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rnn stuff

This commit is contained in:
Joseph Redmon 2016-02-05 00:15:12 -08:00
parent c604f2d994
commit c7c1e0e7b7
5 changed files with 166 additions and 126 deletions
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4
Makefile
View File

@ -1,5 +1,5 @@
GPU=1
OPENCV=1
GPU=0
OPENCV=0
DEBUG=0
ARCH= --gpu-architecture=compute_20 --gpu-code=compute_20

21
cfg/rnn.cfg
View File

@ -1,29 +1,32 @@
[net]
subdivisions=1
inputs=256
batch = 128
batch = 1
momentum=0.9
decay=0.001
max_batches = 50000
time_steps=900
max_batches = 2000
time_steps=1
learning_rate=0.1
policy=steps
steps=1000,1500
scales=.1,.1
[rnn]
batch_normalize=1
output = 256
hidden=512
output = 1024
hidden=1024
activation=leaky
[rnn]
batch_normalize=1
output = 256
hidden=512
output = 1024
hidden=1024
activation=leaky
[rnn]
batch_normalize=1
output = 256
hidden=512
output = 1024
hidden=1024
activation=leaky
[connected]

40
cfg/rnn.train.cfg Normal file
View File

@ -0,0 +1,40 @@
[net]
subdivisions=1
inputs=256
batch = 128
momentum=0.9
decay=0.001
max_batches = 2000
time_steps=576
learning_rate=0.1
policy=steps
steps=1000,1500
scales=.1,.1
[rnn]
batch_normalize=1
output = 1024
hidden=1024
activation=leaky
[rnn]
batch_normalize=1
output = 1024
hidden=1024
activation=leaky
[rnn]
batch_normalize=1
output = 1024
hidden=1024
activation=leaky
[connected]
output=256
activation=leaky
[softmax]
[cost]
type=sse

