From 168af40fe9a3cc81c6ee16b3e81f154780c36bdb Mon Sep 17 00:00:00 2001
From: Scheaven <xuepengqiang>
Date: 星期四, 03 六月 2021 15:03:27 +0800
Subject: [PATCH] up new v4
---
lib/detecter_tools/darknet/rnn.c | 996 +++++++++++++++++++++++++++++-----------------------------
1 files changed, 498 insertions(+), 498 deletions(-)
diff --git a/lib/detecter_tools/darknet/rnn.c b/lib/detecter_tools/darknet/rnn.c
index a9ee97b..53fb8f3 100644
--- a/lib/detecter_tools/darknet/rnn.c
+++ b/lib/detecter_tools/darknet/rnn.c
@@ -1,498 +1,498 @@
-#include "network.h"
-#include "cost_layer.h"
-#include "utils.h"
-#include "blas.h"
-#include "parser.h"
-
-typedef struct {
- float *x;
- float *y;
-} float_pair;
-
-int *read_tokenized_data(char *filename, size_t *read)
-{
- size_t size = 512;
- size_t count = 0;
- FILE *fp = fopen(filename, "r");
- int* d = (int*)xcalloc(size, sizeof(int));
- int n, one;
- one = fscanf(fp, "%d", &n);
- while(one == 1){
- ++count;
- if(count > size){
- size = size*2;
- d = (int*)xrealloc(d, size * sizeof(int));
- }
- d[count-1] = n;
- one = fscanf(fp, "%d", &n);
- }
- fclose(fp);
- d = (int*)xrealloc(d, count * sizeof(int));
- *read = count;
- return d;
-}
-
-char **read_tokens(char *filename, size_t *read)
-{
- size_t size = 512;
- size_t count = 0;
- FILE *fp = fopen(filename, "r");
- char** d = (char**)xcalloc(size, sizeof(char*));
- char *line;
- while((line=fgetl(fp)) != 0){
- ++count;
- if(count > size){
- size = size*2;
- d = (char**)xrealloc(d, size * sizeof(char*));
- }
- d[count-1] = line;
- }
- fclose(fp);
- d = (char**)xrealloc(d, count * sizeof(char*));
- *read = count;
- return d;
-}
-
-float_pair get_rnn_token_data(int *tokens, size_t *offsets, int characters, size_t len, int batch, int steps)
-{
- float* x = (float*)xcalloc(batch * steps * characters, sizeof(float));
- float* y = (float*)xcalloc(batch * steps * characters, sizeof(float));
- int i,j;
- for(i = 0; i < batch; ++i){
- for(j = 0; j < steps; ++j){
- int curr = tokens[(offsets[i])%len];
- int next = tokens[(offsets[i] + 1)%len];
-
- x[(j*batch + i)*characters + curr] = 1;
- y[(j*batch + i)*characters + next] = 1;
-
- offsets[i] = (offsets[i] + 1) % len;
-
- if(curr >= characters || curr < 0 || next >= characters || next < 0){
- error("Bad char");
- }
- }
- }
- float_pair p;
- p.x = x;
- p.y = y;
- return p;
-}
-
-float_pair get_rnn_data(unsigned char *text, size_t *offsets, int characters, size_t len, int batch, int steps)
-{
- float* x = (float*)xcalloc(batch * steps * characters, sizeof(float));
- float* y = (float*)xcalloc(batch * steps * characters, sizeof(float));
- int i,j;
- for(i = 0; i < batch; ++i){
- for(j = 0; j < steps; ++j){
- unsigned char curr = text[(offsets[i])%len];
- unsigned char next = text[(offsets[i] + 1)%len];
-
- x[(j*batch + i)*characters + curr] = 1;
- y[(j*batch + i)*characters + next] = 1;
-
- offsets[i] = (offsets[i] + 1) % len;
-
- if(curr > 255 || curr <= 0 || next > 255 || next <= 0){
- /*text[(index+j+2)%len] = 0;
