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#include "dynnet.h"
#include "ode_solver.h"
#include "adjoint.h"
#include "adam.h"
#include "train.h"
#include "spiral.h"
#include "tests.h"
#include "test_cnf.h"
#include "cnf_train.h"
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
int main(void) {
RNG r = rng_init(42);
/* --- sanity checks --- */
test_ode_solver();
test_dynnet_gradients(&r);
test_adjoint_gradients(&r);
test_multi_obs_adjoint(&r);
test_training(&r);
printf("\n--- DynNet tests ---\n");
test_dynnet_deep_gradients(&r);
test_dynnet_residual_gradients(&r);
test_dynnet_layernorm_gradients(&r);
test_dynnet_time_inject_gradients(&r);
test_dynnet_swish_gradients(&r);
test_dynnet_softplus_gradients(&r);
test_adjoint_deep(&r);
printf("\n--- CNF tests ---\n");
test_cnf_trace(&r);
test_cnf_invertibility(&r);
test_cnf_gradients(&r);
test_cnf_training(&r);
cnf_train_demo(&r);
printf("\n--- Training demo (spiral) ---\n\n");
r = rng_init((uint64_t)time(NULL));
const double t0 = 0.0, t1 = 1.5;
const double noise_std = 0.1;
const double atol_train = 1e-3, rtol_train = 1e-3;
const double atol_eval = 1e-5, rtol_eval = 1e-5;
const int TRAIN_N = 200, TEST_N = 50;
const int ITERS = 500, BATCH = 16, LOG_EVERY = 25;
Dataset train_ds = generate_spiral_dataset(TRAIN_N, t0, t1, noise_std, &r);
Dataset test_ds = generate_spiral_dataset(TEST_N, t0, t1, noise_std, &r);
const int D = 2, H = 32;
DynNet *net = dynnet_create(D);
dynnet_add_time_concat(net);
dynnet_add_linear(net, H);
dynnet_add_tanh(net);
dynnet_add_linear(net, D);
dynnet_finalize(net);
int nparams = net->total_params;
double *theta = vec_alloc(nparams);
dynnet_init_params(net, theta, &r);
Adam adam = adam_init(nparams, 1e-3, 0.9, 0.999, 1e-8);
printf("%-6s %-12s %-12s %-10s %-10s",
"Iter", "Train Loss", "Test Loss", "Fwd NFE", "Bwd NFE");
printf("\n--------------------------------------------------\n");
const double **batch_z0 = (const double **)xmalloc(BATCH * sizeof(double *));
const double **batch_tgt = (const double **)xmalloc(BATCH * sizeof(double *));
for (int iter = 1; iter <= ITERS; iter++) {
for (int b = 0; b < BATCH; b++) {
int idx = (int)(rng_next(&r) % (uint64_t)TRAIN_N);
batch_z0[b] = train_ds.z0[idx];
batch_tgt[b] = train_ds.target[idx];
}
TrainStepResult res = train_step(net, theta, batch_z0, batch_tgt,
t0, t1, BATCH, &adam,
atol_train, rtol_train, 10);
if (iter % LOG_EVERY == 0) {
double test_loss = evaluate(net, theta, &test_ds,
t0, t1, atol_eval, rtol_eval);
printf("%-6d %-12.6f %-12.6f %-10d %-10d\n",
iter, res.loss, test_loss,
res.nfe_fwd, res.nfe_bwd);
fflush(stdout);
}
}
free(batch_z0);
free(batch_tgt);
printf("\n--------------------------------------------------\n");
double final_test_loss = evaluate(net, theta, &test_ds,
t0, t1, atol_eval, rtol_eval);
printf("Final test loss : %.6f\n", final_test_loss);
printf("Total parameters: %d\n", nparams);
printf("\nSample predictions:\n");
double *ws = vec_alloc(net->total_workspace);
double *neg_a = vec_alloc(D);
AdjointCtx ac = { net, theta, ws, neg_a };
for (int s = 0; s < 5; s++) {
int idx = (int)(rng_next(&r) % (uint64_t)TEST_N);
ODEResult fwd = ode_solve(neural_ode_rhs,
test_ds.z0[idx], t0, t1,
NULL, D, atol_eval, rtol_eval, &ac);
printf(" z0=(%.4f, %.4f) predicted=(%.4f, %.4f) target=(%.4f, %.4f)\n",
test_ds.z0[idx][0], test_ds.z0[idx][1],
fwd.y[0], fwd.y[1],
test_ds.target[idx][0], test_ds.target[idx][1]);
free(fwd.y);
}
free(ws);
free(neg_a);
free(theta);
adam_free(&adam);
dynnet_free(net);
dataset_free(&train_ds);
dataset_free(&test_ds);
return 0;
}
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