layer make_rnn_layer(int batch, int inputs, int hidden, int outputs, int steps,
ACTIVATION activation, int batch_normalize, int log) {
fprintf(stderr, "RNN Layer: %d inputs, %d outputs\n", inputs, outputs);
batch = batch / steps;
layer l = { 0 };
l.batch = batch;
l.type = RNN;
l.steps = steps;
l.hidden = hidden;
l.inputs = inputs;
l.state = calloc(batch * hidden * (steps + 1), sizeof(float));
l.input_layer = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.input_layer) = make_connected_layer(batch * steps, inputs, hidden,
activation, batch_normalize);
l.input_layer->batch = batch;
l.self_layer = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.self_layer) = make_connected_layer(batch * steps, hidden, hidden,
(log == 2) ? LOGGY : (log == 1 ? LOGISTIC : activation),
batch_normalize);
l.self_layer->batch = batch;
l.output_layer = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.output_layer) = make_connected_layer(batch * steps, hidden, outputs,
activation, batch_normalize);
l.output_layer->batch = batch;
l.outputs = outputs;
l.output = l.output_layer->output;
l.delta = l.output_layer->delta;
#ifdef GPU
l.state_gpu = cuda_make_array(l.state, batch*hidden*(steps+1));
l.output_gpu = l.output_layer->output_gpu;
l.delta_gpu = l.output_layer->delta_gpu;
#endif
return l;
}
layer parse_connected(list *options, size_params params)
{
int output = option_find_int(options, "output",1);
char *activation_s = option_find_str(options, "activation", "logistic");
ACTIVATION activation = get_activation(activation_s);
int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
layer l = make_connected_layer(params.batch, params.inputs, output, activation, batch_normalize, params.net->adam);
return l;
}
connected_layer parse_connected(list *options, size_params params)
{
int output = option_find_int(options, "output",1);
char *activation_s = option_find_str(options, "activation", "logistic");
ACTIVATION activation = get_activation(activation_s);
connected_layer layer = make_connected_layer(params.batch, params.inputs, output, activation);
char *weights = option_find_str(options, "weights", 0);
char *biases = option_find_str(options, "biases", 0);
parse_data(biases, layer.biases, output);
parse_data(weights, layer.weights, params.inputs*output);
#ifdef GPU
if(weights || biases) push_connected_layer(layer);
#endif
return layer;
}
layer make_lstm_layer(int batch, int inputs, int outputs, int steps,
int batch_normalize, int adam) {
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, adam);
l.uf->batch = batch;
l.ui = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.ui) = make_connected_layer(batch * steps, inputs, outputs, LINEAR,
batch_normalize, adam);
l.ui->batch = batch;
l.ug = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.ug) = make_connected_layer(batch * steps, inputs, outputs, LINEAR,
batch_normalize, adam);
l.ug->batch = batch;
l.uo = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.uo) = make_connected_layer(batch * steps, inputs, outputs, LINEAR,
batch_normalize, adam);
l.uo->batch = batch;
l.wf = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.wf) = make_connected_layer(batch * steps, outputs, outputs, LINEAR,
batch_normalize, adam);
l.wf->batch = batch;
l.wi = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.wi) = make_connected_layer(batch * steps, outputs, outputs, LINEAR,
batch_normalize, adam);
l.wi->batch = batch;
l.wg = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.wg) = make_connected_layer(batch * steps, outputs, outputs, LINEAR,
batch_normalize, adam);
l.wg->batch = batch;
l.wo = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.wo) = make_connected_layer(batch * steps, outputs, outputs, LINEAR,
batch_normalize, adam);
l.wo->batch = batch;
l.batch_normalize = batch_normalize;
l.outputs = outputs;
l.output = calloc(outputs * batch * steps, sizeof(real_t));
l.state = calloc(outputs * batch, sizeof(real_t));
l.forward = forward_lstm_layer;
l.update = update_lstm_layer;
l.prev_state_cpu = calloc(batch * outputs, sizeof(real_t));
l.prev_cell_cpu = calloc(batch * outputs, sizeof(real_t));
l.cell_cpu = calloc(batch * outputs * steps, sizeof(real_t));
l.f_cpu = calloc(batch * outputs, sizeof(real_t));
l.i_cpu = calloc(batch * outputs, sizeof(real_t));
l.g_cpu = calloc(batch * outputs, sizeof(real_t));
l.o_cpu = calloc(batch * outputs, sizeof(real_t));
l.c_cpu = calloc(batch * outputs, sizeof(real_t));
l.h_cpu = calloc(batch * outputs, sizeof(real_t));
l.temp_cpu = calloc(batch * outputs, sizeof(real_t));
l.temp2_cpu = calloc(batch * outputs, sizeof(real_t));
l.temp3_cpu = calloc(batch * outputs, sizeof(real_t));
l.dc_cpu = calloc(batch * outputs, sizeof(real_t));
l.dh_cpu = calloc(batch * outputs, sizeof(real_t));
#ifdef GPU
l.forward_gpu = forward_lstm_layer_gpu;
l.backward_gpu = backward_lstm_layer_gpu;
l.update_gpu = update_lstm_layer_gpu;
l.output_gpu = cuda_make_array(0, batch * outputs * steps);
l.delta_gpu = cuda_make_array(0, batch * l.outputs * steps);
l.prev_state_gpu = cuda_make_array(0, batch * outputs);
l.prev_cell_gpu = cuda_make_array(0, batch * outputs);
l.cell_gpu = cuda_make_array(0, batch * outputs * steps);
l.f_gpu = cuda_make_array(0, batch * outputs);
l.i_gpu = cuda_make_array(0, batch * outputs);
l.g_gpu = cuda_make_array(0, batch * outputs);
l.o_gpu = cuda_make_array(0, batch * outputs);
l.c_gpu = cuda_make_array(0, batch * outputs);
l.h_gpu = cuda_make_array(0, batch * outputs);
l.temp_gpu = cuda_make_array(0, batch * outputs);
l.temp2_gpu = cuda_make_array(0, batch * outputs);
l.temp3_gpu = cuda_make_array(0, batch * outputs);
l.dc_gpu = cuda_make_array(0, batch * outputs);
l.dh_gpu = cuda_make_array(0, batch * outputs);
#ifdef CUDNN
cudnnSetTensor4dDescriptor(l.wf->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wf->out_c, l.wf->out_h, l.wf->out_w);
cudnnSetTensor4dDescriptor(l.wi->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wi->out_c, l.wi->out_h, l.wi->out_w);
cudnnSetTensor4dDescriptor(l.wg->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wg->out_c, l.wg->out_h, l.wg->out_w);
cudnnSetTensor4dDescriptor(l.wo->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wo->out_c, l.wo->out_h, l.wo->out_w);
cudnnSetTensor4dDescriptor(l.uf->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uf->out_c, l.uf->out_h, l.uf->out_w);
cudnnSetTensor4dDescriptor(l.ui->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ui->out_c, l.ui->out_h, l.ui->out_w);
cudnnSetTensor4dDescriptor(l.ug->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ug->out_c, l.ug->out_h, l.ug->out_w);
cudnnSetTensor4dDescriptor(l.uo->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uo->out_c, l.uo->out_h, l.uo->out_w);
#endif
#endif
return l;
}