- printf("%ld %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;
- p.x = x;
- p.y = y;
- return p;
-}
-
-void reset_rnn_state(network net, int b)
-{
- int i;
- for (i = 0; i < net.n; ++i) {
- #ifdef GPU
- layer l = net.layers[i];
- if(l.state_gpu){
- fill_ongpu(l.outputs, 0, l.state_gpu + l.outputs*b, 1);
- }
- #endif
- }
-}
-
-void train_char_rnn(char *cfgfile, char *weightfile, char *filename, int clear, int tokenized)
-{
- srand(time(0));
- unsigned char *text = 0;
- int *tokens = 0;
- size_t size;
- if(tokenized){
- tokens = read_tokenized_data(filename, &size);
- } else {
- FILE *fp = fopen(filename, "rb");
-
- fseek(fp, 0, SEEK_END);
- size = ftell(fp);
- fseek(fp, 0, SEEK_SET);
-
- text = (unsigned char *)xcalloc(size + 1, sizeof(char));
- fread(text, 1, size, fp);
- fclose(fp);
- }
-
- char* backup_directory = "backup/";
- char *base = basecfg(cfgfile);
- fprintf(stderr, "%s\n", base);
- float avg_loss = -1;
- network net = parse_network_cfg(cfgfile);
- 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;
- if (clear) {
- *net.seen = 0;
- *net.cur_iteration = 0;
- }
- int i = (*net.seen)/net.batch;
-
- int streams = batch/steps;
- printf("\n batch = %d, steps = %d, streams = %d, subdivisions = %d, text_size = %ld \n", batch, steps, streams, net.subdivisions, size);
- printf(" global_batch = %d \n", batch*net.subdivisions);
- size_t* offsets = (size_t*)xcalloc(streams, sizeof(size_t));
- int j;
- for(j = 0; j < streams; ++j){
- offsets[j] = rand_size_t()%size;
- //printf(" offset[%d] = %d, ", j, offsets[j]);
- }
- //printf("\n");
-
- clock_t time;
- while(get_current_batch(net) < net.max_batches){
- i += 1;
- time=clock();
- float_pair p;
- if(tokenized){
- p = get_rnn_token_data(tokens, offsets, inputs, size, streams, steps);
- }else{
- p = get_rnn_data(text, offsets, inputs, size, streams, steps);
- }
-
- float loss = train_network_datum(net, p.x, p.y) / (batch);
- free(p.x);
- free(p.y);
- if (avg_loss < 0) avg_loss = loss;
- avg_loss = avg_loss*.9 + loss*.1;
-
- int chars = get_current_batch(net)*batch;
- fprintf(stderr, "%d: %f, %f avg, %f rate, %lf seconds, %f epochs\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), (float) chars/size);
-
- for(j = 0; j < streams; ++j){
- //printf("%d\n", j);
- if(rand()%10 == 0){
- //fprintf(stderr, "Reset\n");
- offsets[j] = rand_size_t()%size;
- reset_rnn_state(net, j);
- }
- }
-
- if(i%1000==0){
- char buff[256];
- sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
- save_weights(net, buff);
- }
- if(i%10==0){
- char buff[256];
- sprintf(buff, "%s/%s.backup", backup_directory, base);
- save_weights(net, buff);
- }
- }
- char buff[256];
- sprintf(buff, "%s/%s_final.weights", backup_directory, base);
- save_weights(net, buff);
-}
-
-void print_symbol(int n, char **tokens){
- if(tokens){
- printf("%s ", tokens[n]);
- } else {
- printf("%c", n);
- }
-}
-
-void test_char_rnn(char *cfgfile, char *weightfile, int num, char *seed, float temp, int rseed, char *token_file)
-{
- char **tokens = 0;
- if(token_file){
- size_t n;
