From db0397cfaaf488364e3d2e1669dfefae2ee6ea73 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Mon, 14 Dec 2015 19:57:10 +0000
Subject: [PATCH] shortcut layers, msr networks
---
src/connected_layer.c | 205 ++++++++++++++++++++++++++-------------------------
1 files changed, 105 insertions(+), 100 deletions(-)
diff --git a/src/connected_layer.c b/src/connected_layer.c
index 4c40fc0..2d83dd9 100644
--- a/src/connected_layer.c
+++ b/src/connected_layer.c
@@ -9,171 +9,176 @@
#include <stdlib.h>
#include <string.h>
-connected_layer *make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation)
+connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation)
{
int i;
- connected_layer *layer = calloc(1, sizeof(connected_layer));
+ connected_layer l = {0};
+ l.type = CONNECTED;
- layer->inputs = inputs;
- layer->outputs = outputs;
- layer->batch=batch;
+ l.inputs = inputs;
+ l.outputs = outputs;
+ l.batch=batch;
- layer->output = calloc(batch*outputs, sizeof(float*));
- layer->delta = calloc(batch*outputs, sizeof(float*));
+ l.output = calloc(batch*outputs, sizeof(float*));
+ l.delta = calloc(batch*outputs, sizeof(float*));
- layer->weight_updates = calloc(inputs*outputs, sizeof(float));
- layer->bias_updates = calloc(outputs, sizeof(float));
+ l.weight_updates = calloc(inputs*outputs, sizeof(float));
+ l.bias_updates = calloc(outputs, sizeof(float));
- layer->weight_prev = calloc(inputs*outputs, sizeof(float));
- layer->bias_prev = calloc(outputs, sizeof(float));
-
- layer->weights = calloc(inputs*outputs, sizeof(float));
- layer->biases = calloc(outputs, sizeof(float));
+ l.weights = calloc(outputs*inputs, sizeof(float));
+ l.biases = calloc(outputs, sizeof(float));
- float scale = 1./sqrt(inputs);
- for(i = 0; i < inputs*outputs; ++i){
- //layer->weights[i] = scale*rand_normal();
+ //float scale = 1./sqrt(inputs);
+ float scale = sqrt(2./inputs);
+ for(i = 0; i < outputs*inputs; ++i){
+ l.weights[i] = 2*scale*rand_uniform() - scale;
}
for(i = 0; i < outputs; ++i){
- layer->biases[i] = scale;
+ l.biases[i] = scale;
}
#ifdef GPU
- layer->weights_gpu = cuda_make_array(layer->weights, inputs*outputs);
- layer->biases_gpu = cuda_make_array(layer->biases, outputs);
+ l.weights_gpu = cuda_make_array(l.weights, outputs*inputs);
+ l.biases_gpu = cuda_make_array(l.biases, outputs);
- layer->weight_updates_gpu = cuda_make_array(layer->weight_updates, inputs*outputs);
- layer->bias_updates_gpu = cuda_make_array(layer->bias_updates, outputs);
+ l.weight_updates_gpu = cuda_make_array(l.weight_updates, outputs*inputs);
+ l.bias_updates_gpu = cuda_make_array(l.bias_updates, outputs);
- layer->output_gpu = cuda_make_array(layer->output, outputs*batch);
- layer->delta_gpu = cuda_make_array(layer->delta, outputs*batch);
+ l.output_gpu = cuda_make_array(l.output, outputs*batch);
+ l.delta_gpu = cuda_make_array(l.delta, outputs*batch);
#endif
- layer->activation = activation;
+ l.activation = activation;
fprintf(stderr, "Connected Layer: %d inputs, %d outputs\n", inputs, outputs);
- return layer;
+ return l;
}
-void update_connected_layer(connected_layer layer, int batch, float learning_rate, float momentum, float decay)
+void update_connected_layer(connected_layer l, int batch, float learning_rate, float momentum, float decay)
{
- axpy_cpu(layer.outputs, learning_rate/batch, layer.bias_updates, 1, layer.biases, 1);
- scal_cpu(layer.outputs, momentum, layer.bias_updates, 1);
+ axpy_cpu(l.outputs, learning_rate/batch, l.bias_updates, 1, l.biases, 1);
+ scal_cpu(l.outputs, momentum, l.bias_updates, 1);
- axpy_cpu(layer.inputs*layer.outputs, -decay*batch, layer.weights, 1, layer.weight_updates, 1);
