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snapkitty-mlc / examples /mnist.c
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/* mnist.c — SnapKitty MLC: MNIST digit classifier example
*
* Architecture:
* Input (784) → W0(16×784) ReLU → W1(16×16) ReLU [+ skip] → W2(10×16) Softmax
* Loss: cross-entropy
* Init: Xavier uniform (Glorot)
* Train: SGD, 10 epochs, batch=50, lr=0.01
*
* Data: run examples/data_convert.py first to generate .mat files.
*
* Authors: Ahmad Ali Parr, Jessica L. Williams (SNAPKITTYWEST)
* Source: SNAPKITTYAGENT9NOVA/MLC
* License: BSL-1.1 / AGPL-3.0 / MPL-2.0
*/
#define _CRT_SECURE_NO_WARNINGS
#include <stdio.h>
#include <string.h>
#include <math.h>
#include "../include/sk_defs.h"
#include "../include/sk_arena.h"
#include "../include/sk_random.h"
#include "../include/sk_matrix.h"
#include "../include/sk_model.h"
/* ── ANSI terminal: render digit as coloured blocks ──────────────── */
static void draw_mnist_digit(const f32* data) {
for (u32 y = 0; y < 28; y++) {
for (u32 x = 0; x < 28; x++) {
f32 num = data[x + y * 28];
u32 col = 232 + (u32)(num * 23);
printf("\x1b[48;5;%dm ", col);
}
printf("\x1b[0m\n");
}
printf("\x1b[0m");
}
/* ── Build the MNIST model ────────────────────────────────────────── */
static void create_mnist_model(sk_arena* arena, sk_model* model) {
/* Xavier uniform init bounds */
f32 b0 = sqrtf(6.0f / (784 + 16));
f32 b1 = sqrtf(6.0f / ( 16 + 16));
f32 b2 = sqrtf(6.0f / ( 16 + 10));
sk_model_var* input = sk_mv_create(arena, model, 784, 1, SK_MV_INPUT);
sk_model_var* W0 = sk_mv_create(arena, model, 16, 784, SK_MV_REQUIRES_GRAD | SK_MV_PARAMETER);
sk_model_var* W1 = sk_mv_create(arena, model, 16, 16, SK_MV_REQUIRES_GRAD | SK_MV_PARAMETER);
sk_model_var* W2 = sk_mv_create(arena, model, 10, 16, SK_MV_REQUIRES_GRAD | SK_MV_PARAMETER);
sk_mat_fill_rand(W0->val, -b0, b0);
sk_mat_fill_rand(W1->val, -b1, b1);
sk_mat_fill_rand(W2->val, -b2, b2);
sk_model_var* b0v = sk_mv_create(arena, model, 16, 1, SK_MV_REQUIRES_GRAD | SK_MV_PARAMETER);
sk_model_var* b1v = sk_mv_create(arena, model, 16, 1, SK_MV_REQUIRES_GRAD | SK_MV_PARAMETER);
sk_model_var* b2v = sk_mv_create(arena, model, 10, 1, SK_MV_REQUIRES_GRAD | SK_MV_PARAMETER);
/* Layer 0: W0 × input + b0 → ReLU */
sk_model_var* z0 = sk_mv_matmul(arena, model, W0, input, 0);
sk_model_var* z0b = sk_mv_add(arena, model, z0, b0v, 0);
sk_model_var* a0 = sk_mv_relu(arena, model, z0b, 0);
/* Layer 1: W1 × a0 + b1 → ReLU + skip */
sk_model_var* z1 = sk_mv_matmul(arena, model, W1, a0, 0);
sk_model_var* z1b = sk_mv_add(arena, model, z1, b1v, 0);
sk_model_var* z1r = sk_mv_relu(arena, model, z1b, 0);
sk_model_var* a1 = sk_mv_add(arena, model, a0, z1r, 0); /* skip connection */
/* Layer 2: W2 × a1 + b2 → Softmax */
sk_model_var* z2 = sk_mv_matmul(arena, model, W2, a1, 0);
sk_model_var* z2b = sk_mv_add(arena, model, z2, b2v, 0);
sk_model_var* out = sk_mv_softmax(arena, model, z2b, SK_MV_OUTPUT);
/* Labels + loss */
sk_model_var* y = sk_mv_create(arena, model, 10, 1, SK_MV_DESIRED_OUTPUT);
sk_model_var* cost = sk_mv_cross_entropy(arena, model, y, out, SK_MV_COST);
(void)cost;
}
/* ── main ─────────────────────────────────────────────────────────── */
int main(void) {
sk_arena* perm = sk_arena_create(GiB(1), MiB(1));
sk_matrix* train_images = sk_mat_load(perm, 60000, 784, "train_images.mat");
sk_matrix* test_images = sk_mat_load(perm, 10000, 784, "test_images.mat");
sk_matrix* train_labels = sk_mat_create(perm, 60000, 10);
sk_matrix* test_labels = sk_mat_create(perm, 10000, 10);
{
sk_matrix* tl = sk_mat_load(perm, 60000, 1, "train_labels.mat");
sk_matrix* tst = sk_mat_load(perm, 10000, 1, "test_labels.mat");
for (u32 i = 0; i < 60000; i++)
train_labels->data[i * 10 + (u32)tl->data[i]] = 1.0f;
for (u32 i = 0; i < 10000; i++)
test_labels->data[i * 10 + (u32)tst->data[i]] = 1.0f;
}
draw_mnist_digit(test_images->data);
printf("Label: ");
for (u32 i = 0; i < 10; i++) printf("%.0f ", test_labels->data[i]);
printf("\n\n");
sk_model* model = sk_model_create(perm);
create_mnist_model(perm, model);
sk_model_compile(perm, model);
memcpy(model->input->val->data, test_images->data, sizeof(f32) * 784);
sk_model_feedforward(model);
printf("Pre-training: ");
for (u32 i = 0; i < 10; i++) printf("%.2f ", model->output->val->data[i]);
printf("\n");
sk_training_desc desc = {
.train_images = train_images,
.train_labels = train_labels,
.test_images = test_images,
.test_labels = test_labels,
.epochs = 10,
.batch_size = 50,
.learning_rate = 0.01f,
};
sk_model_train(model, &desc);
memcpy(model->input->val->data, test_images->data, sizeof(f32) * 784);
sk_model_feedforward(model);
printf("Post-training: ");
for (u32 i = 0; i < 10; i++) printf("%.4f ", model->output->val->data[i]);
printf("\nPredicted: %llu\n", (unsigned long long)sk_mat_argmax(model->output->val));
sk_arena_destroy(perm);
return 0;
}