- tokens = read_tokens(token_file, &n);
- }
-
- srand(rseed);
- char *base = basecfg(cfgfile);
- fprintf(stderr, "%s\n", base);
-
- network net = parse_network_cfg_custom(cfgfile, 1, 1); // batch=1, time_steps=1
- 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;
- int c = 0;
- int len = strlen(seed);
- float* input = (float*)xcalloc(inputs, sizeof(float));
-
- /*
- fill_cpu(inputs, 0, input, 1);
- for(i = 0; i < 10; ++i){
- network_predict(net, input);
- }
- fill_cpu(inputs, 0, input, 1);
- */
-
- for(i = 0; i < len-1; ++i){
- c = seed[i];
- input[c] = 1;
- network_predict(net, input);
- input[c] = 0;
- print_symbol(c, tokens);
- }
- if(len) c = seed[len-1];
- print_symbol(c, tokens);
- for(i = 0; i < num; ++i){
- input[c] = 1;
- float *out = network_predict(net, input);
- input[c] = 0;
- for(j = 32; j < 127; ++j){
- //printf("%d %c %f\n",j, j, out[j]);
- }
- for(j = 0; j < inputs; ++j){
- if (out[j] < .0001) out[j] = 0;
- }
- c = sample_array(out, inputs);
- //c = sample_array_custom(out, inputs);
- //c = max_index(out, inputs);
- //c = top_max_index(out, inputs, 2);
- print_symbol(c, tokens);
- }
- printf("\n");
-}
-
-void test_tactic_rnn(char *cfgfile, char *weightfile, int num, float temp, int rseed, char *token_file)
-{
- char **tokens = 0;
- if(token_file){
- size_t n;
- tokens = read_tokens(token_file, &n);
- }
-
- srand(rseed);
- char *base = basecfg(cfgfile);
- fprintf(stderr, "%s\n", base);
-
- network net = parse_network_cfg(cfgfile);
- 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;
- int c = 0;
- float* input = (float*)xcalloc(inputs, sizeof(float));
- float *out = 0;
-
- while((c = getc(stdin)) != EOF){
- input[c] = 1;
- out = network_predict(net, input);
- input[c] = 0;
- }
- for(i = 0; i < num; ++i){
- for(j = 0; j < inputs; ++j){
- if (out[j] < .0001) out[j] = 0;
- }
- int next = sample_array(out, inputs);
- if(c == '.' && next == '\n') break;
- c = next;
- print_symbol(c, tokens);
-
- input[c] = 1;
- out = network_predict(net, input);
- input[c] = 0;
- }
- printf("\n");
-}
-
-void valid_tactic_rnn(char *cfgfile, char *weightfile, char *seed)
-{
- char *base = basecfg(cfgfile);
- fprintf(stderr, "%s\n", base);
-
- network net = parse_network_cfg(cfgfile);
- if(weightfile){
- load_weights(&net, weightfile);
- }
- int inputs = get_network_input_size(net);
-
- int count = 0;
- int words = 1;
- int c;
- int len = strlen(seed);
- float* input = (float*)xcalloc(inputs, sizeof(float));
- int i;
- for(i = 0; i < len; ++i){
- c = seed[i];
- input[(int)c] = 1;
- network_predict(net, input);
- input[(int)c] = 0;
- }
- float sum = 0;
- c = getc(stdin);
- float log2 = log(2);
- int in = 0;
- while(c != EOF){
- int next = getc(stdin);
- if(next == EOF) break;
- if(next < 0 || next >= 255) error("Out of range character");
-
- input[c] = 1;
- float *out = network_predict(net, input);
- input[c] = 0;
-
- if(c == '.' && next == '\n') in = 0;
- if(!in) {
- if(c == '>' && next == '>'){
- in = 1;
- ++words;
- }
- c = next;
- continue;
- }
- ++count;
- sum += log(out[next])/log2;