- axpy_cpu(layer.inputs*layer.outputs, learning_rate/batch, layer.weight_updates, 1, layer.weights, 1);
- scal_cpu(layer.inputs*layer.outputs, momentum, layer.weight_updates, 1);
+ axpy_cpu(l.inputs*l.outputs, -decay*batch, l.weights, 1, l.weight_updates, 1);
+ axpy_cpu(l.inputs*l.outputs, learning_rate/batch, l.weight_updates, 1, l.weights, 1);
+ scal_cpu(l.inputs*l.outputs, momentum, l.weight_updates, 1);
}
-void forward_connected_layer(connected_layer layer, network_state state)
+void forward_connected_layer(connected_layer l, network_state state)
{
int i;
- for(i = 0; i < layer.batch; ++i){
- copy_cpu(layer.outputs, layer.biases, 1, layer.output + i*layer.outputs, 1);
+ for(i = 0; i < l.batch; ++i){
+ copy_cpu(l.outputs, l.biases, 1, l.output + i*l.outputs, 1);
}
- int m = layer.batch;
- int k = layer.inputs;
- int n = layer.outputs;
+ int m = l.batch;
+ int k = l.inputs;
+ int n = l.outputs;
float *a = state.input;
- float *b = layer.weights;
- float *c = layer.output;
- gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
- activate_array(layer.output, layer.outputs*layer.batch, layer.activation);
+ float *b = l.weights;
+ float *c = l.output;
+ gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
+ activate_array(l.output, l.outputs*l.batch, l.activation);
}
-void backward_connected_layer(connected_layer layer, network_state state)
+void backward_connected_layer(connected_layer l, network_state state)
{
int i;
- gradient_array(layer.output, layer.outputs*layer.batch, layer.activation, layer.delta);
- for(i = 0; i < layer.batch; ++i){
- axpy_cpu(layer.outputs, 1, layer.delta + i*layer.outputs, 1, layer.bias_updates, 1);
+ gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);
+ for(i = 0; i < l.batch; ++i){
+ axpy_cpu(l.outputs, 1, l.delta + i*l.outputs, 1, l.bias_updates, 1);
}
- int m = layer.inputs;
- int k = layer.batch;
- int n = layer.outputs;
- float *a = state.input;
- float *b = layer.delta;
- float *c = layer.weight_updates;
+ int m = l.outputs;
+ int k = l.batch;
+ int n = l.inputs;
+ float *a = l.delta;
+ float *b = state.input;
+ float *c = l.weight_updates;
gemm(1,0,m,n,k,1,a,m,b,n,1,c,n);
- m = layer.batch;
- k = layer.outputs;
- n = layer.inputs;
+ m = l.batch;
+ k = l.outputs;
+ n = l.inputs;
- a = layer.delta;
- b = layer.weights;
+ a = l.delta;
+ b = l.weights;
c = state.delta;
- if(c) gemm(0,1,m,n,k,1,a,k,b,k,0,c,n);
+ if(c) gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
}
#ifdef GPU
-void pull_connected_layer(connected_layer layer)
+void pull_connected_layer(connected_layer l)
{
- cuda_pull_array(layer.weights_gpu, layer.weights, layer.inputs*layer.outputs);
- cuda_pull_array(layer.biases_gpu, layer.biases, layer.outputs);
- cuda_pull_array(layer.weight_updates_gpu, layer.weight_updates, layer.inputs*layer.outputs);
- cuda_pull_array(layer.bias_updates_gpu, layer.bias_updates, layer.outputs);
+ cuda_pull_array(l.weights_gpu, l.weights, l.inputs*l.outputs);
+ cuda_pull_array(l.biases_gpu, l.biases, l.outputs);
+ cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.inputs*l.outputs);
+ cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.outputs);
}
-void push_connected_layer(connected_layer layer)
+void push_connected_layer(connected_layer l)
{
- cuda_push_array(layer.weights_gpu, layer.weights, layer.inputs*layer.outputs);
- cuda_push_array(layer.biases_gpu, layer.biases, layer.outputs);
- cuda_push_array(layer.weight_updates_gpu, layer.weight_updates, layer.inputs*layer.outputs);
- cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.outputs);
+ cuda_push_array(l.weights_gpu, l.weights, l.inputs*l.outputs);
+ cuda_push_array(l.biases_gpu, l.biases, l.outputs);
+ cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.inputs*l.outputs);
+ cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.outputs);
}
-void update_connected_layer_gpu(connected_layer layer, int batch, float learning_rate, float momentum, float decay)
+void update_connected_layer_gpu(connected_layer l, int batch, float learning_rate, float momentum, float decay)
{
- axpy_ongpu(layer.outputs, learning_rate/batch, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
- scal_ongpu(layer.outputs, momentum, layer.bias_updates_gpu, 1);
+ axpy_ongpu(l.outputs, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1);
+ scal_ongpu(l.outputs, momentum, l.bias_updates_gpu, 1);
- axpy_ongpu(layer.inputs*layer.outputs, -decay*batch, layer.weights_gpu, 1, layer.weight_updates_gpu, 1);
- axpy_ongpu(layer.inputs*layer.outputs, learning_rate/batch, layer.weight_updates_gpu, 1, layer.weights_gpu, 1);
- scal_ongpu(layer.inputs*layer.outputs, momentum, layer.weight_updates_gpu, 1);
+ axpy_ongpu(l.inputs*l.outputs, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1);
+ axpy_ongpu(l.inputs*l.outputs, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1);
+ scal_ongpu(l.inputs*l.outputs, momentum, l.weight_updates_gpu, 1);
}
-void forward_connected_layer_gpu(connected_layer layer, network_state state)
+void forward_connected_layer_gpu(connected_layer l, network_state state)
{
int i;
- for(i = 0; i < layer.batch; ++i){
- copy_ongpu_offset(layer.outputs, layer.biases_gpu, 0, 1, layer.output_gpu, i*layer.outputs, 1);
+ for(i = 0; i < l.batch; ++i){
+ copy_ongpu_offset(l.outputs, l.biases_gpu, 0, 1, l.output_gpu, i*l.outputs, 1);
}
- int m = layer.batch;
- int k = layer.inputs;
- int n = layer.outputs;
+ int m = l.batch;
+ int k = l.inputs;
+ int n = l.outputs;
float * a = state.input;
- float * b = layer.weights_gpu;
- float * c = layer.output_gpu;
- gemm_ongpu(0,0,m,n,k,1,a,k,b,n,1,c,n);
- activate_array_ongpu(layer.output_gpu, layer.outputs*layer.batch, layer.activation);
+ float * b = l.weights_gpu;
+ float * c = l.output_gpu;
+ gemm_ongpu(0,1,m,n,k,1,a,k,b,k,1,c,n);
+ activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
+
+/*
+ cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch);
+ float avg = mean_array(l.output, l.outputs*l.batch);
+ printf("%f\n", avg);
+ */
}
-void backward_connected_layer_gpu(connected_layer layer, network_state state)
+void backward_connected_layer_gpu(connected_layer l, network_state state)
{
int i;
- gradient_array_ongpu(layer.output_gpu, layer.outputs*layer.batch, layer.activation, layer.delta_gpu);
- for(i = 0; i < layer.batch; ++i){
- axpy_ongpu_offset(layer.outputs, 1, layer.delta_gpu, i*layer.outputs, 1, layer.bias_updates_gpu, 0, 1);
+ gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
+ for(i = 0; i < l.batch; ++i){
+ axpy_ongpu_offset(l.outputs, 1, l.delta_gpu, i*l.outputs, 1, l.bias_updates_gpu, 0, 1);
}
- int m = layer.inputs;
- int k = layer.batch;
- int n = layer.outputs;
- float * a = state.input;
- float * b = layer.delta_gpu;
- float * c = layer.weight_updates_gpu;
+ int m = l.outputs;
+ int k = l.batch;
+ int n = l.inputs;
+ float * a = l.delta_gpu;
+ float * b = state.input;
+ float * c = l.weight_updates_gpu;
gemm_ongpu(1,0,m,n,k,1,a,m,b,n,1,c,n);
- m = layer.batch;
- k = layer.outputs;
- n = layer.inputs;
+ m = l.batch;
+ k = l.outputs;
+ n = l.inputs;
- a = layer.delta_gpu;
- b = layer.weights_gpu;
+ a = l.delta_gpu;
+ b = l.weights_gpu;
c = state.delta;
- if(c) gemm_ongpu(0,1,m,n,k,1,a,k,b,k,0,c,n);
+ if(c) gemm_ongpu(0,0,m,n,k,1,a,k,b,n,1,c,n);
}
#endif
--
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