83
src/rnn.c
View File

@ -12,22 +12,31 @@ typedef struct {
float *y;
} float_pair;
float_pair get_rnn_data(char *text, int len, int batch, int steps)
float_pair get_rnn_data(unsigned char *text, int characters, int len, int batch, int steps)
{
float *x = calloc(batch * steps * 256, sizeof(float));
float *y = calloc(batch * steps * 256, sizeof(float));
float *x = calloc(batch * steps * characters, sizeof(float));
float *y = calloc(batch * steps * characters, sizeof(float));
int i,j;
for(i = 0; i < batch; ++i){
int index = rand() %(len - steps - 1);
/*
int done = 1;
while(!done){
index = rand() %(len - steps - 1);
while(index < len-steps-1 && text[index++] != '\n');
if (index < len-steps-1) done = 1;
}
}
*/
for(j = 0; j < steps; ++j){
x[(j*batch + i)*256 + text[index + j]] = 1;
y[(j*batch + i)*256 + text[index + j + 1]] = 1;
x[(j*batch + i)*characters + text[index + j]] = 1;
y[(j*batch + i)*characters + text[index + j + 1]] = 1;
if(text[index+j] > 255 || text[index+j] <= 0 || text[index+j+1] > 255 || text[index+j+1] <= 0){
text[index+j+2] = 0;
printf("%d %d %d %d %d\n", index, j, len, (int)text[index+j], (int)text[index+j+1]);
printf("%s", text+index);
error("Bad char");
}
}
}
float_pair p;
@ -38,7 +47,7 @@ float_pair get_rnn_data(char *text, int len, int batch, int steps)
void train_char_rnn(char *cfgfile, char *weightfile, char *filename)
{
FILE *fp = fopen(filename, "r");
FILE *fp = fopen(filename, "rb");
//FILE *fp = fopen("data/ab.txt", "r");
//FILE *fp = fopen("data/grrm/asoiaf.txt", "r");
@ -46,7 +55,7 @@ void train_char_rnn(char *cfgfile, char *weightfile, char *filename)
size_t size = ftell(fp);
fseek(fp, 0, SEEK_SET);
char *text = calloc(size, sizeof(char));
unsigned char *text = calloc(size+1, sizeof(char));
fread(text, 1, size, fp);
fclose(fp);
@ -60,6 +69,7 @@ void train_char_rnn(char *cfgfile, char *weightfile, char *filename)
if(weightfile){
load_weights(&net, weightfile);
}
int inputs = get_network_input_size(net);
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int batch = net.batch;
int steps = net.time_steps;
@ -69,7 +79,7 @@ void train_char_rnn(char *cfgfile, char *weightfile, char *filename)
while(get_current_batch(net) < net.max_batches){
i += 1;
time=clock();
float_pair p = get_rnn_data(text, size, batch/steps, steps);
float_pair p = get_rnn_data(text, inputs, size, batch/steps, steps);
float loss = train_network_datum(net, p.x, p.y) / (batch);
free(p.x);
@ -104,12 +114,13 @@ void test_char_rnn(char *cfgfile, char *weightfile, int num, char *seed, float t
if(weightfile){
load_weights(&net, weightfile);
}
int inputs = get_network_input_size(net);
int i, j;
for(i = 0; i < net.n; ++i) net.layers[i].temperature = temp;
char c;
unsigned char c;
int len = strlen(seed);
float *input = calloc(256, sizeof(float));
float *input = calloc(inputs, sizeof(float));
for(i = 0; i < len-1; ++i){
c = seed[i];
input[(int)c] = 1;
@ -125,7 +136,7 @@ void test_char_rnn(char *cfgfile, char *weightfile, int num, char *seed, float t
input[(int)c] = 1;
float *out = network_predict(net, input);
input[(int)c] = 0;
for(j = 0; j < 256; ++j){
for(j = 0; j < inputs; ++j){
sum += out[j];
if(sum > r) break;
}
@ -134,20 +145,8 @@ void test_char_rnn(char *cfgfile, char *weightfile, int num, char *seed, float t
printf("\n");
}
void valid_char_rnn(char *cfgfile, char *weightfile, char *filename)
void valid_char_rnn(char *cfgfile, char *weightfile)
{
FILE *fp = fopen(filename, "r");
//FILE *fp = fopen("data/ab.txt", "r");
//FILE *fp = fopen("data/grrm/asoiaf.txt", "r");
fseek(fp, 0, SEEK_END);
size_t size = ftell(fp);
fseek(fp, 0, SEEK_SET);
char *text = calloc(size, sizeof(char));
fread(text, 1, size, fp);
fclose(fp);
char *base = basecfg(cfgfile);
fprintf(stderr, "%s\n", base);
@ -155,19 +154,25 @@ void valid_char_rnn(char *cfgfile, char *weightfile, char *filename)
if(weightfile){
load_weights(&net, weightfile);
}
int i;
char c;
float *input = calloc(256, sizeof(float));
int inputs = get_network_input_size(net);
int count = 0;
int c;
float *input = calloc(inputs, sizeof(float));
float sum = 0;
for(i = 0; i < size-1; ++i){
c = text[i];
input[(int)c] = 1;
c = getc(stdin);
float log2 = log(2);
while(c != EOF){
int next = getc(stdin);
if(next == EOF) break;
++count;
input[c] = 1;
float *out = network_predict(net, input);
input[(int)c] = 0;
sum += log(out[(int)text[i+1]]);
input[c] = 0;
sum += log(out[next])/log2;
c = next;
}
printf("Log Probability: %f\n", sum);
printf("Perplexity: %f\n", pow(2, -sum/count));
}
@ -179,13 +184,13 @@ void run_char_rnn(int argc, char **argv)
}
char *filename = find_char_arg(argc, argv, "-file", "data/shakespeare.txt");
char *seed = find_char_arg(argc, argv, "-seed", "\n");
int len = find_int_arg(argc, argv, "-len", 100);
float temp = find_float_arg(argc, argv, "-temp", 1);
int len = find_int_arg(argc, argv, "-len", 1000);
float temp = find_float_arg(argc, argv, "-temp", .7);
int rseed = find_int_arg(argc, argv, "-srand", time(0));
char *cfg = argv[3];
char *weights = (argc > 4) ? argv[4] : 0;
if(0==strcmp(argv[2], "train")) train_char_rnn(cfg, weights, filename);
else if(0==strcmp(argv[2], "valid")) valid_char_rnn(cfg, weights, filename);
else if(0==strcmp(argv[2], "valid")) valid_char_rnn(cfg, weights);
else if(0==strcmp(argv[2], "test")) test_char_rnn(cfg, weights, len, seed, temp, rseed);
}