- c = next;
- printf("%d %d Perplexity: %4.4f Word Perplexity: %4.4f\n", count, words, pow(2, -sum/count), pow(2, -sum/words));
- }
-}
-
-void valid_char_rnn(char *cfgfile, char *weightfile, char *seed)
-{
- char *base = basecfg(cfgfile);
- fprintf(stderr, "%s\n", base);
-
- network net = parse_network_cfg(cfgfile);
- if(weightfile){
- load_weights(&net, weightfile);
- }
- int inputs = get_network_input_size(net);
-
- int count = 0;
- int words = 1;
- int c;
- int len = strlen(seed);
- float* input = (float*)xcalloc(inputs, sizeof(float));
- int i;
- for(i = 0; i < len; ++i){
- c = seed[i];
- input[(int)c] = 1;
- network_predict(net, input);
- input[(int)c] = 0;
- }
- float sum = 0;
- c = getc(stdin);
- float log2 = log(2);
- while(c != EOF){
- int next = getc(stdin);
- if(next == EOF) break;
- if(next < 0 || next >= 255) error("Out of range character");
- ++count;
- if(next == ' ' || next == '\n' || next == '\t') ++words;
- input[c] = 1;
- float *out = network_predict(net, input);
- input[c] = 0;
- sum += log(out[next])/log2;
- c = next;
- printf("%d Perplexity: %4.4f Word Perplexity: %4.4f\n", count, pow(2, -sum/count), pow(2, -sum/words));
- }
-}
-
-void vec_char_rnn(char *cfgfile, char *weightfile, char *seed)
-{
- char *base = basecfg(cfgfile);
- fprintf(stderr, "%s\n", base);
-
- network net = parse_network_cfg(cfgfile);
- if(weightfile){
- load_weights(&net, weightfile);
- }
- int inputs = get_network_input_size(net);
-
- int c;
- int seed_len = strlen(seed);
- float* input = (float*)xcalloc(inputs, sizeof(float));
- int i;
- char *line;
- while((line=fgetl(stdin)) != 0){
- reset_rnn_state(net, 0);
- for(i = 0; i < seed_len; ++i){
- c = seed[i];
- input[(int)c] = 1;
- network_predict(net, input);
- input[(int)c] = 0;
- }
- strip(line);
- int str_len = strlen(line);
- for(i = 0; i < str_len; ++i){
- c = line[i];
- input[(int)c] = 1;
- network_predict(net, input);
- input[(int)c] = 0;
- }
- c = ' ';
- input[(int)c] = 1;
- network_predict(net, input);
- input[(int)c] = 0;
-
- layer l = net.layers[0];
- #ifdef GPU
- cuda_pull_array(l.output_gpu, l.output, l.outputs);
- #endif
- printf("%s", line);
- for(i = 0; i < l.outputs; ++i){
- printf(",%g", l.output[i]);
- }
- printf("\n");
- }
-}
-
-void run_char_rnn(int argc, char **argv)
-{
- if(argc < 4){
- fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
- return;
- }
- char *filename = find_char_arg(argc, argv, "-file", "data/shakespeare.txt");
- char *seed = find_char_arg(argc, argv, "-seed", "\n\n");
- 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));
- int clear = find_arg(argc, argv, "-clear");
- int tokenized = find_arg(argc, argv, "-tokenized");
- char *tokens = find_char_arg(argc, argv, "-tokens", 0);
-
- char *cfg = argv[3];
- char *weights = (argc > 4) ? argv[4] : 0;
- if(0==strcmp(argv[2], "train")) train_char_rnn(cfg, weights, filename, clear, tokenized);
- else if(0==strcmp(argv[2], "valid")) valid_char_rnn(cfg, weights, seed);
- else if(0==strcmp(argv[2], "validtactic")) valid_tactic_rnn(cfg, weights, seed);
