Reformatted to use proper command-line switches

paragraph_nn.c

@@ -1,59 +1,116 @@
 #include <stdio.h>
+#include <string.h>
 #include "fann.h"
 
+int ArgPos(char *str, int argc, char **argv) {
+  int a;
+  for (a = 1; a < argc; a++) if (!strcmp(str, argv[a])) {
+    if (a == argc - 1) {
+      printf("Argument missing for %s\n", str);
+      exit(1);
+    }
+    return a;
+  }
+  return -1;
+}
+
 int main(int argc, char *argv[]) {
-  if (argc < 4) {
-    printf("Usage: ./paragraph_nn <train_data> <test_data> <save_file> <load_file>");
-    exit(0);
+  int i;
+  if (argc == 1) {
+    printf("PARAGRAPH VECTOR neural network trainer\n\n");
+    printf("Options:\n");
+    printf("Parameters for training:\n");
+    printf("\t-hidden <int>\n");
+    printf("\t\tNumber of neurons in the hidden layer; default 50\n");
+    printf("\t-error <float>\n");
+    printf("\t\tDesired training error; default 0.001\n");
+    printf("\t-epochs <int>\n");
+    printf("\t\tMax epochs for training; default 3000\n");
+    printf("\t-train <file>\n");
+    printf("\t\tName of file with training vectors (See FANN documentation for format.)\n");
+    printf("\t-test <file>\n");
+    printf("\t\tname of file with test vectors (See FANN documentation for format.)\n");
+    printf("\t-network <file>\n");
+    printf("\t\tName of file with previously trained network (-train option will be ignored.)\n");
+    printf("\t-output <file>\n");
+    printf("\t\tName of file to save network.\n");
   }
 
-  const unsigned int num_layers = 3;
-  const unsigned int num_neurons_hidden = 50;
-  const float desired_error = (const float) 0.001;
+  unsigned int num_layers = 3;
+  unsigned int num_neurons_hidden = 50;
+  unsigned int max_epochs = 3000;
+  float desired_error = 0.001f;
+  char train_file[1000];
+  char test_file[1000];
+  char network_file[1000];
+  char output_file[1000];
   struct fann *ann;
   struct fann_train_data *train_data, *test_data;
-  unsigned int i = 0;
+
   fann_type* output;
   unsigned int errors = 0;
+
+  train_file[0] = 0;
+  test_file[0] = 0;
+  network_file[0] = 0;
+  output_file[0] = 0;
+
+  if ((i = ArgPos((char *)"-hidden", argc, argv)) > 0) num_neurons_hidden = atoi(argv[i + 1]);
+  if ((i = ArgPos((char *)"-epochs", argc, argv)) > 0) max_epochs = atoi(argv[i + 1]);
+  if ((i = ArgPos((char *)"-error", argc, argv)) > 0) desired_error = atof(argv[i + 1]);
+  if ((i = ArgPos((char *)"-train", argc, argv)) > 0) strcpy(train_file, argv[i + 1]);
+  if ((i = ArgPos((char *)"-test", argc, argv)) > 0) strcpy(test_file, argv[i + 1]);
+  if ((i = ArgPos((char *)"-network", argc, argv)) > 0) strcpy(network_file, argv[i + 1]);
+  if ((i = ArgPos((char *)"-output", argc, argv)) > 0) strcpy(output_file, argv[i + 1]);
 
-  if(argc == 5) {
+  if(network_file[0] != 0) {
     printf("Loading network.\n");
-    ann = fann_create_from_file(argv[4]);
-  } else {
-    train_data = fann_read_train_from_file(argv[1]);
+    ann = fann_create_from_file(network_file);
+  } else if (train_file[0] != 0) {
+    printf("Reading training data.\n");
+    train_data = fann_read_train_from_file(train_file);
 
     ann = fann_create_standard(num_layers, train_data->num_input, num_neurons_hidden, train_data->num_output);
 
     printf("Training network.\n");
 
-    fann_set_training_algorithm(ann, FANN_TRAIN_INCREMENTAL);
+    fann_set_training_algorithm(ann, FANN_TRAIN_RPROP);
     fann_set_learning_momentum(ann, 0.4f);
     fann_shuffle_train_data(train_data);
-    fann_train_on_data(ann, train_data, 3000, 10, desired_error);
-  }
-  printf("Testing network.\n");
+    fann_train_on_data(ann, train_data, max_epochs, 10, desired_error);
+  } else
+    exit(0);
+
+  if(test_file[0] != 0) {
+    printf("Testing network.\n");
 
-  test_data = fann_read_train_from_file(argv[2]);
+    test_data = fann_read_train_from_file(test_file);
 
-  fann_reset_MSE(ann);
-  for(i = 0; i < fann_length_train_data(test_data); i++) {
-    if(*test_data->output[i] == (fann_type)-1) continue;
-    fann_test(ann, test_data->input[i], test_data->output[i]);
-    output = fann_run(ann, test_data->input[i]);
-    if(*output != *test_data->output[i])
-      errors++;
+    fann_reset_MSE(ann);
+    int num_test_data = fann_length_train_data(test_data);
+    for(i = 0; i < num_test_data; i++) {
+      if(*test_data->output[i] == (fann_type)-1) continue;
+      fann_test(ann, test_data->input[i], test_data->output[i]);
+      output = fann_run(ann, test_data->input[i]);
+      if(memcmp(output,test_data->output[i],sizeof(output)) != 0)
+        errors++;
+    }
+
+
+    printf("MSE error on test data: %f\n", fann_get_MSE(ann));
+    printf("Error rate on test data: %f%%\n", (((float) num_test_data - errors)/num_test_data) * 100);
   }
 
-  printf("MSE error on test data: %f\n", fann_get_MSE(ann));
-  printf("Error rate on test data: %f%%\n", ((float)errors)/fann_length_train_data(test_data) * 100);
-
-  printf("Saving network.\n");
+  if(output_file[0] != 0) {
+    printf("Saving network.\n");
 
-  fann_save(ann, argv[3]);
-
+    fann_save(ann, output_file);
+  }
   printf("Cleaning up.\n");
-  fann_destroy_train(train_data);
-  fann_destroy_train(test_data);
+  if (train_file[0] != 0)
+    fann_destroy_train(train_data);
+  if (test_file[0] != 0 )
+    fann_destroy_train(test_data);
   fann_destroy(ann);
 
   return 0;