path: root/src/bin/mnist/src/main.c

#include <neuralnet/matrix.h>
#include <neuralnet/neuralnet.h>
#include <neuralnet/train.h>

#include <zlib.h>

#include <assert.h>
#include <bsd/string.h>
#include <linux/limits.h>
#include <math.h>
#include <stdbool.h>
#include <stdint.h>
#include <stdio.h>
#include <stdlib.h>

static const int TRAIN_ITERATIONS = 100;

static const int32_t IMAGE_FILE_MAGIC = 0x00000803;
static const int32_t LABEL_FILE_MAGIC = 0x00000801;

// Inputs of 0 cancel weights during training. This value is used to rescale the
// input pixels from [0,255] to [PIXEL_LOWER_BOUND, 1.0].
static const double PIXEL_LOWER_BOUND = 0.01;

// Scale the outputs to (0,1) since the sigmoid cannot produce 0 or 1.
static const double LABEL_LOWER_BOUND = 0.01;
static const double LABEL_UPPER_BOUND = 0.99;

// Epsilon used to compare R values.
static const double EPS = 1e-10;

#define min(a,b) ((a) < (b) ? (a) : (b))

typedef struct ImageSet {
  nnMatrix images;  // Images flattened into row vectors of the matrix.
  nnMatrix labels;  // One-hot-encoded labels.
  int count;        // Number of images and labels.
  int rows;         // Rows in an image.
  int cols;         // Columns in an image.
} ImageSet;

static void usage(const char* argv0) {
  fprintf(stderr, "Usage: %s <path to mnist files directory> [num images]\n", argv0);
  fprintf(stderr, "\n");
  fprintf(stderr, "  Use -1 for [num images] to use all the images in the data set\n");
}

static bool R_eq(R a, R b) {
  return fabs(a-b) <= EPS;
}

static void PrintImage(const nnMatrix* images, int rows, int cols, int image_index) {
  assert(images);
  assert((0 <= image_index) && (image_index < images->rows));

  // Top line.
  for (int j = 0; j < cols/2; ++j) {
    printf(" -");
  }
  printf("\n");

  // Image.
  const R* value = nnMatrixRow(images, image_index);
  for (int i = 0; i < rows; ++i) {
    printf("|");
    for (int j = 0; j < cols; ++j) {
      if (*value > 0.8) {
        printf("#");
      } else if (*value > 0.5) {
        printf("*");
      }
      else if (*value > PIXEL_LOWER_BOUND) {
        printf(":");
      } else if (*value == 0.0) {
        // Values should not be exactly 0, otherwise they cancel out weights
        // during training.
        printf("X");
      } else {
        printf(" ");
      }
      value++;
    }
    printf("|\n");
  }

  // Bottom line.
  for (int j = 0; j < cols/2; ++j) {
    printf(" -");
  }
  printf("\n");
}

static void PrintLabel(const nnMatrix* labels, int label_index) {
  assert(labels);
  assert((0 <= label_index) && (label_index < labels->rows));

  // Compute the label from the one-hot encoding.
  const R* value = nnMatrixRow(labels, label_index);
  int label = -1;
  for (int i = 0; i < 10; ++i) {
    if (R_eq(*value++, LABEL_UPPER_BOUND)) {
      label = i;
      break;
    }
  }
  assert((0 <= label) && (label <= 9));

  printf("Label: %d ( ", label);
  value = nnMatrixRow(labels, label_index);
  for (int i = 0; i < 10; ++i) {
    printf("%.3f ", *value++);
  }
  printf(")\n");
}

static R lerp(R a, R b, R t) {
  return a + t*(b-a);
}

/// Rescales a pixel from [0,255] to [PIXEL_LOWER_BOUND, 1.0].
static R FormatPixel(uint8_t pixel) {
  const R value = (R)(pixel) / 255.0 * (1.0 - PIXEL_LOWER_BOUND) + PIXEL_LOWER_BOUND;
  assert(value >= PIXEL_LOWER_BOUND);
  assert(value <= 1.0);
  return value;
}