- else if(0==strcmp(argv[2], "vec")) vec_char_rnn(cfg, weights, seed);
- else if(0==strcmp(argv[2], "generate")) test_char_rnn(cfg, weights, len, seed, temp, rseed, tokens);
- else if(0==strcmp(argv[2], "generatetactic")) test_tactic_rnn(cfg, weights, len, temp, rseed, tokens);
-}
+#include "network.h"
+#include "cost_layer.h"
+#include "utils.h"
+#include "blas.h"
+#include "parser.h"
+
+typedef struct {
+ float *x;
+ float *y;
+} float_pair;
+
+int *read_tokenized_data(char *filename, size_t *read)
+{
+ size_t size = 512;
+ size_t count = 0;
+ FILE *fp = fopen(filename, "r");
+ int* d = (int*)xcalloc(size, sizeof(int));
+ int n, one;
+ one = fscanf(fp, "%d", &n);
+ while(one == 1){
+ ++count;
+ if(count > size){
+ size = size*2;
+ d = (int*)xrealloc(d, size * sizeof(int));
+ }
+ d[count-1] = n;
+ one = fscanf(fp, "%d", &n);
+ }
+ fclose(fp);
+ d = (int*)xrealloc(d, count * sizeof(int));
+ *read = count;
+ return d;
+}
+
+char **read_tokens(char *filename, size_t *read)
+{
+ size_t size = 512;
+ size_t count = 0;
+ FILE *fp = fopen(filename, "r");
+ char** d = (char**)xcalloc(size, sizeof(char*));
+ char *line;
+ while((line=fgetl(fp)) != 0){
+ ++count;
+ if(count > size){
+ size = size*2;
+ d = (char**)xrealloc(d, size * sizeof(char*));
+ }
+ d[count-1] = line;
+ }
+ fclose(fp);
+ d = (char**)xrealloc(d, count * sizeof(char*));
+ *read = count;
+ return d;
+}
+
+float_pair get_rnn_token_data(int *tokens, size_t *offsets, int characters, size_t len, int batch, int steps)
+{
+ float* x = (float*)xcalloc(batch * steps * characters, sizeof(float));
+ float* y = (float*)xcalloc(batch * steps * characters, sizeof(float));
+ int i,j;
+ for(i = 0; i < batch; ++i){
+ for(j = 0; j < steps; ++j){
+ int curr = tokens[(offsets[i])%len];
+ int next = tokens[(offsets[i] + 1)%len];
+
+ x[(j*batch + i)*characters + curr] = 1;
+ y[(j*batch + i)*characters + next] = 1;
+
+ offsets[i] = (offsets[i] + 1) % len;
+
+ if(curr >= characters || curr < 0 || next >= characters || next < 0){
+ error("Bad char");
+ }
+ }
+ }
+ float_pair p;
+ p.x = x;
+ p.y = y;
+ return p;
+}
+
+float_pair get_rnn_data(unsigned char *text, size_t *offsets, int characters, size_t len, int batch, int steps)
+{
+ float* x = (float*)xcalloc(batch * steps * characters, sizeof(float));
+ float* y = (float*)xcalloc(batch * steps * characters, sizeof(float));
+ int i,j;
+ for(i = 0; i < batch; ++i){
+ for(j = 0; j < steps; ++j){
+ unsigned char curr = text[(offsets[i])%len];
+ unsigned char next = text[(offsets[i] + 1)%len];
+
+ x[(j*batch + i)*characters + curr] = 1;
+ y[(j*batch + i)*characters + next] = 1;
+
+ offsets[i] = (offsets[i] + 1) % len;
+
+ if(curr > 255 || curr <= 0 || next > 255 || next <= 0){
+ /*text[(index+j+2)%len] = 0;
+ printf("%ld %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;
+ p.x = x;
+ p.y = y;
+ return p;
+}
+
+void reset_rnn_state(network net, int b)
+{
+ int i;
+ for (i = 0; i < net.n; ++i) {
+ #ifdef GPU
+ layer l = net.layers[i];
+ if(l.state_gpu){
