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Fix GRU, Add LSTM

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This commit is contained in:
Yao Lu 2017-06-06 16:50:19 -07:00
parent d528cbdb7b
commit e9f3b79776
8 changed files with 696 additions and 327 deletions
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4
Makefile
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@ -1,4 +1,4 @@
GPU=0 GPU=1
CUDNN=0 CUDNN=0
OPENCV=0 OPENCV=0
DEBUG=0 DEBUG=0
@ -51,7 +51,7 @@ CFLAGS+= -DCUDNN
LDFLAGS+= -lcudnn LDFLAGS+= -lcudnn
endif endif
OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o detection_layer.o route_layer.o box.o normalization_layer.o avgpool_layer.o layer.o local_layer.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o crnn_layer.o demo.o batchnorm_layer.o region_layer.o reorg_layer.o tree.o OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o detection_layer.o route_layer.o box.o normalization_layer.o avgpool_layer.o layer.o local_layer.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o crnn_layer.o demo.o batchnorm_layer.o region_layer.o reorg_layer.o tree.o lstm_layer.o
EXECOBJA=captcha.o lsd.o super.o voxel.o art.o tag.o cifar.o go.o rnn.o rnn_vid.o compare.o segmenter.o regressor.o classifier.o coco.o dice.o yolo.o detector.o writing.o nightmare.o swag.o darknet.o EXECOBJA=captcha.o lsd.o super.o voxel.o art.o tag.o cifar.o go.o rnn.o rnn_vid.o compare.o segmenter.o regressor.o classifier.o coco.o dice.o yolo.o detector.o writing.o nightmare.o swag.o darknet.o
ifeq ($(GPU), 1) ifeq ($(GPU), 1)
LDFLAGS+= -lstdc++ LDFLAGS+= -lstdc++

31
include/darknet.h
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@ -63,6 +63,7 @@ typedef enum {
ACTIVE, ACTIVE,
RNN, RNN,
GRU, GRU,
LSTM,
CRNN, CRNN,
BATCHNORM, BATCHNORM,
NETWORK, NETWORK,
@ -252,6 +253,21 @@ struct layer{
struct layer *input_h_layer; struct layer *input_h_layer;
struct layer *state_h_layer; struct layer *state_h_layer;
struct layer *wz;
struct layer *uz;
struct layer *wr;
struct layer *ur;
struct layer *wh;
struct layer *uh;
struct layer *uo;
struct layer *wo;
struct layer *uf;
struct layer *wf;
struct layer *ui;
struct layer *wi;
struct layer *ug;
struct layer *wg;
tree *softmax_tree; tree *softmax_tree;
size_t workspace_size; size_t workspace_size;
@ -263,6 +279,21 @@ struct layer{
float *r_gpu; float *r_gpu;
float *h_gpu; float *h_gpu;
float *temp_gpu;
float *temp2_gpu;
float *temp3_gpu;
float *dh_gpu;
float *hh_gpu;
float *prev_cell_gpu;
float *cell_gpu;
float *f_gpu;
float *i_gpu;
float *g_gpu;
float *o_gpu;
float *c_gpu;
float *dc_gpu;
float *m_gpu; float *m_gpu;
float *v_gpu; float *v_gpu;
float *bias_m_gpu; float *bias_m_gpu;

420
src/gru_layer.c
View File

@ -36,63 +36,41 @@ layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_no
l.steps = steps; l.steps = steps;
l.inputs = inputs; l.inputs = inputs;
l.input_z_layer = malloc(sizeof(layer)); l.wz = malloc(sizeof(layer));
fprintf(stderr, "\t\t"); fprintf(stderr, "\t\t");
*(l.input_z_layer) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize); *(l.wz) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize);
l.input_z_layer->batch = batch; l.wz->batch = batch;
l.state_z_layer = malloc(sizeof(layer)); l.uz = malloc(sizeof(layer));
fprintf(stderr, "\t\t"); fprintf(stderr, "\t\t");
*(l.state_z_layer) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize); *(l.uz) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize);
l.state_z_layer->batch = batch; l.uz->batch = batch;
l.wr = malloc(sizeof(layer));
l.input_r_layer = malloc(sizeof(layer));
fprintf(stderr, "\t\t"); fprintf(stderr, "\t\t");
*(l.input_r_layer) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize); *(l.wr) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize);
l.input_r_layer->batch = batch; l.wr->batch = batch;
l.state_r_layer = malloc(sizeof(layer)); l.ur = malloc(sizeof(layer));
fprintf(stderr, "\t\t"); fprintf(stderr, "\t\t");
*(l.state_r_layer) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize); *(l.ur) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize);
l.state_r_layer->batch = batch; l.ur->batch = batch;
l.wh = malloc(sizeof(layer));
l.input_h_layer = malloc(sizeof(layer));
fprintf(stderr, "\t\t"); fprintf(stderr, "\t\t");
*(l.input_h_layer) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize); *(l.wh) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize);
l.input_h_layer->batch = batch; l.wh->batch = batch;
l.state_h_layer = malloc(sizeof(layer)); l.uh = malloc(sizeof(layer));
fprintf(stderr, "\t\t"); fprintf(stderr, "\t\t");
*(l.state_h_layer) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize); *(l.uh) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize);
l.state_h_layer->batch = batch; l.uh->batch = batch;
#ifdef CUDNN
cudnnSetTensor4dDescriptor(l.input_z_layer->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.input_z_layer->out_c, l.input_z_layer->out_h, l.input_z_layer->out_w);
cudnnSetTensor4dDescriptor(l.input_h_layer->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.input_h_layer->out_c, l.input_h_layer->out_h, l.input_h_layer->out_w);
cudnnSetTensor4dDescriptor(l.input_r_layer->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.input_r_layer->out_c, l.input_r_layer->out_h, l.input_r_layer->out_w);
cudnnSetTensor4dDescriptor(l.state_z_layer->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.state_z_layer->out_c, l.state_z_layer->out_h, l.state_z_layer->out_w);