/// Rescales a one-hot-encoded label value to (0,1).
static R FormatLabel(R label) {
  const R value = lerp(LABEL_LOWER_BOUND, LABEL_UPPER_BOUND, label);
  assert(value > 0.0);
  assert(value < 1.0);
  return value;
}

static int32_t ReverseEndian32(int32_t x) {
  const int32_t x0 = x & 0xff;
  const int32_t x1 = (x >> 8) & 0xff;
  const int32_t x2 = (x >> 16) & 0xff;
  const int32_t x3 = (x >> 24) & 0xff;
  return (x0 << 24) | (x1 << 16) | (x2 << 8) | x3;
}

static void ImageToMatrix(
    const uint8_t* pixels, int num_pixels, int row, nnMatrix* images) {
  assert(pixels);
  assert(images);

  for (int i = 0; i < num_pixels; ++i) {
    const R pixel = FormatPixel(pixels[i]);
    nnMatrixSet(images, row, i, pixel);
  }
}

static bool ReadImages(gzFile images_file, int max_num_images, ImageSet* image_set) {
  assert(images_file != Z_NULL);
  assert(image_set);

  bool success = false;

  uint8_t* pixels = 0;

  int32_t magic, total_images, rows, cols;
  if ( (gzread(images_file, (char*)&magic, sizeof(int32_t)) != sizeof(int32_t)) ||
       (gzread(images_file, (char*)&total_images, sizeof(int32_t)) != sizeof(int32_t)) ||
       (gzread(images_file, (char*)&rows, sizeof(int32_t)) != sizeof(int32_t)) ||
       (gzread(images_file, (char*)&cols, sizeof(int32_t)) != sizeof(int32_t)) ) {
    fprintf(stderr, "Failed to read header\n");
    goto cleanup;
  }

  magic = ReverseEndian32(magic);
  total_images = ReverseEndian32(total_images);
  rows = ReverseEndian32(rows);
  cols = ReverseEndian32(cols);

  if (magic != IMAGE_FILE_MAGIC) {
    fprintf(stderr, "Magic number mismatch. Got %x, expected: %x\n",
      magic, IMAGE_FILE_MAGIC);
    goto cleanup;
  }

  printf("Magic: %.8x\nTotal images: %d\nRows: %d\nCols: %d\n",
    magic, total_images, rows, cols);

  total_images = max_num_images >= 0 ? min(total_images, max_num_images) : total_images;

  // Images are flattened into single row vectors.
  const int num_pixels = rows * cols;
  image_set->images = nnMatrixMake(total_images, num_pixels);
  image_set->count = total_images;
  image_set->rows = rows;
  image_set->cols = cols;

  pixels = calloc(1, num_pixels);
  if (!pixels) {
    fprintf(stderr, "Failed to allocate image buffer\n");
    goto cleanup;
  }

  for (int i = 0; i < total_images; ++i) {
    const int bytes_read = gzread(images_file, pixels, num_pixels);
    if (bytes_read < num_pixels) {
      fprintf(stderr, "Failed to read image %d\n", i);
      goto cleanup;
    }
    ImageToMatrix(pixels, num_pixels, i, &image_set->images);
  }

  success = true;

cleanup:
  if (pixels) {
    free(pixels);
  }
  if (!success) {
    nnMatrixDel(&image_set->images);
  }
  return success;
}

static void OneHotEncode(const uint8_t* labels_bytes, int num_labels, nnMatrix* labels) {
  assert(labels_bytes);
  assert(labels);
  assert(labels->rows == num_labels);
  assert(labels->cols == 10);

  static const R one_hot[10][10] = {
    { 1, 0, 0, 0, 0, 0, 0, 0, 0, 0 },
    { 0, 1, 0, 0, 0, 0, 0, 0, 0, 0 },
    { 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 },
    { 0, 0, 0, 1, 0, 0, 0, 0, 0, 0 },
    { 0, 0, 0, 0, 1, 0, 0, 0, 0, 0 },
    { 0, 0, 0, 0, 0, 1, 0, 0, 0, 0 },
    { 0, 0, 0, 0, 0, 0, 1, 0, 0, 0 },
    { 0, 0, 0, 0, 0, 0, 0, 1, 0, 0 },
    { 0, 0, 0, 0, 0, 0, 0, 0, 1, 0 },
    { 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 },
  };

