From f047cfff99e00e28c02eb59b6d32386c122f9af6 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Sun, 08 Mar 2015 18:31:12 +0000
Subject: [PATCH] renamed sigmoid to logistic
---
src/connected_layer.c | 243 ++++++++++++++++++++++++++++++++++++------------
1 files changed, 181 insertions(+), 62 deletions(-)
diff --git a/src/connected_layer.c b/src/connected_layer.c
index 9fafc38..642570c 100644
--- a/src/connected_layer.c
+++ b/src/connected_layer.c
@@ -1,96 +1,215 @@
#include "connected_layer.h"
+#include "utils.h"
+#include "cuda.h"
+#include "blas.h"
+#include "gemm.h"
#include <math.h>
+#include <stdio.h>
#include <stdlib.h>
#include <string.h>
-connected_layer *make_connected_layer(int inputs, int outputs, ACTIVATION activator)
+connected_layer *make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation, float learning_rate, float momentum, float decay)
{
int i;
connected_layer *layer = calloc(1, sizeof(connected_layer));
+
+ layer->learning_rate = learning_rate;
+ layer->momentum = momentum;
+ layer->decay = decay;
+
layer->inputs = inputs;
layer->outputs = outputs;
+ layer->batch=batch;
- layer->output = calloc(outputs, sizeof(double*));
+ layer->output = calloc(batch*outputs, sizeof(float*));
+ layer->delta = calloc(batch*outputs, sizeof(float*));
- layer->weight_updates = calloc(inputs*outputs, sizeof(double));
- layer->weights = calloc(inputs*outputs, sizeof(double));
- for(i = 0; i < inputs*outputs; ++i)
- layer->weights[i] = .5 - (double)rand()/RAND_MAX;
+ layer->weight_updates = calloc(inputs*outputs, sizeof(float));
+ layer->bias_updates = calloc(outputs, sizeof(float));
- layer->bias_updates = calloc(outputs, sizeof(double));
- layer->biases = calloc(outputs, sizeof(double));
- for(i = 0; i < outputs; ++i)
- layer->biases[i] = (double)rand()/RAND_MAX;
+ layer->weight_prev = calloc(inputs*outputs, sizeof(float));
+ layer->bias_prev = calloc(outputs, sizeof(float));
- if(activator == SIGMOID){
- layer->activation = sigmoid_activation;
- layer->gradient = sigmoid_gradient;
- }else if(activator == RELU){
- layer->activation = relu_activation;
- layer->gradient = relu_gradient;
- }else if(activator == IDENTITY){
- layer->activation = identity_activation;
- layer->gradient = identity_gradient;
+ layer->weights = calloc(inputs*outputs, sizeof(float));
+ layer->biases = calloc(outputs, sizeof(float));
+
+
+ float scale = 1./sqrt(inputs);
+ for(i = 0; i < inputs*outputs; ++i){
+ layer->weights[i] = scale*rand_normal();
}
+ for(i = 0; i < outputs; ++i){
+ layer->biases[i] = scale;
+ }
+
+#ifdef GPU
+ layer->weights_gpu = cuda_make_array(layer->weights, inputs*outputs);
+ layer->biases_gpu = cuda_make_array(layer->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);
+
+ layer->output_gpu = cuda_make_array(layer->output, outputs*batch);
+ layer->delta_gpu = cuda_make_array(layer->delta, outputs*batch);
+#endif
+ layer->activation = activation;
+ fprintf(stderr, "Connected Layer: %d inputs, %d outputs\n", inputs, outputs);
return layer;
}
-void run_connected_layer(double *input, connected_layer layer)
+void secret_update_connected_layer(connected_layer *layer)
{
- int i, j;
- for(i = 0; i < layer.outputs; ++i){
- layer.output[i] = layer.biases[i];
- for(j = 0; j < layer.inputs; ++j){
- layer.output[i] += input[j]*layer.weights[i*layer.inputs + j];
- }
- layer.output[i] = layer.activation(layer.output[i]);
+ int n = layer->outputs*layer->inputs;
+ float dot = dot_cpu(n, layer->weight_updates, 1, layer->weight_prev, 1);
+ float mag = sqrt(dot_cpu(n, layer->weight_updates, 1, layer->weight_updates, 1))
+ * sqrt(dot_cpu(n, layer->weight_prev, 1, layer->weight_prev, 1));
+ float cos = dot/mag;
+ if(cos > .3) layer->learning_rate *= 1.1;
+ else if (cos < -.3) layer-> learning_rate /= 1.1;
+
+ scal_cpu(n, layer->momentum, layer->weight_prev, 1);
+ axpy_cpu(n, 1, layer->weight_updates, 1, layer->weight_prev, 1);
+ scal_cpu(n, 0, layer->weight_updates, 1);
+
+ scal_cpu(layer->outputs, layer->momentum, layer->bias_prev, 1);
+ axpy_cpu(layer->outputs, 1, layer->bias_updates, 1, layer->bias_prev, 1);
+ scal_cpu(layer->outputs, 0, layer->bias_updates, 1);
+
+ axpy_cpu(layer->outputs, layer->learning_rate, layer->bias_prev, 1, layer->biases, 1);
+
+ axpy_cpu(layer->inputs*layer->outputs, -layer->decay, layer->weights, 1, layer->weight_prev, 1);
+ axpy_cpu(layer->inputs*layer->outputs, layer->learning_rate, layer->weight_prev, 1, layer->weights, 1);
+}
+
+void update_connected_layer(connected_layer layer)
+{
+ axpy_cpu(layer.outputs, layer.learning_rate, layer.bias_updates, 1, layer.biases, 1);
+ scal_cpu(layer.outputs, layer.momentum, layer.bias_updates, 1);
+
