mirror of https://github.com/pjreddie/darknet.git
337 lines
11 KiB
C
337 lines
11 KiB
C
#include "connected_layer.h"
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#include "convolutional_layer.h"
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#include "batchnorm_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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layer make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation, int batch_normalize, int adam)
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{
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int i;
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layer l = {0};
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l.learning_rate_scale = 1;
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l.type = CONNECTED;
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l.inputs = inputs;
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l.outputs = outputs;
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l.batch=batch;
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l.batch_normalize = batch_normalize;
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l.h = 1;
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l.w = 1;
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l.c = inputs;
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l.out_h = 1;
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l.out_w = 1;
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l.out_c = outputs;
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l.output = calloc(batch*outputs, sizeof(float));
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l.delta = calloc(batch*outputs, sizeof(float));
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l.weight_updates = calloc(inputs*outputs, sizeof(float));
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l.bias_updates = calloc(outputs, sizeof(float));
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l.weights = calloc(outputs*inputs, sizeof(float));
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l.biases = calloc(outputs, sizeof(float));
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l.forward = forward_connected_layer;
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l.backward = backward_connected_layer;
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l.update = update_connected_layer;
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//float scale = 1./sqrt(inputs);
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float scale = sqrt(2./inputs);
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for(i = 0; i < outputs*inputs; ++i){
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l.weights[i] = scale*rand_uniform(-1, 1);
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}
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for(i = 0; i < outputs; ++i){
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l.biases[i] = 0;
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}
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if(adam){
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l.m = calloc(l.inputs*l.outputs, sizeof(float));
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l.v = calloc(l.inputs*l.outputs, sizeof(float));
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l.bias_m = calloc(l.outputs, sizeof(float));
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l.scale_m = calloc(l.outputs, sizeof(float));
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l.bias_v = calloc(l.outputs, sizeof(float));
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l.scale_v = calloc(l.outputs, sizeof(float));
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}
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if(batch_normalize){
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l.scales = calloc(outputs, sizeof(float));
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l.scale_updates = calloc(outputs, sizeof(float));
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for(i = 0; i < outputs; ++i){
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l.scales[i] = 1;
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}
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l.mean = calloc(outputs, sizeof(float));
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l.mean_delta = calloc(outputs, sizeof(float));
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l.variance = calloc(outputs, sizeof(float));
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l.variance_delta = calloc(outputs, sizeof(float));
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l.rolling_mean = calloc(outputs, sizeof(float));
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l.rolling_variance = calloc(outputs, sizeof(float));
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l.x = calloc(batch*outputs, sizeof(float));
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l.x_norm = calloc(batch*outputs, sizeof(float));
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}
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#ifdef GPU
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l.forward_gpu = forward_connected_layer_gpu;
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l.backward_gpu = backward_connected_layer_gpu;
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l.update_gpu = update_connected_layer_gpu;
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l.weights_gpu = cuda_make_array(l.weights, outputs*inputs);
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l.biases_gpu = cuda_make_array(l.biases, outputs);
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l.weight_updates_gpu = cuda_make_array(l.weight_updates, outputs*inputs);
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l.bias_updates_gpu = cuda_make_array(l.bias_updates, outputs);
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l.output_gpu = cuda_make_array(l.output, outputs*batch);
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l.delta_gpu = cuda_make_array(l.delta, outputs*batch);
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if (adam) {
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l.m_gpu = cuda_make_array(0, inputs*outputs);
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l.v_gpu = cuda_make_array(0, inputs*outputs);
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l.bias_m_gpu = cuda_make_array(0, outputs);
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l.bias_v_gpu = cuda_make_array(0, outputs);
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l.scale_m_gpu = cuda_make_array(0, outputs);
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l.scale_v_gpu = cuda_make_array(0, outputs);
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}
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if(batch_normalize){
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l.mean_gpu = cuda_make_array(l.mean, outputs);
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l.variance_gpu = cuda_make_array(l.variance, outputs);
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l.rolling_mean_gpu = cuda_make_array(l.mean, outputs);
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l.rolling_variance_gpu = cuda_make_array(l.variance, outputs);
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l.mean_delta_gpu = cuda_make_array(l.mean, outputs);
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l.variance_delta_gpu = cuda_make_array(l.variance, outputs);
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l.scales_gpu = cuda_make_array(l.scales, outputs);
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l.scale_updates_gpu = cuda_make_array(l.scale_updates, outputs);
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l.x_gpu = cuda_make_array(l.output, l.batch*outputs);
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l.x_norm_gpu = cuda_make_array(l.output, l.batch*outputs);
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#ifdef CUDNN