  R* value = labels->values;

  for (int i = 0; i < num_labels; ++i) {
    const uint8_t label = labels_bytes[i];
    const R* one_hot_value = one_hot[label];

    for (int j = 0; j < 10; ++j) {
      *value++ = FormatLabel(*one_hot_value++);
    }
  }
}

static int OneHotDecode(const nnMatrix* label_matrix) {
  assert(label_matrix);
  assert(label_matrix->cols == 10);
  assert(label_matrix->rows == 1);

  R max_value = 0;
  int pos_max = 0;
  for (int i = 0; i < 10; ++i) {
    const R value = nnMatrixAt(label_matrix, 0, i);
    if (value > max_value) {
      max_value = value;
      pos_max = i;
    }
  }
  assert(pos_max >= 0);
  assert(pos_max <= 10);
  return pos_max;
}

static bool ReadLabels(gzFile labels_file, int max_num_labels, ImageSet* image_set) {
  assert(labels_file != Z_NULL);
  assert(image_set != 0);

  bool success = false;

  uint8_t* labels = 0;

  int32_t magic, total_labels;
  if ( (gzread(labels_file, (char*)&magic, sizeof(int32_t)) != sizeof(int32_t)) ||
       (gzread(labels_file, (char*)&total_labels, sizeof(int32_t)) != sizeof(int32_t)) ) {
    fprintf(stderr, "Failed to read header\n");
    goto cleanup;
  }

  magic = ReverseEndian32(magic);
  total_labels = ReverseEndian32(total_labels);

  if (magic != LABEL_FILE_MAGIC) {
    fprintf(stderr, "Magic number mismatch. Got %x, expected: %x\n",
      magic, LABEL_FILE_MAGIC);
    goto cleanup;
  }

  printf("Magic: %.8x\nTotal labels: %d\n", magic, total_labels);

  total_labels = max_num_labels >= 0 ? min(total_labels, max_num_labels) : total_labels;

  assert(image_set->count == total_labels);

  // One-hot encoding of labels, 10 values (digits) per label.
  image_set->labels = nnMatrixMake(total_labels, 10);

  labels = calloc(total_labels, sizeof(uint8_t));
  if (!labels) {
    fprintf(stderr, "Failed to allocate labels buffer\n");
    goto cleanup;
  }

  if (gzread(labels_file, labels, total_labels * sizeof(uint8_t)) != total_labels) {
    fprintf(stderr, "Failed to read labels\n");
    goto cleanup;
  }

  OneHotEncode(labels, total_labels, &image_set->labels);

  success = true;

cleanup:
  if (labels) {
    free(labels);
  }
  if (!success) {
    nnMatrixDel(&image_set->labels);
  }
  return success;
}

int main(int argc, const char** argv) {
  if (argc < 2) {
    usage(argv[0]);
    return 1;
  }

  bool success = false;

  gzFile train_images_file = Z_NULL;
  gzFile train_labels_file = Z_NULL;
  gzFile test_images_file  = Z_NULL;
  gzFile test_labels_file  = Z_NULL;
  ImageSet train_set = { 0 };
  ImageSet test_set  = { 0 };
  nnNeuralNetwork* net = 0;
  nnQueryObject* query = 0;

  const char* mnist_files_dir = argv[1];
  const int max_num_images = argc > 2 ? atoi(argv[2]) : -1;

  char train_labels_path[PATH_MAX];
  char train_images_path[PATH_MAX];
  char test_labels_path[PATH_MAX];
  char test_images_path[PATH_MAX];
  strlcpy(train_labels_path, mnist_files_dir, PATH_MAX);
  strlcpy(train_images_path, mnist_files_dir, PATH_MAX);
  strlcpy(test_labels_path,  mnist_files_dir, PATH_MAX);
  strlcpy(test_images_path,  mnist_files_dir, PATH_MAX);
  strlcat(train_labels_path, "/train-labels-idx1-ubyte.gz", PATH_MAX);
  strlcat(train_images_path, "/train-images-idx3-ubyte.gz", PATH_MAX);
  strlcat(test_labels_path,  "/t10k-labels-idx1-ubyte.gz",  PATH_MAX);
  strlcat(test_images_path,  "/t10k-images-idx3-ubyte.gz",  PATH_MAX);

