facetrain.c

神经网络的识别程序

/*
 ******************************************************************
 * HISTORY
 * 15-Oct-94  Jeff Shufelt (js), Carnegie Mellon University
 *      Prepared for 15-681, Fall 1994.
 *
 * Tue Oct  7 08:12:06 EDT 1997, bthom, added a few comments,
 *       tagged w/bthom
 *
 ******************************************************************
 */

#include <stdio.h>
#include <math.h>
#include <pgmimage.h>
#include <backprop.h>

extern char *strcpy();
extern void exit();

main(argc, argv)
int argc;
char *argv[];
{
  char netname[256], trainname[256], test1name[256], test2name[256];
  IMAGELIST *trainlist, *test1list, *test2list;
  int ind, epochs, seed, savedelta, list_errors;

  seed = 102194;   /*** today's date seemed like a good default ***/
  epochs = 100;
  savedelta = 100;
  list_errors = 0;
  netname[0] = trainname[0] = test1name[0] = test2name[0] = '\0';

  if (argc < 2) {
    printusage(argv[0]);
    exit (-1);
  }

  /*** Create imagelists ***/
  trainlist = imgl_alloc();
  test1list = imgl_alloc();
  test2list = imgl_alloc();

  /*** Scan command line ***/
  for (ind = 1; ind < argc; ind++) {

    /*** Parse switches ***/
    if (argv[ind][0] == '-') {
      switch (argv[ind][1]) {  
        case 'n': strcpy(netname, argv[++ind]);
                  break;
        case 'e': epochs = atoi(argv[++ind]);
                  break;
        case 's': seed = atoi(argv[++ind]);
                  break;
        case 'S': savedelta = atoi(argv[++ind]);
                  break;
        case 't': strcpy(trainname, argv[++ind]);
                  break;
        case '1': strcpy(test1name, argv[++ind]);
                  break;
        case '2': strcpy(test2name, argv[++ind]);
                  break;
        case 'T': list_errors = 1;
	          epochs = 0;
                  break;
        default : printf("Unknown switch '%c'\n", argv[ind][1]);
                  break;
      }
    }
  }

  /*** If any train, test1, or test2 sets have been specified, then
       load them in. ***/
  if (trainname[0] != '\0') 
    imgl_load_images_from_textfile(trainlist, trainname);
  if (test1name[0] != '\0') 
    imgl_load_images_from_textfile(test1list, test1name);
  if (test2name[0] != '\0')
    imgl_load_images_from_textfile(test2list, test2name);

  /*** If we haven't specified a network save file, we should... ***/
  if (netname[0] == '\0') {
    printf("%s: Must specify an output file, i.e., -n <network file>\n",
     argv[0]);
    exit (-1);
  }

  /*** Don't try to train if there's no training data ***/
  if (trainname[0] == '\0') {
    epochs = 0;
  }

  /*** Initialize the neural net package ***/
  bpnn_initialize(seed);

  /*** Show number of images in train, test1, test2 ***/
  printf("%d images in training set\n", trainlist->n);
  printf("%d images in test1 set\n", test1list->n);
  printf("%d images in test2 set\n", test2list->n);

  /*** If we've got at least one image to train on, go train the net ***/
  backprop_face(trainlist, test1list, test2list, epochs, savedelta, netname,
		list_errors);

  exit(0);
}


backprop_face(trainlist, test1list, test2list, epochs, savedelta, netname,
	      list_errors)
IMAGELIST *trainlist, *test1list, *test2list;
int epochs, savedelta, list_errors;
char *netname;
{
  IMAGE *iimg;
  BPNN *net;
  int train_n, epoch, i, imgsize;
  double out_err, hid_err, sumerr;

  train_n = trainlist->n;

  /*** Read network in if it exists, otherwise make one from scratch ***/
  if ((net = bpnn_read(netname)) == NULL) {
    if (train_n > 0) {
      printf("Creating new network '%s'\n", netname);
      iimg = trainlist->list[0];
      imgsize = ROWS(iimg) * COLS(iimg);
      /* bthom ===========================
	make a net with:
	  imgsize inputs, 4 hiden units, and 1 output unit
          */
      net = bpnn_create(imgsize, 4, 1);
    } else {
      printf("Need some images to train on, use -t\n");
      return;
    }
  }

  if (epochs > 0) {
    printf("Training underway (going to %d epochs)\n", epochs);
    printf("Will save network every %d epochs\n", savedelta);
    fflush(stdout);
  }