cudnnSetTensor4dDescriptor(l.state_h_layer->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.state_h_layer->out_c, l.state_h_layer->out_h, l.state_h_layer->out_w);
cudnnSetTensor4dDescriptor(l.state_r_layer->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.state_r_layer->out_c, l.state_r_layer->out_h, l.state_r_layer->out_w);
#endif
l.batch_normalize = batch_normalize; l.batch_normalize = batch_normalize;
l.outputs = outputs; l.outputs = outputs;
l.output = calloc(outputs*batch*steps, sizeof(float));
l.delta = calloc(outputs*batch*steps, sizeof(float));
l.state = calloc(outputs*batch, sizeof(float));
l.prev_state = calloc(outputs*batch, sizeof(float));
l.forgot_state = calloc(outputs*batch, sizeof(float));
l.forgot_delta = calloc(outputs*batch, sizeof(float));
l.r_cpu = calloc(outputs*batch, sizeof(float)); l.output = calloc(outputs*batch*steps, sizeof(float));
l.z_cpu = calloc(outputs*batch, sizeof(float)); l.state = calloc(outputs*batch, sizeof(float));
l.h_cpu = calloc(outputs*batch, sizeof(float));
l.forward = forward_gru_layer; l.forward = forward_gru_layer;
l.backward = backward_gru_layer; l.backward = backward_gru_layer;
@ -103,104 +81,31 @@ layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_no
l.backward_gpu = backward_gru_layer_gpu; l.backward_gpu = backward_gru_layer_gpu;
l.update_gpu = update_gru_layer_gpu; l.update_gpu = update_gru_layer_gpu;
l.forgot_state_gpu = cuda_make_array(l.output, batch*outputs); l.prev_state_gpu = cuda_make_array(0, batch*outputs);
l.forgot_delta_gpu = cuda_make_array(l.output, batch*outputs); l.output_gpu = cuda_make_array(0, batch*outputs*steps);
l.prev_state_gpu = cuda_make_array(l.output, batch*outputs); l.delta_gpu = cuda_make_array(0, batch*outputs*steps);
l.state_gpu = cuda_make_array(l.output, batch*outputs);
l.output_gpu = cuda_make_array(l.output, batch*outputs*steps);
l.delta_gpu = cuda_make_array(l.delta, batch*outputs*steps);
l.r_gpu = cuda_make_array(l.output_gpu, batch*outputs);
l.z_gpu = cuda_make_array(l.output_gpu, batch*outputs);
l.h_gpu = cuda_make_array(l.output_gpu, batch*outputs);
#endif
l.r_gpu = cuda_make_array(l.output, batch*outputs);
l.z_gpu = cuda_make_array(l.output, batch*outputs);
l.hh_gpu = cuda_make_array(l.output, batch*outputs);
l.h_gpu = cuda_make_array(l.output, batch*outputs);
l.temp_gpu = cuda_make_array(l.output, batch*outputs);
l.temp2_gpu = cuda_make_array(l.output, batch*outputs);
l.temp3_gpu = cuda_make_array(l.output, batch*outputs);
l.dh_gpu = cuda_make_array(l.output, batch*outputs);
#endif
return l; return l;
} }
void update_gru_layer(layer l, int batch, float learning_rate, float momentum, float decay) void update_gru_layer(layer l, int batch, float learning_rate, float momentum, float decay)
{ {
update_connected_layer(*(l.input_layer), batch, learning_rate, momentum, decay);
update_connected_layer(*(l.self_layer), batch, learning_rate, momentum, decay);
update_connected_layer(*(l.output_layer), batch, learning_rate, momentum, decay);
} }
void forward_gru_layer(layer l, network net) void forward_gru_layer(layer l, network state)
{ {
network s = net;
s.train = net.train;
int i;
layer input_z_layer = *(l.input_z_layer);
layer input_r_layer = *(l.input_r_layer);
layer input_h_layer = *(l.input_h_layer);
layer state_z_layer = *(l.state_z_layer);
layer state_r_layer = *(l.state_r_layer);
layer state_h_layer = *(l.state_h_layer);
fill_cpu(l.outputs * l.batch * l.steps, 0, input_z_layer.delta, 1);
fill_cpu(l.outputs * l.batch * l.steps, 0, input_r_layer.delta, 1);
fill_cpu(l.outputs * l.batch * l.steps, 0, input_h_layer.delta, 1);
fill_cpu(l.outputs * l.batch * l.steps, 0, state_z_layer.delta, 1);
fill_cpu(l.outputs * l.batch * l.steps, 0, state_r_layer.delta, 1);
fill_cpu(l.outputs * l.batch * l.steps, 0, state_h_layer.delta, 1);
if(net.train) {
fill_cpu(l.outputs * l.batch * l.steps, 0, l.delta, 1);
copy_cpu(l.outputs*l.batch, l.state, 1, l.prev_state, 1);
} }
for (i = 0; i < l.steps; ++i) { void backward_gru_layer(layer l, network state)
s.input = l.state;
forward_connected_layer(state_z_layer, s);
forward_connected_layer(state_r_layer, s);
s.input = net.input;
forward_connected_layer(input_z_layer, s);
forward_connected_layer(input_r_layer, s);
forward_connected_layer(input_h_layer, s);
copy_cpu(l.outputs*l.batch, input_z_layer.output, 1, l.z_cpu, 1);
axpy_cpu(l.outputs*l.batch, 1, state_z_layer.output, 1, l.z_cpu, 1);
copy_cpu(l.outputs*l.batch, input_r_layer.output, 1, l.r_cpu, 1);
axpy_cpu(l.outputs*l.batch, 1, state_r_layer.output, 1, l.r_cpu, 1);
activate_array(l.z_cpu, l.outputs*l.batch, LOGISTIC);
activate_array(l.r_cpu, l.outputs*l.batch, LOGISTIC);
copy_cpu(l.outputs*l.batch, l.state, 1, l.forgot_state, 1);
mul_cpu(l.outputs*l.batch, l.r_cpu, 1, l.forgot_state, 1);
s.input = l.forgot_state;
forward_connected_layer(state_h_layer, s);
copy_cpu(l.outputs*l.batch, input_h_layer.output, 1, l.h_cpu, 1);
axpy_cpu(l.outputs*l.batch, 1, state_h_layer.output, 1, l.h_cpu, 1);
#ifdef USET
activate_array(l.h_cpu, l.outputs*l.batch, TANH);
#else
activate_array(l.h_cpu, l.outputs*l.batch, LOGISTIC);
#endif
weighted_sum_cpu(l.state, l.h_cpu, l.z_cpu, l.outputs*l.batch, l.output);
copy_cpu(l.outputs*l.batch, l.output, 1, l.state, 1);
net.input += l.inputs*l.batch;
l.output += l.outputs*l.batch;
increment_layer(&input_z_layer, 1);
increment_layer(&input_r_layer, 1);
increment_layer(&input_h_layer, 1);
increment_layer(&state_z_layer, 1);
increment_layer(&state_r_layer, 1);
increment_layer(&state_h_layer, 1);