  train_images_file = gzopen(train_images_path, "r");
  if (train_images_file == Z_NULL) {
    fprintf(stderr, "Failed to open file: %s\n", train_images_path);
    goto cleanup;
  }

  train_labels_file = gzopen(train_labels_path, "r");
  if (train_labels_file == Z_NULL) {
    fprintf(stderr, "Failed to open file: %s\n", train_labels_path);
    goto cleanup;
  }

  test_images_file = gzopen(test_images_path, "r");
  if (test_images_file == Z_NULL) {
    fprintf(stderr, "Failed to open file: %s\n", test_images_path);
    goto cleanup;
  }

  test_labels_file = gzopen(test_labels_path, "r");
  if (test_labels_file == Z_NULL) {
    fprintf(stderr, "Failed to open file: %s\n", test_labels_path);
    goto cleanup;
  }

  if (!ReadImages(train_images_file, max_num_images, &train_set)) {
    goto cleanup;
  }
  if (!ReadLabels(train_labels_file, max_num_images, &train_set)) {
    goto cleanup;
  }

  if (!ReadImages(test_images_file, max_num_images, &test_set)) {
    goto cleanup;
  }
  if (!ReadLabels(test_labels_file, max_num_images, &test_set)) {
    goto cleanup;
  }

  printf("\nTraining image/label pair examples:\n");
  for (int i = 0; i < min(3, train_set.images.rows); ++i) {
    PrintImage(&train_set.images, train_set.rows, train_set.cols, i);
    PrintLabel(&train_set.labels, i);
    printf("\n");
  }

  // Network definition.
  const int image_size_pixels = train_set.rows * train_set.cols;
  const int num_layers = 2;
  const int layer_sizes[3] = { image_size_pixels, 100, 10 };
  const nnActivation layer_activations[2] = { nnSigmoid, nnSigmoid };
  if (!(net = nnMakeNet(num_layers, layer_sizes, layer_activations))) {
    fprintf(stderr, "Failed to create neural network\n");
    goto cleanup;
  }

  // Train.
  printf("Training with up to %d images from the data set\n\n", max_num_images);
  const nnTrainingParams training_params = {
    .learning_rate = 0.1,
    .max_iterations = TRAIN_ITERATIONS,
    .seed = 0,
    .weight_init = nnWeightInitNormal,
    .debug = true,
  };
  nnTrain(net, &train_set.images, &train_set.labels, &training_params);

  // Test.
  int hits = 0;
  query = nnMakeQueryObject(net, /*num_inputs=*/1);
  for (int i = 0; i < test_set.count; ++i) {
    const nnMatrix test_image = nnMatrixBorrowRows(&test_set.images, i, 1);
    const nnMatrix test_label = nnMatrixBorrowRows(&test_set.labels, i, 1);

    nnQuery(net, query, &test_image);

    const int test_label_expected = OneHotDecode(&test_label);
    const int test_label_actual   = OneHotDecode(nnNetOutputs(query));

    if (test_label_actual == test_label_expected) {
      ++hits;
    }
  }
  const R hit_ratio = (R)hits / (R)test_set.count;
  printf("Test images: %d\n", test_set.count);
  printf("Hits: %d/%d (%.3f%%)\n", hits, test_set.count, hit_ratio*100);

  success = true;

cleanup:
  if (query) {
    nnDeleteQueryObject(&query);
  }
  if (net) {
    nnDeleteNet(&net);
  }
  nnMatrixDel(&train_set.images);
  nnMatrixDel(&test_set.images);
  if (train_images_file != Z_NULL) {
    gzclose(train_images_file);
  }
  if (train_labels_file != Z_NULL) {
    gzclose(train_labels_file);
  }
  if (test_images_file != Z_NULL) {
    gzclose(test_images_file);
  }
  if (test_labels_file != Z_NULL) {
    gzclose(test_labels_file);
  }
  return success ? 0 : 1;
}