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cudnnCreateTensorDescriptor(&l.normTensorDesc);
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cudnnCreateTensorDescriptor(&l.dstTensorDesc);
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cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w);
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cudnnSetTensor4dDescriptor(l.normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l.out_c, 1, 1);
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#endif
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}
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#endif
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l.activation = activation;
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fprintf(stderr, "connected %4d -> %4d\n", inputs, outputs);
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return l;
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}
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void update_connected_layer(layer l, update_args a)
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{
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float learning_rate = a.learning_rate*l.learning_rate_scale;
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float momentum = a.momentum;
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float decay = a.decay;
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int batch = a.batch;
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axpy_cpu(l.outputs, learning_rate/batch, l.bias_updates, 1, l.biases, 1);
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scal_cpu(l.outputs, momentum, l.bias_updates, 1);
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if(l.batch_normalize){
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axpy_cpu(l.outputs, learning_rate/batch, l.scale_updates, 1, l.scales, 1);
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scal_cpu(l.outputs, momentum, l.scale_updates, 1);
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}
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axpy_cpu(l.inputs*l.outputs, -decay*batch, l.weights, 1, l.weight_updates, 1);
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axpy_cpu(l.inputs*l.outputs, learning_rate/batch, l.weight_updates, 1, l.weights, 1);
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scal_cpu(l.inputs*l.outputs, momentum, l.weight_updates, 1);
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}
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void forward_connected_layer(layer l, network net)
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{
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fill_cpu(l.outputs*l.batch, 0, l.output, 1);
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int m = l.batch;
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int k = l.inputs;
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int n = l.outputs;
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float *a = net.input;
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float *b = l.weights;
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float *c = l.output;
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gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
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if(l.batch_normalize){
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forward_batchnorm_layer(l, net);
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} else {
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add_bias(l.output, l.biases, l.batch, l.outputs, 1);
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}
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activate_array(l.output, l.outputs*l.batch, l.activation);
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}
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void backward_connected_layer(layer l, network net)
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{
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gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);
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if(l.batch_normalize){
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backward_batchnorm_layer(l, net);
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} else {
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backward_bias(l.bias_updates, l.delta, l.batch, l.outputs, 1);
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}
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int m = l.outputs;
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int k = l.batch;
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int n = l.inputs;
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float *a = l.delta;
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float *b = net.input;
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float *c = l.weight_updates;
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gemm(1,0,m,n,k,1,a,m,b,n,1,c,n);
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m = l.batch;
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k = l.outputs;
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n = l.inputs;
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a = l.delta;
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b = l.weights;
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c = net.delta;
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if(c) gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
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}
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void denormalize_connected_layer(layer l)
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{
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int i, j;
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for(i = 0; i < l.outputs; ++i){
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float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .000001);
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for(j = 0; j < l.inputs; ++j){
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l.weights[i*l.inputs + j] *= scale;
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}
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l.biases[i] -= l.rolling_mean[i] * scale;
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l.scales[i] = 1;
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l.rolling_mean[i] = 0;
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l.rolling_variance[i] = 1;
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}
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}
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void statistics_connected_layer(layer l)
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{
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if(l.batch_normalize){
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printf("Scales ");
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print_statistics(l.scales, l.outputs);
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/*
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printf("Rolling Mean ");
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print_statistics(l.rolling_mean, l.outputs);
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printf("Rolling Variance ");
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print_statistics(l.rolling_variance, l.outputs);
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*/
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}
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printf("Biases ");
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print_statistics(l.biases, l.outputs);
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printf("Weights ");
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print_statistics(l.weights, l.outputs);
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}
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#ifdef GPU