+ fill_ongpu(l.outputs, 0, l.state_gpu + l.outputs*b, 1);
+ }
+ #endif
+ }
+}
+
+void train_char_rnn(char *cfgfile, char *weightfile, char *filename, int clear, int tokenized)
+{
+ srand(time(0));
+ unsigned char *text = 0;
+ int *tokens = 0;
+ size_t size;
+ if(tokenized){
+ tokens = read_tokenized_data(filename, &size);
+ } else {
+ FILE *fp = fopen(filename, "rb");
+
+ fseek(fp, 0, SEEK_END);
+ size = ftell(fp);
+ fseek(fp, 0, SEEK_SET);
+
+ text = (unsigned char *)xcalloc(size + 1, sizeof(char));
+ fread(text, 1, size, fp);
+ fclose(fp);
+ }
+
+ char* backup_directory = "backup/";
+ char *base = basecfg(cfgfile);
+ fprintf(stderr, "%s\n", base);
+ float avg_loss = -1;
+ network net = parse_network_cfg(cfgfile);
+ 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;
+ if (clear) {
+ *net.seen = 0;
+ *net.cur_iteration = 0;
+ }
+ int i = (*net.seen)/net.batch;
+
+ int streams = batch/steps;
+ printf("\n batch = %d, steps = %d, streams = %d, subdivisions = %d, text_size = %ld \n", batch, steps, streams, net.subdivisions, size);
+ printf(" global_batch = %d \n", batch*net.subdivisions);
+ size_t* offsets = (size_t*)xcalloc(streams, sizeof(size_t));
+ int j;
+ for(j = 0; j < streams; ++j){
+ offsets[j] = rand_size_t()%size;
+ //printf(" offset[%d] = %d, ", j, offsets[j]);
+ }
+ //printf("\n");
+
+ clock_t time;
+ while(get_current_batch(net) < net.max_batches){
+ i += 1;
+ time=clock();
+ float_pair p;
+ if(tokenized){
+ p = get_rnn_token_data(tokens, offsets, inputs, size, streams, steps);
+ }else{
+ p = get_rnn_data(text, offsets, inputs, size, streams, steps);
+ }
+
+ float loss = train_network_datum(net, p.x, p.y) / (batch);
+ free(p.x);
+ free(p.y);
+ if (avg_loss < 0) avg_loss = loss;
+ avg_loss = avg_loss*.9 + loss*.1;
+
+ int chars = get_current_batch(net)*batch;
+ fprintf(stderr, "%d: %f, %f avg, %f rate, %lf seconds, %f epochs\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), (float) chars/size);
+
+ for(j = 0; j < streams; ++j){
+ //printf("%d\n", j);
+ if(rand()%10 == 0){
+ //fprintf(stderr, "Reset\n");
+ offsets[j] = rand_size_t()%size;
+ reset_rnn_state(net, j);
+ }
+ }
+
+ if(i%1000==0){
+ char buff[256];
+ sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
+ save_weights(net, buff);
+ }
+ if(i%10==0){
+ char buff[256];
+ sprintf(buff, "%s/%s.backup", backup_directory, base);
+ save_weights(net, buff);
+ }
+ }
+ char buff[256];
+ sprintf(buff, "%s/%s_final.weights", backup_directory, base);
+ save_weights(net, buff);
+}
+
+void print_symbol(int n, char **tokens){
+ if(tokens){
+ printf("%s ", tokens[n]);
+ } else {
+ printf("%c", n);
+ }
+}
+
+void test_char_rnn(char *cfgfile, char *weightfile, int num, char *seed, float temp, int rseed, char *token_file)
+{
+ char **tokens = 0;
+ if(token_file){
+ size_t n;
+ tokens = read_tokens(token_file, &n);
+ }
+
+ srand(rseed);
+ char *base = basecfg(cfgfile);
+ fprintf(stderr, "%s\n", base);
+
+ network net = parse_network_cfg_custom(cfgfile, 1, 1); // batch=1, time_steps=1
