mirror of
https://github.com/pjreddie/darknet.git
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396 lines
14 KiB
C
396 lines
14 KiB
C
#include "gru_layer.h"
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#include "connected_layer.h"
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#include "utils.h"
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#include "cuda.h"
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#include "blas.h"
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#include "gemm.h"
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#include <math.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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static void increment_layer(layer *l, int steps)
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{
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int num = l->outputs*l->batch*steps;
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l->output += num;
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l->delta += num;
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l->x += num;
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l->x_norm += num;
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#ifdef GPU
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l->output_gpu += num;
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l->delta_gpu += num;
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l->x_gpu += num;
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l->x_norm_gpu += num;
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#endif
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}
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layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_normalize)
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{
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fprintf(stderr, "GRU Layer: %d inputs, %d outputs\n", inputs, outputs);
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batch = batch / steps;
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layer l = {0};
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l.batch = batch;
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l.type = GRU;
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l.steps = steps;
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l.inputs = inputs;
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l.input_z_layer = malloc(sizeof(layer));
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fprintf(stderr, "\t\t");
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*(l.input_z_layer) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize);
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l.input_z_layer->batch = batch;
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l.state_z_layer = malloc(sizeof(layer));
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fprintf(stderr, "\t\t");
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*(l.state_z_layer) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize);
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l.state_z_layer->batch = batch;
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l.input_r_layer = malloc(sizeof(layer));
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fprintf(stderr, "\t\t");
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*(l.input_r_layer) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize);
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l.input_r_layer->batch = batch;
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l.state_r_layer = malloc(sizeof(layer));
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fprintf(stderr, "\t\t");
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*(l.state_r_layer) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize);
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l.state_r_layer->batch = batch;
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l.input_h_layer = malloc(sizeof(layer));
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fprintf(stderr, "\t\t");
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*(l.input_h_layer) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize);
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l.input_h_layer->batch = batch;
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l.state_h_layer = malloc(sizeof(layer));
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fprintf(stderr, "\t\t");
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*(l.state_h_layer) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize);
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l.state_h_layer->batch = batch;
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l.batch_normalize = batch_normalize;
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l.outputs = outputs;
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l.output = calloc(outputs*batch*steps, sizeof(float));
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l.delta = calloc(outputs*batch*steps, sizeof(float));
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l.state = calloc(outputs*batch, sizeof(float));
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l.prev_state = calloc(outputs*batch, sizeof(float));
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l.forgot_state = calloc(outputs*batch, sizeof(float));
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l.forgot_delta = calloc(outputs*batch, sizeof(float));
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l.r_cpu = calloc(outputs*batch, sizeof(float));
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l.z_cpu = calloc(outputs*batch, sizeof(float));
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l.h_cpu = calloc(outputs*batch, sizeof(float));
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l.forward = forward_gru_layer;
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l.backward = backward_gru_layer;
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l.update = update_gru_layer;
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#ifdef GPU
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l.forward_gpu = forward_gru_layer_gpu;
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l.backward_gpu = backward_gru_layer_gpu;
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l.update_gpu = update_gru_layer_gpu;
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l.forgot_state_gpu = cuda_make_array(l.output, batch*outputs);
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l.forgot_delta_gpu = cuda_make_array(l.output, batch*outputs);
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l.prev_state_gpu = cuda_make_array(l.output, batch*outputs);
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l.state_gpu = cuda_make_array(l.output, batch*outputs);
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l.output_gpu = cuda_make_array(l.output, batch*outputs*steps);
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l.delta_gpu = cuda_make_array(l.delta, batch*outputs*steps);
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l.r_gpu = cuda_make_array(l.output_gpu, batch*outputs);
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l.z_gpu = cuda_make_array(l.output_gpu, batch*outputs);