}
}
void backward_gru_layer(layer l, network net)
{ {
} }
@ -216,189 +121,202 @@ void push_gru_layer(layer l)
void update_gru_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay) void update_gru_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay)
{ {
update_connected_layer_gpu(*(l.input_r_layer), batch, learning_rate, momentum, decay); update_connected_layer_gpu(*(l.wr), batch, learning_rate, momentum, decay);
update_connected_layer_gpu(*(l.input_z_layer), batch, learning_rate, momentum, decay); update_connected_layer_gpu(*(l.wz), batch, learning_rate, momentum, decay);
update_connected_layer_gpu(*(l.input_h_layer), batch, learning_rate, momentum, decay); update_connected_layer_gpu(*(l.wh), batch, learning_rate, momentum, decay);
update_connected_layer_gpu(*(l.state_r_layer), batch, learning_rate, momentum, decay); update_connected_layer_gpu(*(l.ur), batch, learning_rate, momentum, decay);
update_connected_layer_gpu(*(l.state_z_layer), batch, learning_rate, momentum, decay); update_connected_layer_gpu(*(l.uz), batch, learning_rate, momentum, decay);
update_connected_layer_gpu(*(l.state_h_layer), batch, learning_rate, momentum, decay); update_connected_layer_gpu(*(l.uh), batch, learning_rate, momentum, decay);
} }
void forward_gru_layer_gpu(layer l, network net) void forward_gru_layer_gpu(layer l, network state)
{ {
network s = net; network s = { 0 };
s.train = net.train; s.train = state.train;
int i; int i;
layer input_z_layer = *(l.input_z_layer); layer wz = *(l.wz);
layer input_r_layer = *(l.input_r_layer); layer wr = *(l.wr);
layer input_h_layer = *(l.input_h_layer); layer wh = *(l.wh);
layer state_z_layer = *(l.state_z_layer); layer uz = *(l.uz);
layer state_r_layer = *(l.state_r_layer); layer ur = *(l.ur);
layer state_h_layer = *(l.state_h_layer); layer uh = *(l.uh);
fill_ongpu(l.outputs * l.batch * l.steps, 0, input_z_layer.delta_gpu, 1); fill_ongpu(l.outputs * l.batch * l.steps, 0, wz.delta_gpu, 1);
fill_ongpu(l.outputs * l.batch * l.steps, 0, input_r_layer.delta_gpu, 1); fill_ongpu(l.outputs * l.batch * l.steps, 0, wr.delta_gpu, 1);
fill_ongpu(l.outputs * l.batch * l.steps, 0, input_h_layer.delta_gpu, 1); fill_ongpu(l.outputs * l.batch * l.steps, 0, wh.delta_gpu, 1);
fill_ongpu(l.outputs * l.batch * l.steps, 0, state_z_layer.delta_gpu, 1); fill_ongpu(l.outputs * l.batch * l.steps, 0, uz.delta_gpu, 1);
fill_ongpu(l.outputs * l.batch * l.steps, 0, state_r_layer.delta_gpu, 1); fill_ongpu(l.outputs * l.batch * l.steps, 0, ur.delta_gpu, 1);
fill_ongpu(l.outputs * l.batch * l.steps, 0, state_h_layer.delta_gpu, 1); fill_ongpu(l.outputs * l.batch * l.steps, 0, uh.delta_gpu, 1);
if(net.train) {
if (state.train) {
fill_ongpu(l.outputs * l.batch * l.steps, 0, l.delta_gpu, 1); fill_ongpu(l.outputs * l.batch * l.steps, 0, l.delta_gpu, 1);
copy_ongpu(l.outputs*l.batch, l.state_gpu, 1, l.prev_state_gpu, 1);
} }
for (i = 0; i < l.steps; ++i) { for (i = 0; i < l.steps; ++i) {
s.input_gpu = l.state_gpu; s.input = l.h_gpu;
forward_connected_layer_gpu(state_z_layer, s); forward_connected_layer_gpu(uz, s);
forward_connected_layer_gpu(state_r_layer, s); forward_connected_layer_gpu(ur, s);
s.input_gpu = net.input_gpu; s.input = state.input;
forward_connected_layer_gpu(input_z_layer, s); forward_connected_layer_gpu(wz, s);
forward_connected_layer_gpu(input_r_layer, s); forward_connected_layer_gpu(wr, s);
forward_connected_layer_gpu(input_h_layer, s); forward_connected_layer_gpu(wh, s);
copy_ongpu(l.outputs*l.batch, wz.output_gpu, 1, l.z_gpu, 1);
axpy_ongpu(l.outputs*l.batch, 1, uz.output_gpu, 1, l.z_gpu, 1);
copy_ongpu(l.outputs*l.batch, input_z_layer.output_gpu, 1, l.z_gpu, 1); copy_ongpu(l.outputs*l.batch, wr.output_gpu, 1, l.r_gpu, 1);
axpy_ongpu(l.outputs*l.batch, 1, state_z_layer.output_gpu, 1, l.z_gpu, 1); axpy_ongpu(l.outputs*l.batch, 1, ur.output_gpu, 1, l.r_gpu, 1);
copy_ongpu(l.outputs*l.batch, input_r_layer.output_gpu, 1, l.r_gpu, 1);
axpy_ongpu(l.outputs*l.batch, 1, state_r_layer.output_gpu, 1, l.r_gpu, 1);
activate_array_ongpu(l.z_gpu, l.outputs*l.batch, LOGISTIC); activate_array_ongpu(l.z_gpu, l.outputs*l.batch, LOGISTIC);
activate_array_ongpu(l.r_gpu, l.outputs*l.batch, LOGISTIC); activate_array_ongpu(l.r_gpu, l.outputs*l.batch, LOGISTIC);
copy_ongpu(l.outputs*l.batch, l.state_gpu, 1, l.forgot_state_gpu, 1); copy_ongpu(l.outputs*l.batch, l.h_gpu, 1, l.hh_gpu, 1);
mul_ongpu(l.outputs*l.batch, l.r_gpu, 1, l.forgot_state_gpu, 1); mul_ongpu(l.outputs*l.batch, l.r_gpu, 1, l.hh_gpu, 1);
s.input_gpu = l.forgot_state_gpu; s.input = l.hh_gpu;
forward_connected_layer_gpu(state_h_layer, s); forward_connected_layer_gpu(uh, s);
copy_ongpu(l.outputs*l.batch, input_h_layer.output_gpu, 1, l.h_gpu, 1); copy_ongpu(l.outputs*l.batch, wh.output_gpu, 1, l.hh_gpu, 1);
axpy_ongpu(l.outputs*l.batch, 1, state_h_layer.output_gpu, 1, l.h_gpu, 1); axpy_ongpu(l.outputs*l.batch, 1, uh.output_gpu, 1, l.hh_gpu, 1);
#ifdef USET activate_array_ongpu(l.hh_gpu, l.outputs*l.batch, TANH);
activate_array_ongpu(l.h_gpu, l.outputs*l.batch, TANH);
#else
activate_array_ongpu(l.h_gpu, l.outputs*l.batch, LOGISTIC);
#endif
weighted_sum_gpu(l.state_gpu, l.h_gpu, l.z_gpu, l.outputs*l.batch, l.output_gpu); weighted_sum_gpu(l.h_gpu, l.hh_gpu, l.z_gpu, l.outputs*l.batch, l.output_gpu);
//ht = z .* ht-1 + (1-z) .* hh
copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.h_gpu, 1);
copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.state_gpu, 1); state.input += l.inputs*l.batch;
net.input_gpu += l.inputs*l.batch;
