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2025-01-16 22:03:28 -06:00
parent 40a2b072b8
commit cc944d8cc3
4 changed files with 535 additions and 199 deletions
--- a/mnist.c
+++ b/mnist.c
@@ -0,0 +1,189 @@
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include "cnn.c"
+
+#define IMG_HEIGHT 28
+#define IMG_WIDTH 28
+#define NUM_CLASSES 10
+#define BATCH_SIZE 32
+#define LEARNING_RATE 0.01
+#define NUM_EPOCHS 10
+
+float* read_mnist_images(const char* filename, int* num_images) {
+	FILE* fp = fopen(filename, "rb");
+	if (!fp) {
+		printf("Error opening file %s\n", filename);
+		return NULL;
+	}
+
+	int magic_number = 0;
+	fread(&magic_number, sizeof(int), 1, fp);
+	magic_number = ((magic_number & 0xff000000) >> 24) | 
+		((magic_number & 0x00ff0000) >> 8) |
+		((magic_number & 0x0000ff00) << 8) |
+		((magic_number & 0x000000ff) << 24);
+
+	if (magic_number != 2051) {
+		printf("Invalid MNIST image file format\n");
+		fclose(fp);
+		return NULL;
+	}
+
+	fread(num_images, sizeof(int), 1, fp);
+	*num_images = ((*num_images & 0xff000000) >> 24) |
+		((*num_images & 0x00ff0000) >> 8) |
+		((*num_images & 0x0000ff00) << 8) |
+		((*num_images & 0x000000ff) << 24);
+
+	int rows, cols;
+	fread(&rows, sizeof(int), 1, fp);
+	fread(&cols, sizeof(int), 1, fp);
+	rows = ((rows & 0xff000000) >> 24) |
+		((rows & 0x00ff0000) >> 8) |
+		((rows & 0x0000ff00) << 8) |
+		((rows & 0x000000ff) << 24);
+	cols = ((cols & 0xff000000) >> 24) |
+		((cols & 0x00ff0000) >> 8) |
+		((cols & 0x0000ff00) << 8) |
+		((cols & 0x000000ff) << 24);
+
+	if (rows != IMG_HEIGHT || cols != IMG_WIDTH) {
+		printf("Invalid image dimensions\n");
+		fclose(fp);
+		return NULL;
+	}
+
+	float* images = (float*)malloc(*num_images * IMG_HEIGHT * IMG_WIDTH * sizeof(float));
+	unsigned char* temp = (unsigned char*)malloc(IMG_HEIGHT * IMG_WIDTH);
+
+	for (int i = 0; i < *num_images; i++) {
+		fread(temp, 1, IMG_HEIGHT * IMG_WIDTH, fp);
+		for (int j = 0; j < IMG_HEIGHT * IMG_WIDTH; j++) {
+			images[i * IMG_HEIGHT * IMG_WIDTH + j] = temp[j] / 255.0f;
+		}
+	}
+
+	free(temp);
+	fclose(fp);
+	return images;
+}
+
+float* read_mnist_labels(const char* filename, int* num_labels) {
+	FILE* fp = fopen(filename, "rb");
+	if (!fp) {
+		printf("Error opening file %s\n", filename);
+		return NULL;
+	}
+
+	int magic_number = 0;
+	fread(&magic_number, sizeof(int), 1, fp);
+	magic_number = ((magic_number & 0xff000000) >> 24) |
+		((magic_number & 0x00ff0000) >> 8) |
+		((magic_number & 0x0000ff00) << 8) |
+		((magic_number & 0x000000ff) << 24);
+
+	if (magic_number != 2049) {
+		printf("Invalid MNIST label file format\n");
+		fclose(fp);
+		return NULL;
+	}
+
+	fread(num_labels, sizeof(int), 1, fp);
+	*num_labels = ((*num_labels & 0xff000000) >> 24) |
+		((*num_labels & 0x00ff0000) >> 8) |
+		((*num_labels & 0x0000ff00) << 8) |
+		((*num_labels & 0x000000ff) << 24);
+
+	float* labels = (float*)calloc(*num_labels * NUM_CLASSES, sizeof(float));