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l.h_gpu = cuda_make_array(l.output_gpu, batch*outputs);
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#endif
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return l;
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}
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void update_gru_layer(layer l, int batch, float learning_rate, float momentum, float decay)
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{
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update_connected_layer(*(l.input_layer), batch, learning_rate, momentum, decay);
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update_connected_layer(*(l.self_layer), batch, learning_rate, momentum, decay);
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update_connected_layer(*(l.output_layer), batch, learning_rate, momentum, decay);
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}
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void forward_gru_layer(layer l, network_state state)
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{
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network_state s = {0};
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s.train = state.train;
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int i;
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layer input_z_layer = *(l.input_z_layer);
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layer input_r_layer = *(l.input_r_layer);
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layer input_h_layer = *(l.input_h_layer);
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layer state_z_layer = *(l.state_z_layer);
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layer state_r_layer = *(l.state_r_layer);
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layer state_h_layer = *(l.state_h_layer);
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fill_cpu(l.outputs * l.batch * l.steps, 0, input_z_layer.delta, 1);
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fill_cpu(l.outputs * l.batch * l.steps, 0, input_r_layer.delta, 1);
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fill_cpu(l.outputs * l.batch * l.steps, 0, input_h_layer.delta, 1);
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fill_cpu(l.outputs * l.batch * l.steps, 0, state_z_layer.delta, 1);
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fill_cpu(l.outputs * l.batch * l.steps, 0, state_r_layer.delta, 1);
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fill_cpu(l.outputs * l.batch * l.steps, 0, state_h_layer.delta, 1);
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if(state.train) {
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fill_cpu(l.outputs * l.batch * l.steps, 0, l.delta, 1);
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copy_cpu(l.outputs*l.batch, l.state, 1, l.prev_state, 1);
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}
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for (i = 0; i < l.steps; ++i) {
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s.input = l.state;
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forward_connected_layer(state_z_layer, s);
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forward_connected_layer(state_r_layer, s);
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s.input = state.input;
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forward_connected_layer(input_z_layer, s);
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forward_connected_layer(input_r_layer, s);
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forward_connected_layer(input_h_layer, s);
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copy_cpu(l.outputs*l.batch, input_z_layer.output, 1, l.z_cpu, 1);
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axpy_cpu(l.outputs*l.batch, 1, state_z_layer.output, 1, l.z_cpu, 1);
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copy_cpu(l.outputs*l.batch, input_r_layer.output, 1, l.r_cpu, 1);
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axpy_cpu(l.outputs*l.batch, 1, state_r_layer.output, 1, l.r_cpu, 1);
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activate_array(l.z_cpu, l.outputs*l.batch, LOGISTIC);
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activate_array(l.r_cpu, l.outputs*l.batch, LOGISTIC);
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copy_cpu(l.outputs*l.batch, l.state, 1, l.forgot_state, 1);
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mul_cpu(l.outputs*l.batch, l.r_cpu, 1, l.forgot_state, 1);
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s.input = l.forgot_state;
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forward_connected_layer(state_h_layer, s);
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copy_cpu(l.outputs*l.batch, input_h_layer.output, 1, l.h_cpu, 1);
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axpy_cpu(l.outputs*l.batch, 1, state_h_layer.output, 1, l.h_cpu, 1);
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#ifdef USET
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activate_array(l.h_cpu, l.outputs*l.batch, TANH);
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#else
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activate_array(l.h_cpu, l.outputs*l.batch, LOGISTIC);
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#endif
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weighted_sum_cpu(l.state, l.h_cpu, l.z_cpu, l.outputs*l.batch, l.output);
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copy_cpu(l.outputs*l.batch, l.output, 1, l.state, 1);
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state.input += l.inputs*l.batch;
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l.output += l.outputs*l.batch;
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increment_layer(&input_z_layer, 1);
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increment_layer(&input_r_layer, 1);
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increment_layer(&input_h_layer, 1);
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increment_layer(&state_z_layer, 1);
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increment_layer(&state_r_layer, 1);
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increment_layer(&state_h_layer, 1);
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}
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}
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void backward_gru_layer(layer l, network_state state)
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{
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}
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#ifdef GPU
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void pull_gru_layer(layer l)
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{
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}
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void push_gru_layer(layer l)
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{
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}
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void update_gru_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay)