l.output_gpu += l.outputs*l.batch; l.output_gpu += l.outputs*l.batch;
increment_layer(&input_z_layer, 1);
increment_layer(&input_r_layer, 1);
increment_layer(&input_h_layer, 1);
increment_layer(&state_z_layer, 1); increment_layer(&wz, 1);
increment_layer(&state_r_layer, 1); increment_layer(&wr, 1);
increment_layer(&state_h_layer, 1); increment_layer(&wh, 1);
increment_layer(&uz, 1);
increment_layer(&ur, 1);
increment_layer(&uh, 1);
} }
} }
void backward_gru_layer_gpu(layer l, network net) void backward_gru_layer_gpu(layer l, network state)
{ {
network s = net; network s = { 0 };
s.train = net.train; s.train = state.train;
int i; int i;
layer input_z_layer = *(l.input_z_layer); layer wz = *(l.wz);
layer input_r_layer = *(l.input_r_layer); layer wr = *(l.wr);
layer input_h_layer = *(l.input_h_layer); layer wh = *(l.wh);
layer state_z_layer = *(l.state_z_layer); layer uz = *(l.uz);
layer state_r_layer = *(l.state_r_layer); layer ur = *(l.ur);
layer state_h_layer = *(l.state_h_layer); layer uh = *(l.uh);
increment_layer(&input_z_layer, l.steps - 1); increment_layer(&wz, l.steps - 1);
increment_layer(&input_r_layer, l.steps - 1); increment_layer(&wr, l.steps - 1);
increment_layer(&input_h_layer, l.steps - 1); increment_layer(&wh, l.steps - 1);
increment_layer(&state_z_layer, l.steps - 1); increment_layer(&uz, l.steps - 1);
increment_layer(&state_r_layer, l.steps - 1); increment_layer(&ur, l.steps - 1);
increment_layer(&state_h_layer, l.steps - 1); increment_layer(&uh, l.steps - 1);
state.input += l.inputs*l.batch*(l.steps - 1);
if (state.delta) state.delta += l.inputs*l.batch*(l.steps - 1);
net.input_gpu += l.inputs*l.batch*(l.steps-1);
if(net.delta_gpu) net.delta_gpu += l.inputs*l.batch*(l.steps-1);
l.output_gpu += l.outputs*l.batch*(l.steps - 1); l.output_gpu += l.outputs*l.batch*(l.steps - 1);
l.delta_gpu += l.outputs*l.batch*(l.steps - 1); l.delta_gpu += l.outputs*l.batch*(l.steps - 1);
for (i = l.steps - 1; i >= 0; --i) { for (i = l.steps - 1; i >= 0; --i) {
if(i != 0) copy_ongpu(l.outputs*l.batch, l.output_gpu - l.outputs*l.batch, 1, l.prev_state_gpu, 1); if (i>0) copy_ongpu(l.outputs*l.batch, l.output_gpu - l.outputs*l.batch, 1, l.prev_state_gpu, 1);
float *prev_delta_gpu = (i == 0) ? 0 : l.delta_gpu - l.outputs*l.batch; l.dh_gpu = (i == 0) ? 0 : l.delta_gpu - l.outputs*l.batch;
copy_ongpu(l.outputs*l.batch, input_z_layer.output_gpu, 1, l.z_gpu, 1); copy_ongpu(l.outputs*l.batch, wz.output_gpu, 1, l.z_gpu, 1);
axpy_ongpu(l.outputs*l.batch, 1, state_z_layer.output_gpu, 1, l.z_gpu, 1); axpy_ongpu(l.outputs*l.batch, 1, uz.output_gpu, 1, l.z_gpu, 1);
copy_ongpu(l.outputs*l.batch, input_r_layer.output_gpu, 1, l.r_gpu, 1); copy_ongpu(l.outputs*l.batch, wr.output_gpu, 1, l.r_gpu, 1);
axpy_ongpu(l.outputs*l.batch, 1, state_r_layer.output_gpu, 1, l.r_gpu, 1); axpy_ongpu(l.outputs*l.batch, 1, ur.output_gpu, 1, l.r_gpu, 1);
activate_array_ongpu(l.z_gpu, l.outputs*l.batch, LOGISTIC); activate_array_ongpu(l.z_gpu, l.outputs*l.batch, LOGISTIC);
activate_array_ongpu(l.r_gpu, l.outputs*l.batch, LOGISTIC); activate_array_ongpu(l.r_gpu, l.outputs*l.batch, LOGISTIC);
copy_ongpu(l.outputs*l.batch, input_h_layer.output_gpu, 1, l.h_gpu, 1); copy_ongpu(l.outputs*l.batch, wh.output_gpu, 1, l.hh_gpu, 1);
axpy_ongpu(l.outputs*l.batch, 1, state_h_layer.output_gpu, 1, l.h_gpu, 1); axpy_ongpu(l.outputs*l.batch, 1, uh.output_gpu, 1, l.hh_gpu, 1);
#ifdef USET activate_array_ongpu(l.hh_gpu, l.outputs*l.batch, TANH);
activate_array_ongpu(l.h_gpu, l.outputs*l.batch, TANH);
#else
activate_array_ongpu(l.h_gpu, l.outputs*l.batch, LOGISTIC);
#endif
weighted_delta_gpu(l.prev_state_gpu, l.h_gpu, l.z_gpu, prev_delta_gpu, input_h_layer.delta_gpu, input_z_layer.delta_gpu, l.outputs*l.batch, l.delta_gpu); copy_ongpu(l.outputs*l.batch, l.delta_gpu, 1, l.temp3_gpu, 1);
#ifdef USET fill_ongpu(l.outputs*l.batch, 1, l.temp_gpu, 1);
gradient_array_ongpu(l.h_gpu, l.outputs*l.batch, TANH, input_h_layer.delta_gpu); axpy_ongpu(l.outputs*l.batch, -1, l.z_gpu, 1, l.temp_gpu, 1);
#else mul_ongpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp_gpu, 1);
gradient_array_ongpu(l.h_gpu, l.outputs*l.batch, LOGISTIC, input_h_layer.delta_gpu); gradient_array_ongpu(l.hh_gpu, l.outputs*l.batch, TANH, l.temp_gpu);
#endif
copy_ongpu(l.outputs*l.batch, input_h_layer.delta_gpu, 1, state_h_layer.delta_gpu, 1); copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wh.delta_gpu, 1);
s.input = state.input;
s.delta = state.delta;
backward_connected_layer_gpu(wh, s);
copy_ongpu(l.outputs*l.batch, l.prev_state_gpu, 1, l.forgot_state_gpu, 1); copy_ongpu(l.outputs*l.batch, l.prev_state_gpu, 1, l.temp2_gpu, 1);
mul_ongpu(l.outputs*l.batch, l.r_gpu, 1, l.forgot_state_gpu, 1); mul_ongpu(l.outputs*l.batch, l.r_gpu, 1, l.temp2_gpu, 1);
fill_ongpu(l.outputs*l.batch, 0, l.forgot_delta_gpu, 1);
s.input_gpu = l.forgot_state_gpu; copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, uh.delta_gpu, 1);
s.delta_gpu = l.forgot_delta_gpu; fill_ongpu(l.outputs*l.batch, 0, l.temp_gpu, 1);
s.input = l.temp2_gpu;
s.delta = l.temp_gpu;
backward_connected_layer_gpu(uh, s);
backward_connected_layer_gpu(state_h_layer, s); copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, l.temp2_gpu, 1);
if(prev_delta_gpu) mult_add_into_gpu(l.outputs*l.batch, l.forgot_delta_gpu, l.r_gpu, prev_delta_gpu); mul_ongpu(l.outputs*l.batch, l.prev_state_gpu, 1, l.temp2_gpu, 1);
mult_add_into_gpu(l.outputs*l.batch, l.forgot_delta_gpu, l.prev_state_gpu, input_r_layer.delta_gpu); gradient_array_ongpu(l.r_gpu, l.outputs*l.batch, LOGISTIC, l.temp2_gpu);