+ 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;
+ int c = 0;
+ int len = strlen(seed);
+ float* input = (float*)xcalloc(inputs, sizeof(float));
+
+ /*
+ fill_cpu(inputs, 0, input, 1);
+ for(i = 0; i < 10; ++i){
+ network_predict(net, input);
+ }
+ fill_cpu(inputs, 0, input, 1);
+ */
+
+ for(i = 0; i < len-1; ++i){
+ c = seed[i];
+ input[c] = 1;
+ network_predict(net, input);
+ input[c] = 0;
+ print_symbol(c, tokens);
+ }
+ if(len) c = seed[len-1];
+ print_symbol(c, tokens);
+ for(i = 0; i < num; ++i){
+ input[c] = 1;
+ float *out = network_predict(net, input);
+ input[c] = 0;
+ for(j = 32; j < 127; ++j){
+ //printf("%d %c %f\n",j, j, out[j]);
+ }
+ for(j = 0; j < inputs; ++j){
+ if (out[j] < .0001) out[j] = 0;
+ }
+ c = sample_array(out, inputs);
+ //c = sample_array_custom(out, inputs);
+ //c = max_index(out, inputs);
+ //c = top_max_index(out, inputs, 2);
+ print_symbol(c, tokens);
+ }
+ printf("\n");
+}
+
+void test_tactic_rnn(char *cfgfile, char *weightfile, int num, float temp, int rseed, char *token_file)
+{
+ char **tokens = 0;
+ if(token_file){
+ size_t n;
+ tokens = read_tokens(token_file, &n);
+ }
+
+ srand(rseed);
+ char *base = basecfg(cfgfile);
+ fprintf(stderr, "%s\n", base);
+
+ network net = parse_network_cfg(cfgfile);
+ 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;
+ int c = 0;
+ float* input = (float*)xcalloc(inputs, sizeof(float));
+ float *out = 0;
+
+ while((c = getc(stdin)) != EOF){
+ input[c] = 1;
+ out = network_predict(net, input);
+ input[c] = 0;
+ }
+ for(i = 0; i < num; ++i){
+ for(j = 0; j < inputs; ++j){
+ if (out[j] < .0001) out[j] = 0;
+ }
+ int next = sample_array(out, inputs);
+ if(c == '.' && next == '\n') break;
+ c = next;
+ print_symbol(c, tokens);
+
+ input[c] = 1;
+ out = network_predict(net, input);
+ input[c] = 0;
+ }
+ printf("\n");
+}
+
+void valid_tactic_rnn(char *cfgfile, char *weightfile, char *seed)
+{
+ char *base = basecfg(cfgfile);
+ fprintf(stderr, "%s\n", base);
+
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ int inputs = get_network_input_size(net);
+
+ int count = 0;
+ int words = 1;
+ int c;
+ int len = strlen(seed);
+ float* input = (float*)xcalloc(inputs, sizeof(float));
+ int i;
+ for(i = 0; i < len; ++i){
+ c = seed[i];
+ input[(int)c] = 1;
+ network_predict(net, input);
+ input[(int)c] = 0;
+ }
+ float sum = 0;
+ c = getc(stdin);
+ float log2 = log(2);
+ int in = 0;
+ while(c != EOF){
+ int next = getc(stdin);
+ if(next == EOF) break;
+ if(next < 0 || next >= 255) error("Out of range character");
+
+ input[c] = 1;
+ float *out = network_predict(net, input);
+ input[c] = 0;
+
+ if(c == '.' && next == '\n') in = 0;
+ if(!in) {
+ if(c == '>' && next == '>'){
+ in = 1;
+ ++words;
+ }
+ c = next;
+ continue;
+ }
+ ++count;
+ sum += log(out[next])/log2;
+ c = next;
+ printf("%d %d Perplexity: %4.4f Word Perplexity: %4.4f\n", count, words, pow(2, -sum/count), pow(2, -sum/words));
+ }
+}
+
+void valid_char_rnn(char *cfgfile, char *weightfile, char *seed)
+{
+ char *base = basecfg(cfgfile);
+ fprintf(stderr, "%s\n", base);
