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revised autokem model

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This commit is contained in:
minjaesong
2026-03-08 20:34:45 +09:00
parent 39603d897b
commit 244371aa9d
8 changed files with 774 additions and 235 deletions
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50
Autokem/train.c
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@@ -128,12 +128,8 @@ static void save_weights(Network *net, Network *best) {
copy_tensor_data(best->conv2.bias, net->conv2.bias);
copy_tensor_data(best->fc1.weight, net->fc1.weight);
copy_tensor_data(best->fc1.bias, net->fc1.bias);
copy_tensor_data(best->head_shape.weight, net->head_shape.weight);
copy_tensor_data(best->head_shape.bias, net->head_shape.bias);
copy_tensor_data(best->head_ytype.weight, net->head_ytype.weight);
copy_tensor_data(best->head_ytype.bias, net->head_ytype.bias);
copy_tensor_data(best->head_lowheight.weight, net->head_lowheight.weight);
copy_tensor_data(best->head_lowheight.bias, net->head_lowheight.bias);
copy_tensor_data(best->output.weight, net->output.weight);
copy_tensor_data(best->output.bias, net->output.bias);
}
/* ---- Training ---- */
@@ -247,19 +243,15 @@ int train_model(void) {
int ishape[] = {bs, 1, 20, 15};
Tensor *input = tensor_alloc(4, ishape);
int sshape[] = {bs, 10};
Tensor *tgt_shape = tensor_alloc(2, sshape);
int yshape[] = {bs, 1};
Tensor *tgt_ytype = tensor_alloc(2, yshape);
Tensor *tgt_lh = tensor_alloc(2, yshape);
int tshape[] = {bs, 12};
Tensor *target = tensor_alloc(2, tshape);
for (int i = 0; i < bs; i++) {
Sample *s = &all_samples[indices[start + i]];
memcpy(input->data + i * 300, s->input, 300 * sizeof(float));
memcpy(tgt_shape->data + i * 10, s->shape, 10 * sizeof(float));
tgt_ytype->data[i] = s->ytype;
tgt_lh->data[i] = s->lowheight;
memcpy(target->data + i * 12, s->shape, 10 * sizeof(float));
target->data[i * 12 + 10] = s->ytype;
target->data[i * 12 + 11] = s->lowheight;
}
/* Forward */
@@ -267,21 +259,19 @@ int train_model(void) {
network_forward(net, input, 1);
/* Loss */
float loss = network_bce_loss(net, tgt_shape, tgt_ytype, tgt_lh);
float loss = network_bce_loss(net, target);
train_loss += loss;
n_batches++;
/* Backward */
network_backward(net, tgt_shape, tgt_ytype, tgt_lh);
network_backward(net, target);
/* Adam step */
adam_t++;
network_adam_step(net, lr, beta1, beta2, eps, adam_t);
tensor_free(input);
tensor_free(tgt_shape);
tensor_free(tgt_ytype);
tensor_free(tgt_lh);
tensor_free(target);
}
train_loss /= (float)n_batches;
@@ -295,29 +285,23 @@ int train_model(void) {
int ishape[] = {bs, 1, 20, 15};
Tensor *input = tensor_alloc(4, ishape);
int sshape[] = {bs, 10};
Tensor *tgt_shape = tensor_alloc(2, sshape);
int yshape[] = {bs, 1};
Tensor *tgt_ytype = tensor_alloc(2, yshape);
Tensor *tgt_lh = tensor_alloc(2, yshape);
int tshape[] = {bs, 12};
Tensor *target = tensor_alloc(2, tshape);
for (int i = 0; i < bs; i++) {
Sample *s = &all_samples[indices[n_train + start + i]];
memcpy(input->data + i * 300, s->input, 300 * sizeof(float));
memcpy(tgt_shape->data + i * 10, s->shape, 10 * sizeof(float));
tgt_ytype->data[i] = s->ytype;
tgt_lh->data[i] = s->lowheight;
memcpy(target->data + i * 12, s->shape, 10 * sizeof(float));
target->data[i * 12 + 10] = s->ytype;
target->data[i * 12 + 11] = s->lowheight;
}
network_forward(net, input, 0);
val_loss += network_bce_loss(net, tgt_shape, tgt_ytype, tgt_lh);
val_loss += network_bce_loss(net, target);
val_batches++;
tensor_free(input);
tensor_free(tgt_shape);
tensor_free(tgt_ytype);
tensor_free(tgt_lh);
tensor_free(target);
}
val_loss /= (float)val_batches;