gradient_array_ongpu(l.r_gpu, l.outputs*l.batch, LOGISTIC, input_r_layer.delta_gpu); copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, wr.delta_gpu, 1);
copy_ongpu(l.outputs*l.batch, input_r_layer.delta_gpu, 1, state_r_layer.delta_gpu, 1); s.input = state.input;
s.delta = state.delta;
backward_connected_layer_gpu(wr, s);
gradient_array_ongpu(l.z_gpu, l.outputs*l.batch, LOGISTIC, input_z_layer.delta_gpu); copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, ur.delta_gpu, 1);
copy_ongpu(l.outputs*l.batch, input_z_layer.delta_gpu, 1, state_z_layer.delta_gpu, 1); s.input = l.prev_state_gpu;
s.delta = l.dh_gpu;
backward_connected_layer_gpu(ur, s);
s.input_gpu = l.prev_state_gpu; copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, l.temp2_gpu, 1);
s.delta_gpu = prev_delta_gpu; mul_ongpu(l.outputs*l.batch, l.r_gpu, 1, l.temp2_gpu, 1);
if (l.dh_gpu) axpy_ongpu(l.outputs*l.batch, 1, l.temp2_gpu, 1, l.dh_gpu, 1);
backward_connected_layer_gpu(state_r_layer, s); copy_ongpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp2_gpu, 1);
backward_connected_layer_gpu(state_z_layer, s); mul_ongpu(l.outputs*l.batch, l.z_gpu, 1, l.temp2_gpu, 1);
if (l.dh_gpu) axpy_ongpu(l.outputs*l.batch, 1, l.temp2_gpu, 1, l.dh_gpu, 1);
s.input_gpu = net.input_gpu; copy_ongpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp2_gpu, 1);
s.delta_gpu = net.delta_gpu; mul_ongpu(l.outputs*l.batch, l.prev_state_gpu, 1, l.temp3_gpu, 1);
mul_ongpu(l.outputs*l.batch, l.hh_gpu, 1, l.temp2_gpu, 1);
axpy_ongpu(l.outputs*l.batch, -1, l.temp2_gpu, 1, l.temp3_gpu, 1);
gradient_array_ongpu(l.z_gpu, l.outputs*l.batch, LOGISTIC, l.temp3_gpu);
backward_connected_layer_gpu(input_h_layer, s); copy_ongpu(l.outputs*l.batch, l.temp3_gpu, 1, wz.delta_gpu, 1);
backward_connected_layer_gpu(input_r_layer, s); s.input = state.input;
backward_connected_layer_gpu(input_z_layer, s); s.delta = state.delta;
backward_connected_layer_gpu(wz, s);
copy_ongpu(l.outputs*l.batch, l.temp3_gpu, 1, uz.delta_gpu, 1);
s.input = l.prev_state_gpu;
s.delta = l.dh_gpu;
backward_connected_layer_gpu(uz, s);
net.input_gpu -= l.inputs*l.batch; state.input -= l.inputs*l.batch;
if(net.delta_gpu) net.delta_gpu -= l.inputs*l.batch; if (state.delta) state.delta -= l.inputs*l.batch;
l.output_gpu -= l.outputs*l.batch; l.output_gpu -= l.outputs*l.batch;
l.delta_gpu -= l.outputs*l.batch; l.delta_gpu -= l.outputs*l.batch;
increment_layer(&input_z_layer, -1);
increment_layer(&input_r_layer, -1);
increment_layer(&input_h_layer, -1);
increment_layer(&state_z_layer, -1); increment_layer(&wz, -1);
increment_layer(&state_r_layer, -1); increment_layer(&wr, -1);
increment_layer(&state_h_layer, -1); increment_layer(&wh, -1);
increment_layer(&uz, -1);
increment_layer(&ur, -1);
increment_layer(&uh, -1);
} }
} }
#endif #endif

8
src/gru_layer.h
View File

@ -8,13 +8,13 @@
layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_normalize); layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_normalize);
void forward_gru_layer(layer l, network net); void forward_gru_layer(layer l, network state);
void backward_gru_layer(layer l, network net); void backward_gru_layer(layer l, network state);
void update_gru_layer(layer l, int batch, float learning_rate, float momentum, float decay); void update_gru_layer(layer l, int batch, float learning_rate, float momentum, float decay);
#ifdef GPU #ifdef GPU
void forward_gru_layer_gpu(layer l, network net); void forward_gru_layer_gpu(layer l, network state);
void backward_gru_layer_gpu(layer l, network net); void backward_gru_layer_gpu(layer l, network state);
void update_gru_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay); void update_gru_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay);
void push_gru_layer(layer l); void push_gru_layer(layer l);
void pull_gru_layer(layer l); void pull_gru_layer(layer l);

365
src/lstm_layer.c Normal file
View File

@ -0,0 +1,365 @@
#include "lstm_layer.h"
#include "connected_layer.h"
#include "utils.h"
#include "cuda.h"
#include "blas.h"
#include "gemm.h"
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
static void increment_layer(layer *l, int steps)
{
int num = l->outputs*l->batch*steps;
l->output += num;
l->delta += num;
l->x += num;
l->x_norm += num;
#ifdef GPU
l->output_gpu += num;
l->delta_gpu += num;
l->x_gpu += num;
l->x_norm_gpu += num;
#endif
}
layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_normalize)
{
fprintf(stderr, "LSTM Layer: %d inputs, %d outputs\n", inputs, outputs);
batch = batch / steps;
layer l = { 0 };
l.batch = batch;
l.type = LSTM;
l.steps = steps;
l.inputs = inputs;
l.uf = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.uf) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize);
l.uf->batch = batch;
l.wf = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.wf) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize);
l.wf->batch = batch;
l.ui = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.ui) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize);
l.ui->batch = batch;
l.wi = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.wi) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize);
l.wi->batch = batch;
l.ug = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.ug) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize);
l.ug->batch = batch;
l.wg = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.wg) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize);
