#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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connected_layer *make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation)
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{
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int i;
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connected_layer *layer = calloc(1, sizeof(connected_layer));
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layer->inputs = inputs;
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layer->outputs = outputs;
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layer->batch=batch;
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layer->output = calloc(batch*outputs, sizeof(float*));
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layer->delta = calloc(batch*outputs, sizeof(float*));
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layer->weight_updates = calloc(inputs*outputs, sizeof(float));
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layer->bias_updates = calloc(outputs, sizeof(float));
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layer->weight_prev = calloc(inputs*outputs, sizeof(float));
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layer->bias_prev = calloc(outputs, sizeof(float));
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layer->weights = calloc(inputs*outputs, sizeof(float));
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layer->biases = calloc(outputs, sizeof(float));
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float scale = 1./sqrt(inputs);
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for(i = 0; i < inputs*outputs; ++i){
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layer->weights[i] = scale*rand_normal();
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}
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for(i = 0; i < outputs; ++i){
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layer->biases[i] = scale;
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}
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#ifdef GPU
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layer->weights_gpu = cuda_make_array(layer->weights, inputs*outputs);
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layer->biases_gpu = cuda_make_array(layer->biases, outputs);
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layer->weight_updates_gpu = cuda_make_array(layer->weight_updates, inputs*outputs);
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layer->bias_updates_gpu = cuda_make_array(layer->bias_updates, outputs);
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layer->output_gpu = cuda_make_array(layer->output, outputs*batch);
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layer->delta_gpu = cuda_make_array(layer->delta, outputs*batch);
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#endif
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layer->activation = activation;
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fprintf(stderr, "Connected Layer: %d inputs, %d outputs\n", inputs, outputs);
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return layer;
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}
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void update_connected_layer(connected_layer layer, float learning_rate, float momentum, float decay)
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{
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axpy_cpu(layer.outputs, learning_rate, layer.bias_updates, 1, layer.biases, 1);
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scal_cpu(layer.outputs, momentum, layer.bias_updates, 1);
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axpy_cpu(layer.inputs*layer.outputs, -decay, layer.weights, 1, layer.weight_updates, 1);
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axpy_cpu(layer.inputs*layer.outputs, learning_rate, layer.weight_updates, 1, layer.weights, 1);
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scal_cpu(layer.inputs*layer.outputs, momentum, layer.weight_updates, 1);
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}
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void forward_connected_layer(connected_layer layer, network_state state)
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{
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int i;
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for(i = 0; i < layer.batch; ++i){
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copy_cpu(layer.outputs, layer.biases, 1, layer.output + i*layer.outputs, 1);
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}
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int m = layer.batch;
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int k = layer.inputs;
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int n = layer.outputs;
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float *a = state.input;
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float *b = layer.weights;
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float *c = layer.output;
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gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
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activate_array(layer.output, layer.outputs*layer.batch, layer.activation);
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}
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void backward_connected_layer(connected_layer layer, network_state state)
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{
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int i;
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float alpha = 1./layer.batch;
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gradient_array(layer.output, layer.outputs*layer.batch, layer.activation, layer.delta);
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for(i = 0; i < layer.batch; ++i){
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axpy_cpu(layer.outputs, alpha, layer.delta + i*layer.outputs, 1, layer.bias_updates, 1);
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}
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int m = layer.inputs;
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int k = layer.batch;
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int n = layer.outputs;
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float *a = state.input;
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float *b = layer.delta;
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float *c = layer.weight_updates;
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gemm(1,0,m,n,k,alpha,a,m,b,n,1,c,n);
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m = layer.batch;
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k = layer.outputs;
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n = layer.inputs;
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a = layer.delta;
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b = layer.weights;
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c = state.delta;
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if(c) gemm(0,1,m,n,k,1,a,k,b,k,0,c,n);
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}
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#ifdef GPU
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void pull_connected_layer(connected_layer layer)
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{
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cuda_pull_array(layer.weights_gpu, layer.weights, layer.inputs*layer.outputs);
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cuda_pull_array(layer.biases_gpu, layer.biases, layer.outputs);
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cuda_pull_array(layer.weight_updates_gpu, layer.weight_updates, layer.inputs*layer.outputs);
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cuda_pull_array(layer.bias_updates_gpu, layer.bias_updates, layer.outputs);
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}
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void push_connected_layer(connected_layer layer)
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{
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cuda_push_array(layer.weights_gpu, layer.weights, layer.inputs*layer.outputs);
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cuda_push_array(layer.biases_gpu, layer.biases, layer.outputs);
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cuda_push_array(layer.weight_updates_gpu, layer.weight_updates, layer.inputs*layer.outputs);
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cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.outputs);
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}
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void update_connected_layer_gpu(connected_layer layer, float learning_rate, float momentum, float decay)
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{
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axpy_ongpu(layer.outputs, learning_rate, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
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scal_ongpu(layer.outputs, momentum, layer.bias_updates_gpu, 1);
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axpy_ongpu(layer.inputs*layer.outputs, -decay, layer.weights_gpu, 1, layer.weight_updates_gpu, 1);
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axpy_ongpu(layer.inputs*layer.outputs, learning_rate, layer.weight_updates_gpu, 1, layer.weights_gpu, 1);
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scal_ongpu(layer.inputs*layer.outputs, momentum, layer.weight_updates_gpu, 1);
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}
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void forward_connected_layer_gpu(connected_layer layer, network_state state)
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{
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int i;
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for(i = 0; i < layer.batch; ++i){
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copy_ongpu_offset(layer.outputs, layer.biases_gpu, 0, 1, layer.output_gpu, i*layer.outputs, 1);
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}
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int m = layer.batch;
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int k = layer.inputs;
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int n = layer.outputs;
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float * a = state.input;
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float * b = layer.weights_gpu;
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float * c = layer.output_gpu;
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gemm_ongpu(0,0,m,n,k,1,a,k,b,n,1,c,n);
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activate_array_ongpu(layer.output_gpu, layer.outputs*layer.batch, layer.activation);
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}
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void backward_connected_layer_gpu(connected_layer layer, network_state state)
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{
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float alpha = 1./layer.batch;
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int i;
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gradient_array_ongpu(layer.output_gpu, layer.outputs*layer.batch, layer.activation, layer.delta_gpu);
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for(i = 0; i < layer.batch; ++i){
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axpy_ongpu_offset(layer.outputs, alpha, layer.delta_gpu, i*layer.outputs, 1, layer.bias_updates_gpu, 0, 1);
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}
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int m = layer.inputs;
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int k = layer.batch;
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int n = layer.outputs;
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float * a = state.input;
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float * b = layer.delta_gpu;
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float * c = layer.weight_updates_gpu;
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gemm_ongpu(1,0,m,n,k,alpha,a,m,b,n,1,c,n);
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m = layer.batch;
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k = layer.outputs;
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n = layer.inputs;
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a = layer.delta_gpu;
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b = layer.weights_gpu;
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c = state.delta;
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if(c) gemm_ongpu(0,1,m,n,k,1,a,k,b,k,0,c,n);
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}
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#endif
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