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revised autokem model

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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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34
Autokem/nn.h
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@@ -20,6 +20,7 @@ void tensor_free(Tensor *t);
typedef struct {
int in_ch, out_ch, kh, kw;
int pad_h, pad_w;
Tensor *weight; /* [out_ch, in_ch, kh, kw] */
Tensor *bias; /* [out_ch] */
Tensor *grad_weight;
@@ -45,35 +46,32 @@ typedef struct {
/* ---- Network ---- */
typedef struct {
Conv2D conv1; /* 1->12, 3x3 */
Conv2D conv2; /* 12->16, 3x3 */
Dense fc1; /* 4800->24 */
Dense head_shape; /* 24->10 (bits A-H, J, K) */
Dense head_ytype; /* 24->1 */
Dense head_lowheight;/* 24->1 */
Conv2D conv1; /* 1->32, 7x7, pad=1 */
Conv2D conv2; /* 32->64, 7x7, pad=1 */
Dense fc1; /* 64->256 */
Dense output; /* 256->12 (10 shape + 1 ytype + 1 lowheight) */
/* activation caches (allocated per forward) */
Tensor *act_conv1;
Tensor *act_relu1;
Tensor *act_silu1;
Tensor *act_conv2;
Tensor *act_relu2;
Tensor *act_flat;
Tensor *act_silu2;
Tensor *act_pool; /* global average pool output */
Tensor *act_fc1;
Tensor *act_relu3;
Tensor *out_shape;
Tensor *out_ytype;
Tensor *out_lowheight;
Tensor *act_silu3;
Tensor *act_logits; /* pre-sigmoid */
Tensor *out_all; /* sigmoid output [batch, 12] */
} Network;
/* Init / free */
Network *network_create(void);
void network_free(Network *net);
/* Forward pass. input: [batch, 1, 20, 15]. Outputs stored in net->out_* */
/* Forward pass. input: [batch, 1, 20, 15]. Output stored in net->out_all */
void network_forward(Network *net, Tensor *input, int training);
/* Backward pass. targets: shape[batch,10], ytype[batch,1], lowheight[batch,1] */
void network_backward(Network *net, Tensor *target_shape, Tensor *target_ytype, Tensor *target_lowheight);
/* Backward pass. target: [batch, 12] */
void network_backward(Network *net, Tensor *target);
/* Adam update step */
void network_adam_step(Network *net, float lr, float beta1, float beta2, float eps, int t);
@@ -81,8 +79,8 @@ void network_adam_step(Network *net, float lr, float beta1, float beta2, float e
/* Zero all gradients */
void network_zero_grad(Network *net);
/* Compute BCE loss (sum of all heads) */
float network_bce_loss(Network *net, Tensor *target_shape, Tensor *target_ytype, Tensor *target_lowheight);
/* Compute BCE loss */
float network_bce_loss(Network *net, Tensor *target);
/* Single-sample inference: input float[300], output float[12] (A-H,J,K,ytype,lowheight) */
void network_infer(Network *net, const float *input300, float *output12);