00
parent d9f1b0b16e
commit 7add111509
5 changed files with 54 additions and 93 deletions
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18
src/cnn.c
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@ -281,10 +281,10 @@ void test_vince()
void test_nist_single()
{
srand(222222);
network net = parse_network_cfg("cfg/nist.cfg");
network net = parse_network_cfg("cfg/nist_single.cfg");
data train = load_categorical_data_csv("data/mnist/mnist_tiny.csv", 0, 10);
normalize_data_rows(train);
float loss = train_network_sgd(net, train, 5);
float loss = train_network_sgd(net, train, 1);
printf("Loss: %f, LR: %f, Momentum: %f, Decay: %f\n", loss, net.learning_rate, net.momentum, net.decay);
}
@ -296,20 +296,26 @@ void test_nist()
data train = load_categorical_data_csv("data/mnist/mnist_train.csv", 0, 10);
data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
translate_data_rows(train, -144);
scale_data_rows(train, 1./128);
//scale_data_rows(train, 1./128);
translate_data_rows(test, -144);
scale_data_rows(test, 1./128);
//scale_data_rows(test, 1./128);
//randomize_data(train);
int count = 0;
//clock_t start = clock(), end;
int iters = 10000/net.batch;
while(++count <= 100){
while(++count <= 2000){
clock_t start = clock(), end;
float loss = train_network_sgd(net, train, iters);
end = clock();
float test_acc = network_accuracy(net, test);
//float test_acc = 0;
printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
/*printf("%f %f %f %f %f\n", mean_array(get_network_output_layer(net,0), 100),
mean_array(get_network_output_layer(net,1), 100),
mean_array(get_network_output_layer(net,2), 100),
mean_array(get_network_output_layer(net,3), 100),
mean_array(get_network_output_layer(net,4), 100));
*/
//save_network(net, "cfg/nist_basic_trained.cfg");
//printf("%5d Training Loss: %lf, Params: %f %f %f, ",count*1000, loss, lr, momentum, decay);
@ -759,7 +765,7 @@ int main(int argc, char *argv[])
{
//train_full();
//test_distribution();
//feenableexcept(FE_DIVBYZERO | FE_INVALID | FE_OVERFLOW);
feenableexcept(FE_DIVBYZERO | FE_INVALID | FE_OVERFLOW);
//test_blas();
//test_visualize();

4
src/connected_layer.c
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@ -29,9 +29,9 @@ connected_layer *make_connected_layer(int batch, int inputs, int outputs, ACTIVA
layer->weight_momentum = calloc(inputs*outputs, sizeof(float));
layer->weights = calloc(inputs*outputs, sizeof(float));
float scale = 1./inputs;
//scale = .01;
scale = .05;
for(i = 0; i < inputs*outputs; ++i)
layer->weights[i] = scale*(rand_uniform()-.5);
layer->weights[i] = scale*2*(rand_uniform()-.5);
layer->bias_updates = calloc(outputs, sizeof(float));
layer->bias_adapt = calloc(outputs, sizeof(float));

4
src/convolutional_layer.c
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@ -64,8 +64,8 @@ convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, in
layer->bias_updates = calloc(n, sizeof(float));
layer->bias_momentum = calloc(n, sizeof(float));
float scale = 1./(size*size*c);
//scale = .0001;
for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*(rand_uniform()-.5);
scale = .05;
for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*2*(rand_uniform()-.5);
for(i = 0; i < n; ++i){
//layer->biases[i] = rand_normal()*scale + scale;
layer->biases[i] = .5;