l.wg->batch = batch;
l.uo = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.uo) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize);
l.uo->batch = batch;
l.wo = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.wo) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize);
l.wo->batch = batch;
l.batch_normalize = batch_normalize;
l.outputs = outputs;
l.output = calloc(outputs*batch*steps, sizeof(float));
l.state = calloc(outputs*batch, sizeof(float));
l.forward = forward_lstm_layer;
l.update = update_lstm_layer;
#ifdef GPU
l.forward_gpu = forward_lstm_layer_gpu;
l.backward_gpu = backward_lstm_layer_gpu;
l.update_gpu = update_lstm_layer_gpu;
l.prev_state_gpu = cuda_make_array(0, batch*outputs);
l.prev_cell_gpu = cuda_make_array(0, batch*outputs);
l.output_gpu = cuda_make_array(0, batch*outputs*steps);
l.cell_gpu = cuda_make_array(0, batch*outputs*steps);
l.delta_gpu = cuda_make_array(0, batch*l.outputs*steps);
l.f_gpu = cuda_make_array(l.output, batch*outputs);
l.i_gpu = cuda_make_array(l.output, batch*outputs);
l.g_gpu = cuda_make_array(l.output, batch*outputs);
l.o_gpu = cuda_make_array(l.output, batch*outputs);
l.c_gpu = cuda_make_array(l.output, batch*outputs);
l.h_gpu = cuda_make_array(l.output, batch*outputs);
l.temp_gpu = cuda_make_array(l.output, batch*outputs);
l.temp2_gpu = cuda_make_array(l.output, batch*outputs);
l.temp3_gpu = cuda_make_array(l.output, batch*outputs);
l.dc_gpu = cuda_make_array(l.output, batch*outputs);
l.dh_gpu = cuda_make_array(l.output, batch*outputs);
#endif
return l;
}
void update_lstm_layer(layer l, int batch, float learning_rate, float momentum, float decay)
{
}
void forward_lstm_layer(layer l, network state)
{
}
#ifdef GPU
void update_lstm_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay)
{
update_connected_layer_gpu(*(l.wf), batch, learning_rate, momentum, decay);
update_connected_layer_gpu(*(l.wi), batch, learning_rate, momentum, decay);
update_connected_layer_gpu(*(l.wg), batch, learning_rate, momentum, decay);
update_connected_layer_gpu(*(l.wo), batch, learning_rate, momentum, decay);
update_connected_layer_gpu(*(l.uf), batch, learning_rate, momentum, decay);
update_connected_layer_gpu(*(l.ui), batch, learning_rate, momentum, decay);
update_connected_layer_gpu(*(l.ug), batch, learning_rate, momentum, decay);
update_connected_layer_gpu(*(l.uo), batch, learning_rate, momentum, decay);
}
void forward_lstm_layer_gpu(layer l, network state)
{
network s = { 0 };
s.train = state.train;
int i;
layer wf = *(l.wf);
layer wi = *(l.wi);
layer wg = *(l.wg);
layer wo = *(l.wo);
layer uf = *(l.uf);
layer ui = *(l.ui);
layer ug = *(l.ug);
layer uo = *(l.uo);
fill_ongpu(l.outputs * l.batch * l.steps, 0, wf.delta_gpu, 1);
fill_ongpu(l.outputs * l.batch * l.steps, 0, wi.delta_gpu, 1);
fill_ongpu(l.outputs * l.batch * l.steps, 0, wg.delta_gpu, 1);
fill_ongpu(l.outputs * l.batch * l.steps, 0, wo.delta_gpu, 1);
fill_ongpu(l.outputs * l.batch * l.steps, 0, uf.delta_gpu, 1);
fill_ongpu(l.outputs * l.batch * l.steps, 0, ui.delta_gpu, 1);
fill_ongpu(l.outputs * l.batch * l.steps, 0, ug.delta_gpu, 1);
fill_ongpu(l.outputs * l.batch * l.steps, 0, uo.delta_gpu, 1);
if (state.train) {
fill_ongpu(l.outputs * l.batch * l.steps, 0, l.delta_gpu, 1);
}
for (i = 0; i < l.steps; ++i) {
s.input = l.h_gpu;
forward_connected_layer_gpu(wf, s);
forward_connected_layer_gpu(wi, s);
forward_connected_layer_gpu(wg, s);
forward_connected_layer_gpu(wo, s);
s.input = state.input;
forward_connected_layer_gpu(uf, s);
forward_connected_layer_gpu(ui, s);
forward_connected_layer_gpu(ug, s);
forward_connected_layer_gpu(uo, s);
copy_ongpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1);
axpy_ongpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1);
copy_ongpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1);
axpy_ongpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1);
copy_ongpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1);
axpy_ongpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1);
copy_ongpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1);
axpy_ongpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1);
activate_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC);
activate_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC);
activate_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH);
activate_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC);
copy_ongpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1);
mul_ongpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1);
mul_ongpu(l.outputs*l.batch, l.f_gpu, 1, l.c_gpu, 1);
axpy_ongpu(l.outputs*l.batch, 1, l.temp_gpu, 1, l.c_gpu, 1);
copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.h_gpu, 1);
activate_array_ongpu(l.h_gpu, l.outputs*l.batch, TANH);
mul_ongpu(l.outputs*l.batch, l.o_gpu, 1, l.h_gpu, 1);
copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.cell_gpu, 1);
copy_ongpu(l.outputs*l.batch, l.h_gpu, 1, l.output_gpu, 1);
state.input += l.inputs*l.batch;
l.output_gpu += l.outputs*l.batch;
l.cell_gpu += l.outputs*l.batch;
increment_layer(&wf, 1);
increment_layer(&wi, 1);
increment_layer(&wg, 1);
increment_layer(&wo, 1);
increment_layer(&uf, 1);
increment_layer(&ui, 1);
increment_layer(&ug, 1);
increment_layer(&uo, 1);
}
}
void backward_lstm_layer_gpu(layer l, network state)
{
network s = { 0 };
s.train = state.train;
int i;
layer wf = *(l.wf);
layer wi = *(l.wi);
layer wg = *(l.wg);
layer wo = *(l.wo);