144
src/rnn_layer.c
View File

@ -10,6 +10,19 @@
#include <stdlib.h>
#include <string.h>
void increment_layer(layer *l, int steps)
{
int num = l->outputs*l->batch*steps;
l->output += num;
l->delta += num;
l->x += num;
l->x_norm += num;
l->output_gpu += num;
l->delta_gpu += num;
l->x_gpu += num;
l->x_norm_gpu += num;
}
layer make_rnn_layer(int batch, int inputs, int hidden, int outputs, int steps, ACTIVATION activation, int batch_normalize, int log)
{
@ -22,7 +35,7 @@ layer make_rnn_layer(int batch, int inputs, int hidden, int outputs, int steps,
l.hidden = hidden;
l.inputs = inputs;
l.state = calloc(batch*hidden, sizeof(float));
l.state = calloc(batch*hidden*(steps+1), sizeof(float));
l.input_layer = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
@ -43,11 +56,11 @@ layer make_rnn_layer(int batch, int inputs, int hidden, int outputs, int steps,
l.output = l.output_layer->output;
l.delta = l.output_layer->delta;
#ifdef GPU
l.state_gpu = cuda_make_array(l.state, batch*hidden);
#ifdef GPU
l.state_gpu = cuda_make_array(l.state, batch*hidden*(steps+1));
l.output_gpu = l.output_layer->output_gpu;
l.delta_gpu = l.output_layer->delta_gpu;
#endif
#endif
return l;
}
@ -80,16 +93,23 @@ void forward_rnn_layer(layer l, network_state state)
s.input = l.state;
forward_connected_layer(self_layer, s);
copy_cpu(l.hidden * l.batch, input_layer.output, 1, l.state, 1);
float *old_state = l.state;
if(state.train) l.state += l.hidden*l.batch;
if(l.shortcut){
copy_cpu(l.hidden * l.batch, old_state, 1, l.state, 1);
}else{
fill_cpu(l.hidden * l.batch, 0, l.state, 1);
}
axpy_cpu(l.hidden * l.batch, 1, input_layer.output, 1, l.state, 1);
axpy_cpu(l.hidden * l.batch, 1, self_layer.output, 1, l.state, 1);
s.input = l.state;
forward_connected_layer(output_layer, s);
state.input += l.inputs*l.batch;
input_layer.output += l.hidden*l.batch;
self_layer.output += l.hidden*l.batch;
output_layer.output += l.outputs*l.batch;
increment_layer(&input_layer, 1);
increment_layer(&self_layer, 1);
increment_layer(&output_layer, 1);
}
}
@ -101,14 +121,12 @@ void backward_rnn_layer(layer l, network_state state)
layer input_layer = *(l.input_layer);
layer self_layer = *(l.self_layer);
layer output_layer = *(l.output_layer);
input_layer.output += l.hidden*l.batch*(l.steps-1);
input_layer.delta += l.hidden*l.batch*(l.steps-1);
self_layer.output += l.hidden*l.batch*(l.steps-1);
self_layer.delta += l.hidden*l.batch*(l.steps-1);
increment_layer(&input_layer, l.steps-1);
increment_layer(&self_layer, l.steps-1);
increment_layer(&output_layer, l.steps-1);
output_layer.output += l.outputs*l.batch*(l.steps-1);
output_layer.delta += l.outputs*l.batch*(l.steps-1);
l.state += l.hidden*l.batch*l.steps;
for (i = l.steps-1; i >= 0; --i) {
copy_cpu(l.hidden * l.batch, input_layer.output, 1, l.state, 1);