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void pull_connected_layer(layer l)
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{
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cuda_pull_array(l.weights_gpu, l.weights, l.inputs*l.outputs);
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cuda_pull_array(l.biases_gpu, l.biases, l.outputs);
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cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.inputs*l.outputs);
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cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.outputs);
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if (l.batch_normalize){
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cuda_pull_array(l.scales_gpu, l.scales, l.outputs);
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cuda_pull_array(l.rolling_mean_gpu, l.rolling_mean, l.outputs);
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cuda_pull_array(l.rolling_variance_gpu, l.rolling_variance, l.outputs);
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}
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}
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void push_connected_layer(layer l)
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{
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cuda_push_array(l.weights_gpu, l.weights, l.inputs*l.outputs);
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cuda_push_array(l.biases_gpu, l.biases, l.outputs);
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cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.inputs*l.outputs);
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cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.outputs);
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if (l.batch_normalize){
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cuda_push_array(l.scales_gpu, l.scales, l.outputs);
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cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.outputs);
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cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.outputs);
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}
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}
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void update_connected_layer_gpu(layer l, update_args a)
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{
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float learning_rate = a.learning_rate*l.learning_rate_scale;
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float momentum = a.momentum;
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float decay = a.decay;
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int batch = a.batch;
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if(a.adam){
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adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.inputs*l.outputs, batch, a.t);
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adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.outputs, batch, a.t);
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if(l.scales_gpu){
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adam_update_gpu(l.scales_gpu, l.scale_updates_gpu, l.scale_m_gpu, l.scale_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.outputs, batch, a.t);
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}
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}else{
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axpy_gpu(l.outputs, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1);
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scal_gpu(l.outputs, momentum, l.bias_updates_gpu, 1);
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if(l.batch_normalize){
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axpy_gpu(l.outputs, learning_rate/batch, l.scale_updates_gpu, 1, l.scales_gpu, 1);
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scal_gpu(l.outputs, momentum, l.scale_updates_gpu, 1);
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}
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axpy_gpu(l.inputs*l.outputs, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1);
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axpy_gpu(l.inputs*l.outputs, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1);
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scal_gpu(l.inputs*l.outputs, momentum, l.weight_updates_gpu, 1);
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}
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}
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void forward_connected_layer_gpu(layer l, network net)
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{
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fill_gpu(l.outputs*l.batch, 0, l.output_gpu, 1);
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int m = l.batch;
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int k = l.inputs;
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int n = l.outputs;
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float * a = net.input_gpu;
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float * b = l.weights_gpu;
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float * c = l.output_gpu;
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gemm_gpu(0,1,m,n,k,1,a,k,b,k,1,c,n);
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if (l.batch_normalize) {
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forward_batchnorm_layer_gpu(l, net);
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} else {
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add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.outputs, 1);
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}
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activate_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation);
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}
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void backward_connected_layer_gpu(layer l, network net)
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{
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constrain_gpu(l.outputs*l.batch, 1, l.delta_gpu, 1);
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gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
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if(l.batch_normalize){
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backward_batchnorm_layer_gpu(l, net);
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} else {
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backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.outputs, 1);
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}
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int m = l.outputs;
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int k = l.batch;
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int n = l.inputs;
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float * a = l.delta_gpu;
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float * b = net.input_gpu;
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float * c = l.weight_updates_gpu;
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gemm_gpu(1,0,m,n,k,1,a,m,b,n,1,c,n);
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m = l.batch;
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k = l.outputs;
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n = l.inputs;
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a = l.delta_gpu;
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b = l.weights_gpu;
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c = net.delta_gpu;
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if(c) gemm_gpu(0,0,m,n,k,1,a,k,b,n,1,c,n);
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}
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#endif
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