  /*** Print out performance before any epochs have been completed. ***/
  printf("0 0.0 ");
  performance_on_imagelist(net, trainlist, 0);
  performance_on_imagelist(net, test1list, 0);
  performance_on_imagelist(net, test2list, 0);
  printf("\n");  fflush(stdout);
  if (list_errors) {
    printf("\nFailed to classify the following images from the training set:\n");
    performance_on_imagelist(net, trainlist, 1);
    printf("\nFailed to classify the following images from the test set 1:\n");
    performance_on_imagelist(net, test1list, 1);
    printf("\nFailed to classify the following images from the test set 2:\n");
    performance_on_imagelist(net, test2list, 1);
  }

  /************** Train it *****************************/
  for (epoch = 1; epoch <= epochs; epoch++) {

    printf("%d ", epoch);  fflush(stdout);

    sumerr = 0.0;
    for (i = 0; i < train_n; i++) {

      /** Set up input units on net with image i **/
      load_input_with_image(trainlist->list[i], net);

      /** Set up target vector for image i **/
      load_target(trainlist->list[i], net);

      /** Run backprop, learning rate 0.3, momentum 0.3 **/
      bpnn_train(net, 0.3, 0.3, &out_err, &hid_err);

      sumerr += (out_err + hid_err);
    }
    printf("%g ", sumerr);

    /*** Evaluate performance on train, test, test2, and print perf ***/
    performance_on_imagelist(net, trainlist, 0);
    performance_on_imagelist(net, test1list, 0);
    performance_on_imagelist(net, test2list, 0);
    printf("\n");  fflush(stdout);

    /*** Save network every 'savedelta' epochs ***/
    if (!(epoch % savedelta)) {
      bpnn_save(net, netname);
    }

  }
  printf("\n"); fflush(stdout);

  /** Save the trained network **/
  if (epochs > 0) {
    bpnn_save(net, netname);
  }
}


/*** Computes the performance of a net on the images in the imagelist. ***/
/*** Prints out the percentage correct on the image set, and the
     average error between the target and the output units for the set. ***/
performance_on_imagelist(net, il, list_errors)
BPNN *net;
IMAGELIST *il;
int list_errors;
{
  double err, val;
  int i, n, j, correct;

  err = 0.0;
  correct = 0;
  n = il->n;
  if (n > 0) {
    for (i = 0; i < n; i++) {

      /*** Load the image into the input layer. **/
      load_input_with_image(il->list[i], net);

      /*** Run the net on this input. **/
      bpnn_feedforward(net);

      /*** Set up the target vector for this image. **/
      load_target(il->list[i], net);

      /*** See if it got it right. ***/
      if (evaluate_performance(net, &val, 0)) {
        correct++;
      } else if (list_errors) {
	printf("%s - outputs ", NAME(il->list[i]));
	for (j = 1; j <= net->output_n; j++) {
	  printf("%.3f ", net->output_units[j]);
	}
	putchar('\n');
      }
      err += val;
    }

    err = err / (double) n;

    if (!list_errors)
      /* bthom==================================
	 this line prints part of the ouput line
	 discussed in section 3.1.2 of homework
          */
      printf("%g %g ", ((double) correct / (double) n) * 100.0, err);
  } else {
    if (!list_errors)
      printf("0.0 0.0 ");
  }
}

evaluate_performance(net, err)
BPNN *net;
double *err;
{
  double delta;

  delta = net->target[1] - net->output_units[1];

  *err = (0.5 * delta * delta);

  /*** If the target unit is on... ***/
  if (net->target[1] > 0.5) {

    /*** If the output unit is on, then we correctly recognized me! ***/
    if (net->output_units[1] > 0.5) {
      return (1);

    /*** otherwise, we didn't think it was me... ***/
    } else {
      return (0);
    }

  /*** Else, the target unit is off... ***/
  } else {

    /*** If the output unit is on, then we mistakenly thought it was me ***/
    if (net->output_units[1] > 0.5) {
      return (0);

    /*** else, we correctly realized that it wasn't me ***/
    } else {
      return (1);
    }
  }

}



printusage(prog)
char *prog;
{
  printf("USAGE: %s\n", prog);
  printf("       -n <network file>\n");
  printf("       [-e <number of epochs>]\n");
  printf("       [-s <random number generator seed>]\n");
  printf("       [-S <number of epochs between saves of network>]\n");
  printf("       [-t <training set list>]\n");
  printf("       [-1 <testing set 1 list>]\n");
  printf("       [-2 <testing set 2 list>]\n");
  printf("       [-T]\n");
}