axpy_cpu(l.hidden * l.batch, 1, self_layer.output, 1, l.state, 1);
@ -116,13 +134,16 @@ void backward_rnn_layer(layer l, network_state state)
s.input = l.state;
s.delta = self_layer.delta;
backward_connected_layer(output_layer, s);
if(i > 0){
copy_cpu(l.hidden * l.batch, input_layer.output - l.hidden*l.batch, 1, l.state, 1);
axpy_cpu(l.hidden * l.batch, 1, self_layer.output - l.hidden*l.batch, 1, l.state, 1);
}else{
fill_cpu(l.hidden * l.batch, 0, l.state, 1);
}
l.state -= l.hidden*l.batch;
/*
if(i > 0){
copy_cpu(l.hidden * l.batch, input_layer.output - l.hidden*l.batch, 1, l.state, 1);
axpy_cpu(l.hidden * l.batch, 1, self_layer.output - l.hidden*l.batch, 1, l.state, 1);
}else{
fill_cpu(l.hidden * l.batch, 0, l.state, 1);
}
*/
s.input = l.state;
s.delta = self_layer.delta - l.hidden*l.batch;
@ -130,19 +151,15 @@ void backward_rnn_layer(layer l, network_state state)
backward_connected_layer(self_layer, s);
copy_cpu(l.hidden*l.batch, self_layer.delta, 1, input_layer.delta, 1);
if (i > 0 && l.shortcut) axpy_cpu(l.hidden*l.batch, 1, self_layer.delta, 1, self_layer.delta - l.hidden*l.batch, 1);
s.input = state.input + i*l.inputs*l.batch;
if(state.delta) s.delta = state.delta + i*l.inputs*l.batch;
else s.delta = 0;
backward_connected_layer(input_layer, s);
input_layer.output -= l.hidden*l.batch;
input_layer.delta -= l.hidden*l.batch;
self_layer.output -= l.hidden*l.batch;
self_layer.delta -= l.hidden*l.batch;
output_layer.output -= l.outputs*l.batch;
output_layer.delta -= l.outputs*l.batch;
increment_layer(&input_layer, -1);
increment_layer(&self_layer, -1);
increment_layer(&output_layer, -1);
}
}
@ -190,23 +207,23 @@ void forward_rnn_layer_gpu(layer l, network_state state)
s.input = l.state_gpu;
forward_connected_layer_gpu(self_layer, s);
copy_ongpu(l.hidden * l.batch, input_layer.output_gpu, 1, l.state_gpu, 1);
float *old_state = l.state_gpu;
if(state.train) l.state_gpu += l.hidden*l.batch;
if(l.shortcut){
copy_ongpu(l.hidden * l.batch, old_state, 1, l.state_gpu, 1);
}else{
fill_ongpu(l.hidden * l.batch, 0, l.state_gpu, 1);
}
axpy_ongpu(l.hidden * l.batch, 1, input_layer.output_gpu, 1, l.state_gpu, 1);
axpy_ongpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1);
s.input = l.state_gpu;
forward_connected_layer_gpu(output_layer, s);
state.input += l.inputs*l.batch;
input_layer.output_gpu += l.hidden*l.batch;
input_layer.x_gpu += l.hidden*l.batch;
input_layer.x_norm_gpu += l.hidden*l.batch;
self_layer.output_gpu += l.hidden*l.batch;
self_layer.x_gpu += l.hidden*l.batch;
self_layer.x_norm_gpu += l.hidden*l.batch;
output_layer.output_gpu += l.outputs*l.batch;
output_layer.x_gpu += l.outputs*l.batch;
output_layer.x_norm_gpu += l.outputs*l.batch;
increment_layer(&input_layer, 1);
increment_layer(&self_layer, 1);
increment_layer(&output_layer, 1);
}
}
@ -218,20 +235,10 @@ void backward_rnn_layer_gpu(layer l, network_state state)