116
src/maxpool_layer.c
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@ -27,6 +27,7 @@ maxpool_layer *make_maxpool_layer(int batch, int h, int w, int c, int size, int
layer->c = c;
layer->size = size;
layer->stride = stride;
layer->max_indexes = calloc(((h-1)/stride+1) * ((w-1)/stride+1) * c*batch, sizeof(int));
layer->output = calloc(((h-1)/stride+1) * ((w-1)/stride+1) * c*batch, sizeof(float));
layer->delta = calloc(((h-1)/stride+1) * ((w-1)/stride+1) * c*batch, sizeof(float));
return layer;
@ -41,101 +42,54 @@ void resize_maxpool_layer(maxpool_layer *layer, int h, int w, int c)
layer->delta = realloc(layer->delta, ((h-1)/layer->stride+1) * ((w-1)/layer->stride+1) * c * layer->batch*sizeof(float));
}
float get_max_region(image im, int h, int w, int c, int size)
{
int i,j;
int lower = (-size-1)/2 + 1;
int upper = size/2 + 1;
int lh = (h-lower < 0) ? 0 : h-lower;
int uh = (h+upper > im.h) ? im.h : h+upper;
int lw = (w-lower < 0) ? 0 : w-lower;
int uw = (w+upper > im.w) ? im.w : w+upper;
//printf("%d\n", -3/2);
//printf("%d %d\n", lower, upper);
//printf("%d %d %d %d\n", lh, uh, lw, uw);
float max = -FLT_MAX;
for(i = lh; i < uh; ++i){
for(j = lw; j < uw; ++j){
float val = get_pixel(im, i, j, c);
if (val > max) max = val;
}
}
return max;
}
void forward_maxpool_layer(const maxpool_layer layer, float *in)
void forward_maxpool_layer(const maxpool_layer layer, float *input)
{
int b;
for(b = 0; b < layer.batch; ++b){
image input = float_to_image(layer.h, layer.w, layer.c, in+b*layer.h*layer.w*layer.c);
int h = (layer.h-1)/layer.stride + 1;
int w = (layer.w-1)/layer.stride + 1;
int c = layer.c;
image output = float_to_image(h,w,c,layer.output+b*h*w*c);
int i,j,k;
for(k = 0; k < input.c; ++k){
for(i = 0; i < input.h; i += layer.stride){
for(j = 0; j < input.w; j += layer.stride){
float max = get_max_region(input, i, j, k, layer.size);
set_pixel(output, i/layer.stride, j/layer.stride, k, max);
int i,j,k,l,m;
for(k = 0; k < layer.c; ++k){
for(i = 0; i < layer.h; i += layer.stride){
for(j = 0; j < layer.w; j += layer.stride){
int out_index = j/layer.stride + w*(i/layer.stride + h*(k + c*b));
layer.output[out_index] = -FLT_MAX;
int lower = (-layer.size-1)/2 + 1;
int upper = layer.size/2 + 1;
int lh = (i+lower < 0) ? 0 : i+lower;
int uh = (i+upper > layer.h) ? layer.h : i+upper;
int lw = (j+lower < 0) ? 0 : j+lower;
int uw = (j+upper > layer.w) ? layer.w : j+upper;
for(l = lh; l < uh; ++l){
for(m = lw; m < uw; ++m){
//printf("%d %d\n", l, m);
int index = m + layer.w*(l + layer.h*(k + b*layer.c));
if(input[index] > layer.output[out_index]){
layer.output[out_index] = input[index];
layer.max_indexes[out_index] = index;
}
}
}
}
}
}
}
}
float set_max_region_delta(image im, image delta, int h, int w, int c, int size, float max, float error)
void backward_maxpool_layer(const maxpool_layer layer, float *input, float *delta)
{
int i,j;
int lower = (-size-1)/2 + 1;
int upper = size/2 + 1;
int lh = (h-lower < 0) ? 0 : h-lower;
int uh = (h+upper > im.h) ? im.h : h+upper;
int lw = (w-lower < 0) ? 0 : w-lower;
int uw = (w+upper > im.w) ? im.w : w+upper;
for(i = lh; i < uh; ++i){
for(j = lw; j < uw; ++j){
float val = get_pixel(im, i, j, c);
if (val == max){
add_pixel(delta, i, j, c, error);
}
}
}
return max;
}
void backward_maxpool_layer(const maxpool_layer layer, float *in, float *delta)
{
int b;
for(b = 0; b < layer.batch; ++b){
image input = float_to_image(layer.h, layer.w, layer.c, in+b*layer.h*layer.w*layer.c);
image input_delta = float_to_image(layer.h, layer.w, layer.c, delta+b*layer.h*layer.w*layer.c);
int h = (layer.h-1)/layer.stride + 1;
int w = (layer.w-1)/layer.stride + 1;
int c = layer.c;
image output = float_to_image(h,w,c,layer.output+b*h*w*c);
image output_delta = float_to_image(h,w,c,layer.delta+b*h*w*c);
zero_image(input_delta);
int i,j,k;
for(k = 0; k < input.c; ++k){
for(i = 0; i < input.h; i += layer.stride){
for(j = 0; j < input.w; j += layer.stride){
float max = get_pixel(output, i/layer.stride, j/layer.stride, k);
float error = get_pixel(output_delta, i/layer.stride, j/layer.stride, k);
set_max_region_delta(input, input_delta, i, j, k, layer.size, max, error);
}
}
}
int i;
int h = (layer.h-1)/layer.stride + 1;
int w = (layer.w-1)/layer.stride + 1;
int c = layer.c;
memset(delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
for(i = 0; i < h*w*c*layer.batch; ++i){
int index = layer.max_indexes[i];
delta[index] += layer.delta[i];
}
}

5
src/maxpool_layer.h
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@ -8,6 +8,7 @@ typedef struct {
int h,w,c;
int stride;
int size;
int *max_indexes;
float *delta;
float *output;
} maxpool_layer;
@ -15,8 +16,8 @@ typedef struct {
image get_maxpool_image(maxpool_layer layer);
maxpool_layer *make_maxpool_layer(int batch, int h, int w, int c, int size, int stride);
void resize_maxpool_layer(maxpool_layer *layer, int h, int w, int c);
void forward_maxpool_layer(const maxpool_layer layer, float *in);
void backward_maxpool_layer(const maxpool_layer layer, float *in, float *delta);
void forward_maxpool_layer(const maxpool_layer layer, float *input);
void backward_maxpool_layer(const maxpool_layer layer, float *input, float *delta);
#endif