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{
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update_connected_layer_gpu(*(l.input_r_layer), batch, learning_rate, momentum, decay);
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update_connected_layer_gpu(*(l.input_z_layer), batch, learning_rate, momentum, decay);
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update_connected_layer_gpu(*(l.input_h_layer), batch, learning_rate, momentum, decay);
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update_connected_layer_gpu(*(l.state_r_layer), batch, learning_rate, momentum, decay);
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update_connected_layer_gpu(*(l.state_z_layer), batch, learning_rate, momentum, decay);
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update_connected_layer_gpu(*(l.state_h_layer), batch, learning_rate, momentum, decay);
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}
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void forward_gru_layer_gpu(layer l, network_state state)
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{
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network_state s = {0};
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s.train = state.train;
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int i;
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layer input_z_layer = *(l.input_z_layer);
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layer input_r_layer = *(l.input_r_layer);
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layer input_h_layer = *(l.input_h_layer);
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layer state_z_layer = *(l.state_z_layer);
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layer state_r_layer = *(l.state_r_layer);
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layer state_h_layer = *(l.state_h_layer);
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fill_ongpu(l.outputs * l.batch * l.steps, 0, input_z_layer.delta_gpu, 1);
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fill_ongpu(l.outputs * l.batch * l.steps, 0, input_r_layer.delta_gpu, 1);
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fill_ongpu(l.outputs * l.batch * l.steps, 0, input_h_layer.delta_gpu, 1);
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fill_ongpu(l.outputs * l.batch * l.steps, 0, state_z_layer.delta_gpu, 1);
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fill_ongpu(l.outputs * l.batch * l.steps, 0, state_r_layer.delta_gpu, 1);
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fill_ongpu(l.outputs * l.batch * l.steps, 0, state_h_layer.delta_gpu, 1);
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if(state.train) {
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fill_ongpu(l.outputs * l.batch * l.steps, 0, l.delta_gpu, 1);
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copy_ongpu(l.outputs*l.batch, l.state_gpu, 1, l.prev_state_gpu, 1);
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}
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for (i = 0; i < l.steps; ++i) {
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s.input = l.state_gpu;
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forward_connected_layer_gpu(state_z_layer, s);
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forward_connected_layer_gpu(state_r_layer, s);
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s.input = state.input;
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forward_connected_layer_gpu(input_z_layer, s);
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forward_connected_layer_gpu(input_r_layer, s);
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forward_connected_layer_gpu(input_h_layer, s);
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copy_ongpu(l.outputs*l.batch, input_z_layer.output_gpu, 1, l.z_gpu, 1);
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axpy_ongpu(l.outputs*l.batch, 1, state_z_layer.output_gpu, 1, l.z_gpu, 1);
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copy_ongpu(l.outputs*l.batch, input_r_layer.output_gpu, 1, l.r_gpu, 1);
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axpy_ongpu(l.outputs*l.batch, 1, state_r_layer.output_gpu, 1, l.r_gpu, 1);
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activate_array_ongpu(l.z_gpu, l.outputs*l.batch, LOGISTIC);
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activate_array_ongpu(l.r_gpu, l.outputs*l.batch, LOGISTIC);
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copy_ongpu(l.outputs*l.batch, l.state_gpu, 1, l.forgot_state_gpu, 1);
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mul_ongpu(l.outputs*l.batch, l.r_gpu, 1, l.forgot_state_gpu, 1);
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s.input = l.forgot_state_gpu;
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forward_connected_layer_gpu(state_h_layer, s);
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copy_ongpu(l.outputs*l.batch, input_h_layer.output_gpu, 1, l.h_gpu, 1);
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axpy_ongpu(l.outputs*l.batch, 1, state_h_layer.output_gpu, 1, l.h_gpu, 1);
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#ifdef USET
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activate_array_ongpu(l.h_gpu, l.outputs*l.batch, TANH);
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#else
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activate_array_ongpu(l.h_gpu, l.outputs*l.batch, LOGISTIC);
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#endif
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weighted_sum_gpu(l.state_gpu, l.h_gpu, l.z_gpu, l.outputs*l.batch, l.output_gpu);
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copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.state_gpu, 1);
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state.input += l.inputs*l.batch;
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l.output_gpu += l.outputs*l.batch;
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increment_layer(&input_z_layer, 1);
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increment_layer(&input_r_layer, 1);
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increment_layer(&input_h_layer, 1);
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increment_layer(&state_z_layer, 1);
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increment_layer(&state_r_layer, 1);
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increment_layer(&state_h_layer, 1);
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}
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}
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void backward_gru_layer_gpu(layer l, network_state state)
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{
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network_state s = {0};
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s.train = state.train;
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int i;
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layer input_z_layer = *(l.input_z_layer);
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layer input_r_layer = *(l.input_r_layer);