+
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ int inputs = get_network_input_size(net);
+
+ int count = 0;
+ int words = 1;
+ int c;
+ int len = strlen(seed);
+ float* input = (float*)xcalloc(inputs, sizeof(float));
+ int i;
+ for(i = 0; i < len; ++i){
+ c = seed[i];
+ input[(int)c] = 1;
+ network_predict(net, input);
+ input[(int)c] = 0;
+ }
+ float sum = 0;
+ c = getc(stdin);
+ float log2 = log(2);
+ while(c != EOF){
+ int next = getc(stdin);
+ if(next == EOF) break;
+ if(next < 0 || next >= 255) error("Out of range character");
+ ++count;
+ if(next == ' ' || next == '\n' || next == '\t') ++words;
+ input[c] = 1;
+ float *out = network_predict(net, input);
+ input[c] = 0;
+ sum += log(out[next])/log2;
+ c = next;
+ printf("%d Perplexity: %4.4f Word Perplexity: %4.4f\n", count, pow(2, -sum/count), pow(2, -sum/words));
+ }
+}
+
+void vec_char_rnn(char *cfgfile, char *weightfile, char *seed)
+{
+ char *base = basecfg(cfgfile);
+ fprintf(stderr, "%s\n", base);
+
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ int inputs = get_network_input_size(net);
+
+ int c;
+ int seed_len = strlen(seed);
+ float* input = (float*)xcalloc(inputs, sizeof(float));
+ int i;
+ char *line;
+ while((line=fgetl(stdin)) != 0){
+ reset_rnn_state(net, 0);
+ for(i = 0; i < seed_len; ++i){
+ c = seed[i];
+ input[(int)c] = 1;
+ network_predict(net, input);
+ input[(int)c] = 0;
+ }
+ strip(line);
+ int str_len = strlen(line);
+ for(i = 0; i < str_len; ++i){
+ c = line[i];
+ input[(int)c] = 1;
+ network_predict(net, input);
+ input[(int)c] = 0;
+ }
+ c = ' ';
+ input[(int)c] = 1;
+ network_predict(net, input);
+ input[(int)c] = 0;
+
+ layer l = net.layers[0];
+ #ifdef GPU
+ cuda_pull_array(l.output_gpu, l.output, l.outputs);
+ #endif
+ printf("%s", line);
+ for(i = 0; i < l.outputs; ++i){
+ printf(",%g", l.output[i]);
+ }
+ printf("\n");
+ }
+}
+
+void run_char_rnn(int argc, char **argv)
+{
+ if(argc < 4){
+ fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+ return;
+ }
+ char *filename = find_char_arg(argc, argv, "-file", "data/shakespeare.txt");
+ char *seed = find_char_arg(argc, argv, "-seed", "\n\n");
+ 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));
+ int clear = find_arg(argc, argv, "-clear");
+ int tokenized = find_arg(argc, argv, "-tokenized");
+ char *tokens = find_char_arg(argc, argv, "-tokens", 0);
+
+ char *cfg = argv[3];
+ char *weights = (argc > 4) ? argv[4] : 0;
+ if(0==strcmp(argv[2], "train")) train_char_rnn(cfg, weights, filename, clear, tokenized);
+ else if(0==strcmp(argv[2], "valid")) valid_char_rnn(cfg, weights, seed);
+ else if(0==strcmp(argv[2], "validtactic")) valid_tactic_rnn(cfg, weights, seed);
+ else if(0==strcmp(argv[2], "vec")) vec_char_rnn(cfg, weights, seed);
+ else if(0==strcmp(argv[2], "generate")) test_char_rnn(cfg, weights, len, seed, temp, rseed, tokens);
+ else if(0==strcmp(argv[2], "generatetactic")) test_tactic_rnn(cfg, weights, len, temp, rseed, tokens);
+}
--
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