layer uf = *(l.uf);
layer ui = *(l.ui);
layer ug = *(l.ug);
layer uo = *(l.uo);
increment_layer(&wf, l.steps - 1);
increment_layer(&wi, l.steps - 1);
increment_layer(&wg, l.steps - 1);
increment_layer(&wo, l.steps - 1);
increment_layer(&uf, l.steps - 1);
increment_layer(&ui, l.steps - 1);
increment_layer(&ug, l.steps - 1);
increment_layer(&uo, l.steps - 1);
state.input += l.inputs*l.batch*(l.steps - 1);
if (state.delta) state.delta += l.inputs*l.batch*(l.steps - 1);
l.output_gpu += l.outputs*l.batch*(l.steps - 1);
l.cell_gpu += l.outputs*l.batch*(l.steps - 1);
l.delta_gpu += l.outputs*l.batch*(l.steps - 1);
for (i = l.steps - 1; i >= 0; --i) {
if (i != 0) copy_ongpu(l.outputs*l.batch, l.cell_gpu - l.outputs*l.batch, 1, l.prev_cell_gpu, 1);
copy_ongpu(l.outputs*l.batch, l.cell_gpu, 1, l.c_gpu, 1);
if (i != 0) copy_ongpu(l.outputs*l.batch, l.output_gpu - l.outputs*l.batch, 1, l.prev_state_gpu, 1);
copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.h_gpu, 1);
l.dh_gpu = (i == 0) ? 0 : l.delta_gpu - l.outputs*l.batch;
copy_ongpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1);
axpy_ongpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1);
copy_ongpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1);
axpy_ongpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1);
copy_ongpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1);
axpy_ongpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1);
copy_ongpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1);
axpy_ongpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1);
activate_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC);
activate_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC);
activate_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH);
activate_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC);
copy_ongpu(l.outputs*l.batch, l.delta_gpu, 1, l.temp3_gpu, 1);
copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1);
activate_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH);
copy_ongpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp2_gpu, 1);
mul_ongpu(l.outputs*l.batch, l.o_gpu, 1, l.temp2_gpu, 1);
gradient_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH, l.temp2_gpu);
axpy_ongpu(l.outputs*l.batch, 1, l.dc_gpu, 1, l.temp2_gpu, 1);
copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1);
activate_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH);
mul_ongpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp_gpu, 1);
gradient_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu);
copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wo.delta_gpu, 1);
s.input = l.prev_state_gpu;
s.delta = l.dh_gpu;
backward_connected_layer_gpu(wo, s);
copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, uo.delta_gpu, 1);
s.input = state.input;
s.delta = state.delta;
backward_connected_layer_gpu(uo, s);
copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1);
mul_ongpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1);
gradient_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH, l.temp_gpu);
copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wg.delta_gpu, 1);
s.input = l.prev_state_gpu;
s.delta = l.dh_gpu;
backward_connected_layer_gpu(wg, s);
copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, ug.delta_gpu, 1);
s.input = state.input;
s.delta = state.delta;
backward_connected_layer_gpu(ug, s);
copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1);
mul_ongpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1);
gradient_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu);
copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wi.delta_gpu, 1);
s.input = l.prev_state_gpu;
s.delta = l.dh_gpu;
backward_connected_layer_gpu(wi, s);
copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, ui.delta_gpu, 1);
s.input = state.input;
s.delta = state.delta;
backward_connected_layer_gpu(ui, s);
copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1);
mul_ongpu(l.outputs*l.batch, l.prev_cell_gpu, 1, l.temp_gpu, 1);
gradient_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu);
copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wf.delta_gpu, 1);
s.input = l.prev_state_gpu;
s.delta = l.dh_gpu;
backward_connected_layer_gpu(wf, s);
copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, uf.delta_gpu, 1);
s.input = state.input;
s.delta = state.delta;
backward_connected_layer_gpu(uf, s);
copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1);
mul_ongpu(l.outputs*l.batch, l.f_gpu, 1, l.temp_gpu, 1);
copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, l.dc_gpu, 1);
state.input -= l.inputs*l.batch;
if (state.delta) state.delta -= l.inputs*l.batch;
l.output_gpu -= l.outputs*l.batch;
l.cell_gpu -= l.outputs*l.batch;
l.delta_gpu -= l.outputs*l.batch;
increment_layer(&wf, -1);
increment_layer(&wi, -1);
increment_layer(&wg, -1);
increment_layer(&wo, -1);
increment_layer(&uf, -1);
increment_layer(&ui, -1);
increment_layer(&ug, -1);
increment_layer(&uo, -1);
}
}
#endif

20
src/lstm_layer.h Normal file
View File

@ -0,0 +1,20 @@
#ifndef LSTM_LAYER_H
#define LSTM_LAYER_H
#include "activations.h"
#include "layer.h"
#include "network.h"
#define USET
layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_normalize);
void forward_lstm_layer(layer l, network state);
void update_lstm_layer(layer l, int batch, float learning, float momentum, float decay);
#ifdef GPU
void forward_lstm_layer_gpu(layer l, network state);
void backward_lstm_layer_gpu(layer l, network state);