+ axpy_cpu(layer.inputs*layer.outputs, -layer.decay, layer.weights, 1, layer.weight_updates, 1);
+ axpy_cpu(layer.inputs*layer.outputs, layer.learning_rate, layer.weight_updates, 1, layer.weights, 1);
+ scal_cpu(layer.inputs*layer.outputs, layer.momentum, layer.weight_updates, 1);
+}
+
+void forward_connected_layer(connected_layer layer, float *input)
+{
+ int i;
+ for(i = 0; i < layer.batch; ++i){
+ copy_cpu(layer.outputs, layer.biases, 1, layer.output + i*layer.outputs, 1);
}
+ int m = layer.batch;
+ int k = layer.inputs;
+ int n = layer.outputs;
+ float *a = 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);
}
-void learn_connected_layer(double *input, connected_layer layer)
+void backward_connected_layer(connected_layer layer, float *input, float *delta)
{
- calculate_update_connected_layer(input, layer);
- backpropagate_connected_layer(input, layer);
-}
-
-void update_connected_layer(connected_layer layer, double step)
-{
- int i,j;
- for(i = 0; i < layer.outputs; ++i){
- layer.biases[i] += step*layer.bias_updates[i];
- for(j = 0; j < layer.inputs; ++j){
- int index = i*layer.inputs+j;
- layer.weights[index] += step*layer.weight_updates[index];
- }
+ int i;
+ float alpha = 1./layer.batch;
+ gradient_array(layer.output, layer.outputs*layer.batch, layer.activation, layer.delta);
+ for(i = 0; i < layer.batch; ++i){
+ axpy_cpu(layer.outputs, alpha, layer.delta + i*layer.outputs, 1, layer.bias_updates, 1);
}
- memset(layer.bias_updates, 0, layer.outputs*sizeof(double));
- memset(layer.weight_updates, 0, layer.outputs*layer.inputs*sizeof(double));
+ int m = layer.inputs;
+ int k = layer.batch;
+ int n = layer.outputs;
+ float *a = input;
+ float *b = layer.delta;
+ float *c = layer.weight_updates;
+ gemm(1,0,m,n,k,alpha,a,m,b,n,1,c,n);
+
+ m = layer.batch;
+ k = layer.outputs;
+ n = layer.inputs;
+
+ a = layer.delta;
+ b = layer.weights;
+ c = delta;
+
+ if(c) gemm(0,1,m,n,k,1,a,k,b,k,0,c,n);
}
-void calculate_update_connected_layer(double *input, connected_layer layer)
+#ifdef GPU
+
+void pull_connected_layer(connected_layer layer)
{
- int i, j;
- for(i = 0; i < layer.outputs; ++i){
- layer.bias_updates[i] += layer.output[i];
- for(j = 0; j < layer.inputs; ++j){
- layer.weight_updates[i*layer.inputs + j] += layer.output[i]*input[j];
- }
- }
+ 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);
}
-void backpropagate_connected_layer(double *input, connected_layer layer)
+void push_connected_layer(connected_layer layer)
{
- int i, j;
-
- for(j = 0; j < layer.inputs; ++j){
- double grad = layer.gradient(input[j]);
- input[j] = 0;
- for(i = 0; i < layer.outputs; ++i){
- input[j] += layer.output[i]*layer.weights[i*layer.inputs + j];
- }
- input[j] *= grad;
- }
+ 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);
}
+void update_connected_layer_gpu(connected_layer layer)
+{
+/*
+ cuda_pull_array(layer.weights_gpu, layer.weights, layer.inputs*layer.outputs);
+ cuda_pull_array(layer.weight_updates_gpu, layer.weight_updates, layer.inputs*layer.outputs);
+ printf("Weights: %f updates: %f\n", mag_array(layer.weights, layer.inputs*layer.outputs), layer.learning_rate*mag_array(layer.weight_updates, layer.inputs*layer.outputs));
+*/
+
+ axpy_ongpu(layer.outputs, layer.learning_rate, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
+ scal_ongpu(layer.outputs, layer.momentum, layer.bias_updates_gpu, 1);
+
+ axpy_ongpu(layer.inputs*layer.outputs, -layer.decay, layer.weights_gpu, 1, layer.weight_updates_gpu, 1);
+ axpy_ongpu(layer.inputs*layer.outputs, layer.learning_rate, layer.weight_updates_gpu, 1, layer.weights_gpu, 1);
+ scal_ongpu(layer.inputs*layer.outputs, layer.momentum, layer.weight_updates_gpu, 1);
+}
+
+void forward_connected_layer_gpu(connected_layer layer, float * input)
+{
+ 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);
+ }
+ int m = layer.batch;
+ int k = layer.inputs;
+ int n = layer.outputs;
+ float * a = 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);
+}
+
+void backward_connected_layer_gpu(connected_layer layer, float * input, float * delta)
+{
+ float alpha = 1./layer.batch;
+ 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, alpha, layer.delta_gpu, i*layer.outputs, 1, layer.bias_updates_gpu, 0, 1);
+ }
+ int m = layer.inputs;
+ int k = layer.batch;
+ int n = layer.outputs;
+ float * a = input;
+ float * b = layer.delta_gpu;
+ float * c = layer.weight_updates_gpu;
+ gemm_ongpu(1,0,m,n,k,alpha,a,m,b,n,1,c,n);
+
+ m = layer.batch;
+ k = layer.outputs;
+ n = layer.inputs;
+
+ a = layer.delta_gpu;
+ b = layer.weights_gpu;
+ c = delta;
+
+ if(c) gemm_ongpu(0,1,m,n,k,1,a,k,b,k,0,c,n);
+}
+#endif
--
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