+	unsigned char* temp = (unsigned char*)malloc(*num_labels);
+
+	fread(temp, 1, *num_labels, fp);
+	for (int i = 0; i < *num_labels; i++) {
+		labels[i * NUM_CLASSES + temp[i]] = 1.0f;
+	}
+
+	free(temp);
+	fclose(fp);
+	return labels;
+}
+
+int main() {
+	// load mnist
+	int num_train_images, num_train_labels;
+	float* train_images = read_mnist_images("train-images-idx3-ubyte", &num_train_images);
+	float* train_labels = read_mnist_labels("train-labels-idx1-ubyte", &num_train_labels);
+
+	// creating a lenet-5 inspired network
+	Network* network = create_network(8);
+	network->layers[0] = create_input(IMG_HEIGHT, IMG_WIDTH, 1);
+	network->layers[1] = create_conv(IMG_HEIGHT, IMG_WIDTH, 1, 6, 5, 1, 2);
+	network->layers[2] = create_maxpool(network->layers[1]->height, network->layers[1]->width, network->layers[1]->channels, 2, 2);
+	network->layers[3] = create_conv(network->layers[2]->height, network->layers[2]->width, network->layers[2]->channels, 16, 5, 1, 0);
+	network->layers[4] = create_maxpool(network->layers[3]->height, network->layers[3]->width, network->layers[3]->channels, 2, 2);
+	network->layers[5] = create_fc(120, network->layers[4]->height * network->layers[4]->width * network->layers[4]->channels, a_sigmoid);
+	network->layers[6] = create_fc(84, 120, a_sigmoid);
+	network->layers[7] = create_fc(NUM_CLASSES, 84, a_softmax);
+
+	// training loop
+	for (int epoch = 0; epoch < NUM_EPOCHS; epoch++) {
+		float total_loss = 0.0f;
+		int correct = 0;
+
+		for (int i = 0; i < num_train_images; i++) {
+			// forward pass
+			network_forward(network, &train_images[i * IMG_HEIGHT * IMG_WIDTH]);
+
+			// accuracy
+			float* output = network->layers[network->num_layers - 1]->output;
+			int predicted = 0;
+			float max_prob = output[0];
+			for (int j = 1; j < NUM_CLASSES; j++) {
+				if (output[j] > max_prob) {
+					max_prob = output[j];
+					predicted = j;
+				}
+			}
+
+			int true_label = 0;
+			for (int j = 0; j < NUM_CLASSES; j++) {
+				if (train_labels[i * NUM_CLASSES + j] > 0.5f) {
+					true_label = j;
+					break;
+				}
+			}
+
+			if (predicted == true_label) correct++;
+
+			// backprop 
+			network_backward(network, &train_labels[i * NUM_CLASSES], LEARNING_RATE);
+
+			// cross entropy loss
+			float loss = 0.0f;
+			for (int j = 0; j < NUM_CLASSES; j++) {
+				if (train_labels[i * NUM_CLASSES + j] > 0.5f) {
+					loss -= log(output[j] + 1e-10);
+				}
+			}
+			total_loss += loss;
+
+			// progress
+			if ((i + 1) % 100 == 0) {
+				printf("Epoch %d/%d, Step %d/%d, Loss: %.4f, Accuracy: %.2f%%\n",
+					 epoch + 1, NUM_EPOCHS, i + 1, num_train_images,
+					 total_loss / (i + 1), 100.0f * correct / (i + 1));
+			}
+		}
+
+		printf("Epoch %d/%d completed, Average Loss: %.4f, Accuracy: %.2f%%\n",
+				 epoch + 1, NUM_EPOCHS, total_loss / num_train_images,
+				 100.0f * correct / num_train_images);
+	}
+
+	// Clean up
+	free(train_images);
+	free(train_labels);
+	destroy_network(network);
+
+	return 0;
+}