void update_lstm_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay);
#endif
#endif

2
src/network.c
View File

@ -125,6 +125,8 @@ char *get_layer_string(LAYER_TYPE a)
return "rnn"; return "rnn";
case GRU: case GRU:
return "gru"; return "gru";
case LSTM:
return "lstm";
case CRNN: case CRNN:
return "crnn"; return "crnn";
case MAXPOOL: case MAXPOOL:

57
src/parser.c
View File

@ -29,6 +29,7 @@
#include "route_layer.h" #include "route_layer.h"
#include "shortcut_layer.h" #include "shortcut_layer.h"
#include "softmax_layer.h" #include "softmax_layer.h"
#include "lstm_layer.h"
#include "utils.h" #include "utils.h"
typedef struct{ typedef struct{
@ -56,6 +57,7 @@ LAYER_TYPE string_to_layer_type(char * type)
|| strcmp(type, "[network]")==0) return NETWORK; || strcmp(type, "[network]")==0) return NETWORK;
if (strcmp(type, "[crnn]")==0) return CRNN; if (strcmp(type, "[crnn]")==0) return CRNN;
if (strcmp(type, "[gru]")==0) return GRU; if (strcmp(type, "[gru]")==0) return GRU;
if (strcmp(type, "[lstm]") == 0) return LSTM;
if (strcmp(type, "[rnn]")==0) return RNN; if (strcmp(type, "[rnn]")==0) return RNN;
if (strcmp(type, "[conn]")==0 if (strcmp(type, "[conn]")==0
|| strcmp(type, "[connected]")==0) return CONNECTED; || strcmp(type, "[connected]")==0) return CONNECTED;
@ -239,6 +241,16 @@ layer parse_gru(list *options, size_params params)
return l; return l;
} }
layer parse_lstm(list *options, size_params params)
{
int output = option_find_int(options, "output", 1);
int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
layer l = make_lstm_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize);
return l;
}
connected_layer parse_connected(list *options, size_params params) connected_layer parse_connected(list *options, size_params params)
{ {
int output = option_find_int(options, "output",1); int output = option_find_int(options, "output",1);
@ -666,6 +678,8 @@ network parse_network_cfg(char *filename)
l = parse_rnn(options, params); l = parse_rnn(options, params);
}else if(lt == GRU){ }else if(lt == GRU){
l = parse_gru(options, params); l = parse_gru(options, params);
}else if (lt == LSTM) {
l = parse_lstm(options, params);
}else if(lt == CRNN){ }else if(lt == CRNN){
l = parse_crnn(options, params); l = parse_crnn(options, params);
}else if(lt == CONNECTED){ }else if(lt == CONNECTED){
@ -906,13 +920,22 @@ void save_weights_upto(network net, char *filename, int cutoff)
save_connected_weights(*(l.input_layer), fp); save_connected_weights(*(l.input_layer), fp);
save_connected_weights(*(l.self_layer), fp); save_connected_weights(*(l.self_layer), fp);
save_connected_weights(*(l.output_layer), fp); save_connected_weights(*(l.output_layer), fp);
} if (l.type == LSTM) {
save_connected_weights(*(l.wi), fp);
save_connected_weights(*(l.wf), fp);
save_connected_weights(*(l.wo), fp);
save_connected_weights(*(l.wg), fp);
save_connected_weights(*(l.ui), fp);
save_connected_weights(*(l.uf), fp);
save_connected_weights(*(l.uo), fp);
save_connected_weights(*(l.ug), fp);
} if (l.type == GRU) { } if (l.type == GRU) {
save_connected_weights(*(l.input_z_layer), fp); save_connected_weights(*(l.wz), fp);
save_connected_weights(*(l.input_r_layer), fp); save_connected_weights(*(l.wr), fp);
save_connected_weights(*(l.input_h_layer), fp); save_connected_weights(*(l.wh), fp);
save_connected_weights(*(l.state_z_layer), fp); save_connected_weights(*(l.uz), fp);
save_connected_weights(*(l.state_r_layer), fp); save_connected_weights(*(l.ur), fp);
save_connected_weights(*(l.state_h_layer), fp); save_connected_weights(*(l.uh), fp);
} if(l.type == CRNN){ } if(l.type == CRNN){
save_convolutional_weights(*(l.input_layer), fp); save_convolutional_weights(*(l.input_layer), fp);
save_convolutional_weights(*(l.self_layer), fp); save_convolutional_weights(*(l.self_layer), fp);
@ -1104,14 +1127,24 @@ void load_weights_upto(network *net, char *filename, int start, int cutoff)
load_connected_weights(*(l.input_layer), fp, transpose); load_connected_weights(*(l.input_layer), fp, transpose);
load_connected_weights(*(l.self_layer), fp, transpose); load_connected_weights(*(l.self_layer), fp, transpose);
load_connected_weights(*(l.output_layer), fp, transpose); load_connected_weights(*(l.output_layer), fp, transpose);
}
if (l.type == LSTM) {
load_connected_weights(*(l.wi), fp, transpose);
load_connected_weights(*(l.wf), fp, transpose);
load_connected_weights(*(l.wo), fp, transpose);
load_connected_weights(*(l.wg), fp, transpose);
load_connected_weights(*(l.ui), fp, transpose);
load_connected_weights(*(l.uf), fp, transpose);
load_connected_weights(*(l.uo), fp, transpose);
load_connected_weights(*(l.ug), fp, transpose);
} }
if (l.type == GRU) { if (l.type == GRU) {
load_connected_weights(*(l.input_z_layer), fp, transpose); load_connected_weights(*(l.wz), fp, transpose);
load_connected_weights(*(l.input_r_layer), fp, transpose); load_connected_weights(*(l.wr), fp, transpose);
load_connected_weights(*(l.input_h_layer), fp, transpose); load_connected_weights(*(l.wh), fp, transpose);
load_connected_weights(*(l.state_z_layer), fp, transpose); load_connected_weights(*(l.uz), fp, transpose);
load_connected_weights(*(l.state_r_layer), fp, transpose); load_connected_weights(*(l.ur), fp, transpose);
load_connected_weights(*(l.state_h_layer), fp, transpose); load_connected_weights(*(l.uh), fp, transpose);
} }
if(l.type == LOCAL){ if(l.type == LOCAL){
int locations = l.out_w*l.out_h; int locations = l.out_w*l.out_h;