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layer input_h_layer = *(l.input_h_layer);
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layer state_z_layer = *(l.state_z_layer);
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layer state_r_layer = *(l.state_r_layer);
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layer state_h_layer = *(l.state_h_layer);
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increment_layer(&input_z_layer, l.steps - 1);
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increment_layer(&input_r_layer, l.steps - 1);
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increment_layer(&input_h_layer, l.steps - 1);
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increment_layer(&state_z_layer, l.steps - 1);
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increment_layer(&state_r_layer, l.steps - 1);
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increment_layer(&state_h_layer, l.steps - 1);
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state.input += l.inputs*l.batch*(l.steps-1);
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if(state.delta) state.delta += l.inputs*l.batch*(l.steps-1);
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l.output_gpu += l.outputs*l.batch*(l.steps-1);
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l.delta_gpu += l.outputs*l.batch*(l.steps-1);
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for (i = l.steps-1; i >= 0; --i) {
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if(i != 0) copy_ongpu(l.outputs*l.batch, l.output_gpu - l.outputs*l.batch, 1, l.prev_state_gpu, 1);
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float *prev_delta_gpu = (i == 0) ? 0 : l.delta_gpu - l.outputs*l.batch;
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copy_ongpu(l.outputs*l.batch, input_z_layer.output_gpu, 1, l.z_gpu, 1);
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axpy_ongpu(l.outputs*l.batch, 1, state_z_layer.output_gpu, 1, l.z_gpu, 1);
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copy_ongpu(l.outputs*l.batch, input_r_layer.output_gpu, 1, l.r_gpu, 1);
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axpy_ongpu(l.outputs*l.batch, 1, state_r_layer.output_gpu, 1, l.r_gpu, 1);
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activate_array_ongpu(l.z_gpu, l.outputs*l.batch, LOGISTIC);
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activate_array_ongpu(l.r_gpu, l.outputs*l.batch, LOGISTIC);
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copy_ongpu(l.outputs*l.batch, input_h_layer.output_gpu, 1, l.h_gpu, 1);
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axpy_ongpu(l.outputs*l.batch, 1, state_h_layer.output_gpu, 1, l.h_gpu, 1);
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#ifdef USET
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activate_array_ongpu(l.h_gpu, l.outputs*l.batch, TANH);
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#else
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activate_array_ongpu(l.h_gpu, l.outputs*l.batch, LOGISTIC);
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#endif
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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);
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#ifdef USET
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gradient_array_ongpu(l.h_gpu, l.outputs*l.batch, TANH, input_h_layer.delta_gpu);
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#else
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gradient_array_ongpu(l.h_gpu, l.outputs*l.batch, LOGISTIC, input_h_layer.delta_gpu);
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#endif
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copy_ongpu(l.outputs*l.batch, input_h_layer.delta_gpu, 1, state_h_layer.delta_gpu, 1);
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copy_ongpu(l.outputs*l.batch, l.prev_state_gpu, 1, l.forgot_state_gpu, 1);
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mul_ongpu(l.outputs*l.batch, l.r_gpu, 1, l.forgot_state_gpu, 1);
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fill_ongpu(l.outputs*l.batch, 0, l.forgot_delta_gpu, 1);
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s.input = l.forgot_state_gpu;
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s.delta = l.forgot_delta_gpu;
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backward_connected_layer_gpu(state_h_layer, s);
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if(prev_delta_gpu) mult_add_into_gpu(l.outputs*l.batch, l.forgot_delta_gpu, l.r_gpu, prev_delta_gpu);
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mult_add_into_gpu(l.outputs*l.batch, l.forgot_delta_gpu, l.prev_state_gpu, input_r_layer.delta_gpu);
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gradient_array_ongpu(l.r_gpu, l.outputs*l.batch, LOGISTIC, input_r_layer.delta_gpu);
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copy_ongpu(l.outputs*l.batch, input_r_layer.delta_gpu, 1, state_r_layer.delta_gpu, 1);
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gradient_array_ongpu(l.z_gpu, l.outputs*l.batch, LOGISTIC, input_z_layer.delta_gpu);
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copy_ongpu(l.outputs*l.batch, input_z_layer.delta_gpu, 1, state_z_layer.delta_gpu, 1);
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s.input = l.prev_state_gpu;
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s.delta = prev_delta_gpu;
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backward_connected_layer_gpu(state_r_layer, s);
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backward_connected_layer_gpu(state_z_layer, s);
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s.input = state.input;
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s.delta = state.delta;
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backward_connected_layer_gpu(input_h_layer, s);
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backward_connected_layer_gpu(input_r_layer, s);
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backward_connected_layer_gpu(input_z_layer, s);
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state.input -= l.inputs*l.batch;
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if(state.delta) state.delta -= l.inputs*l.batch;
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l.output_gpu -= l.outputs*l.batch;
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l.delta_gpu -= l.outputs*l.batch;
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increment_layer(&input_z_layer, -1);
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increment_layer(&input_r_layer, -1);
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increment_layer(&input_h_layer, -1);
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increment_layer(&state_z_layer, -1);
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increment_layer(&state_r_layer, -1);
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increment_layer(&state_h_layer, -1);
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}
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}
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#endif
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