layer input_layer = *(l.input_layer);
layer self_layer = *(l.self_layer);
layer output_layer = *(l.output_layer);
input_layer.output_gpu += l.hidden*l.batch*(l.steps-1);
input_layer.delta_gpu += l.hidden*l.batch*(l.steps-1);
input_layer.x_gpu += l.hidden*l.batch*(l.steps-1);
input_layer.x_norm_gpu += l.hidden*l.batch*(l.steps-1);
self_layer.output_gpu += l.hidden*l.batch*(l.steps-1);
self_layer.delta_gpu += l.hidden*l.batch*(l.steps-1);
self_layer.x_gpu += l.hidden*l.batch*(l.steps-1);
self_layer.x_norm_gpu += l.hidden*l.batch*(l.steps-1);
output_layer.output_gpu += l.outputs*l.batch*(l.steps-1);
output_layer.delta_gpu += l.outputs*l.batch*(l.steps-1);
output_layer.x_gpu += l.outputs*l.batch*(l.steps-1);
output_layer.x_norm_gpu += l.outputs*l.batch*(l.steps-1);
increment_layer(&input_layer, l.steps - 1);
increment_layer(&self_layer, l.steps - 1);
increment_layer(&output_layer, l.steps - 1);
l.state_gpu += l.hidden*l.batch*l.steps;
for (i = l.steps-1; i >= 0; --i) {
copy_ongpu(l.hidden * l.batch, input_layer.output_gpu, 1, l.state_gpu, 1);
axpy_ongpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1);
@ -239,13 +246,8 @@ void backward_rnn_layer_gpu(layer l, network_state state)
s.input = l.state_gpu;
s.delta = self_layer.delta_gpu;
backward_connected_layer_gpu(output_layer, s);
if(i > 0){
copy_ongpu(l.hidden * l.batch, input_layer.output_gpu - l.hidden*l.batch, 1, l.state_gpu, 1);
axpy_ongpu(l.hidden * l.batch, 1, self_layer.output_gpu - l.hidden*l.batch, 1, l.state_gpu, 1);
}else{
fill_ongpu(l.hidden * l.batch, 0, l.state_gpu, 1);
}
l.state_gpu -= l.hidden*l.batch;
s.input = l.state_gpu;
s.delta = self_layer.delta_gpu - l.hidden*l.batch;
@ -253,25 +255,15 @@ void backward_rnn_layer_gpu(layer l, network_state state)
backward_connected_layer_gpu(self_layer, s);
copy_ongpu(l.hidden*l.batch, self_layer.delta_gpu, 1, input_layer.delta_gpu, 1);
if (i > 0 && l.shortcut) axpy_ongpu(l.hidden*l.batch, 1, self_layer.delta_gpu, 1, self_layer.delta_gpu - l.hidden*l.batch, 1);
s.input = state.input + i*l.inputs*l.batch;
if(state.delta) s.delta = state.delta + i*l.inputs*l.batch;
else s.delta = 0;
backward_connected_layer_gpu(input_layer, s);
input_layer.output_gpu -= l.hidden*l.batch;
input_layer.delta_gpu -= l.hidden*l.batch;
input_layer.x_gpu -= l.hidden*l.batch;
input_layer.x_norm_gpu -= l.hidden*l.batch;
self_layer.output_gpu -= l.hidden*l.batch;
self_layer.delta_gpu -= l.hidden*l.batch;
self_layer.x_gpu -= l.hidden*l.batch;
self_layer.x_norm_gpu -= l.hidden*l.batch;
output_layer.output_gpu -= l.outputs*l.batch;
output_layer.delta_gpu -= l.outputs*l.batch;
output_layer.x_gpu -= l.outputs*l.batch;
output_layer.x_norm_gpu -= l.outputs*l.batch;
increment_layer(&input_layer, -1);
increment_layer(&self_layer, -1);
increment_layer(&output_layer, -1);
}
}
#endif