From e36182cd8c5dd5c6d0aa1f77cf5cdca87e8bb1f0 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 21 Nov 2014 23:35:19 +0000
Subject: [PATCH] cleaned up data parsing a lot. probably nothing broken?
---
src/connected_layer.c | 108 ++++++++++++++++++++++++++++++++++++++++++++----------
1 files changed, 88 insertions(+), 20 deletions(-)
diff --git a/src/connected_layer.c b/src/connected_layer.c
index 95db5d5..05d4a03 100644
--- a/src/connected_layer.c
+++ b/src/connected_layer.c
@@ -9,7 +9,6 @@
connected_layer *make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation, float learning_rate, float momentum, float decay)
{
- fprintf(stderr, "Connected Layer: %d inputs, %d outputs\n", inputs, outputs);
int i;
connected_layer *layer = calloc(1, sizeof(connected_layer));
@@ -26,48 +25,50 @@
layer->weight_updates = calloc(inputs*outputs, sizeof(float));
//layer->weight_adapt = calloc(inputs*outputs, sizeof(float));
- layer->weight_momentum = calloc(inputs*outputs, sizeof(float));
layer->weights = calloc(inputs*outputs, sizeof(float));
float scale = 1./inputs;
- scale = .05;
+ scale = .01;
for(i = 0; i < inputs*outputs; ++i)
layer->weights[i] = scale*2*(rand_uniform()-.5);
layer->bias_updates = calloc(outputs, sizeof(float));
//layer->bias_adapt = calloc(outputs, sizeof(float));
- layer->bias_momentum = calloc(outputs, sizeof(float));
layer->biases = calloc(outputs, sizeof(float));
for(i = 0; i < outputs; ++i){
//layer->biases[i] = rand_normal()*scale + scale;
layer->biases[i] = 1;
- }
+ }
#ifdef GPU
+ layer->weights_cl = cl_make_array(layer->weights, inputs*outputs);
+ layer->biases_cl = cl_make_array(layer->biases, outputs);
+
+ layer->weight_updates_cl = cl_make_array(layer->weight_updates, inputs*outputs);
+ layer->bias_updates_cl = cl_make_array(layer->bias_updates, outputs);
+
+ layer->output_cl = cl_make_array(layer->output, outputs*batch);
+ layer->delta_cl = cl_make_array(layer->delta, outputs*batch);
#endif
layer->activation = activation;
+ fprintf(stderr, "Connected Layer: %d inputs, %d outputs\n", inputs, outputs);
return layer;
}
void update_connected_layer(connected_layer layer)
{
- int i;
- for(i = 0; i < layer.outputs; ++i){
- layer.bias_momentum[i] = layer.learning_rate*(layer.bias_updates[i]) + layer.momentum*layer.bias_momentum[i];
- layer.biases[i] += layer.bias_momentum[i];
- }
- for(i = 0; i < layer.outputs*layer.inputs; ++i){
- layer.weight_momentum[i] = layer.learning_rate*(layer.weight_updates[i] - layer.decay*layer.weights[i]) + layer.momentum*layer.weight_momentum[i];
- layer.weights[i] += layer.weight_momentum[i];
- }
- memset(layer.bias_updates, 0, layer.outputs*sizeof(float));
- memset(layer.weight_updates, 0, layer.outputs*layer.inputs*sizeof(float));
+ axpy_cpu(layer.outputs, layer.learning_rate, layer.bias_updates, 1, layer.biases, 1);
+ scal_cpu(layer.outputs, layer.momentum, layer.bias_updates, 1);
+
+ scal_cpu(layer.inputs*layer.outputs, 1.-layer.learning_rate*layer.decay, layer.weights, 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){
- memcpy(layer.output+i*layer.outputs, layer.biases, layer.outputs*sizeof(float));
+ copy_cpu(layer.outputs, layer.biases, 1, layer.output + i*layer.outputs, 1);
}
int m = layer.batch;
int k = layer.inputs;
@@ -82,9 +83,9 @@
void backward_connected_layer(connected_layer layer, float *input, float *delta)
{
int i;
- for(i = 0; i < layer.outputs*layer.batch; ++i){
- layer.delta[i] *= gradient(layer.output[i], layer.activation);
- layer.bias_updates[i%layer.outputs] += layer.delta[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);
}
int m = layer.inputs;
int k = layer.batch;
@@ -105,3 +106,70 @@
if(c) gemm(0,1,m,n,k,1,a,k,b,k,0,c,n);
}
+#ifdef GPU
+
+void pull_connected_layer(connected_layer layer)
+{
+ cl_read_array(layer.weights_cl, layer.weights, layer.inputs*layer.outputs);
+ cl_read_array(layer.biases_cl, layer.biases, layer.outputs);
+}
+
+void push_connected_layer(connected_layer layer)
+{
+ cl_write_array(layer.weights_cl, layer.weights, layer.inputs*layer.outputs);
+ cl_write_array(layer.biases_cl, layer.biases, layer.outputs);
+}
+
+void update_connected_layer_gpu(connected_layer layer)
+{
+ axpy_ongpu(layer.outputs, layer.learning_rate, layer.bias_updates_cl, 1, layer.biases_cl, 1);
+ scal_ongpu(layer.outputs, layer.momentum, layer.bias_updates_cl, 1);
+
+ scal_ongpu(layer.inputs*layer.outputs, 1.-layer.learning_rate*layer.decay, layer.weights_cl, 1);
+ axpy_ongpu(layer.inputs*layer.outputs, layer.learning_rate, layer.weight_updates_cl, 1, layer.weights_cl, 1);
+ scal_ongpu(layer.inputs*layer.outputs, layer.momentum, layer.weight_updates_cl, 1);
+ pull_connected_layer(layer);
+}
+
+void forward_connected_layer_gpu(connected_layer layer, cl_mem input)
+{
+ int i;
+ for(i = 0; i < layer.batch; ++i){
+ copy_ongpu_offset(layer.outputs, layer.biases_cl, 0, 1, layer.output_cl, i*layer.outputs, 1);
+ }
+ int m = layer.batch;
+ int k = layer.inputs;
+ int n = layer.outputs;
+ cl_mem a = input;
+ cl_mem b = layer.weights_cl;
+ cl_mem c = layer.output_cl;
+ gemm_ongpu(0,0,m,n,k,1,a,k,b,n,1,c,n);
+ activate_array_ongpu(layer.output_cl, layer.outputs*layer.batch, layer.activation);
+}
+
+void backward_connected_layer_gpu(connected_layer layer, cl_mem input, cl_mem delta)
+{
+ int i;
+ gradient_array_ongpu(layer.output_cl, layer.outputs*layer.batch, layer.activation, layer.delta_cl);
+ for(i = 0; i < layer.batch; ++i){
+ axpy_ongpu_offset(layer.outputs, 1, layer.delta_cl, i*layer.outputs, 1, layer.bias_updates_cl, 0, 1);
+ }
+ int m = layer.inputs;
+ int k = layer.batch;
+ int n = layer.outputs;
+ cl_mem a = input;
+ cl_mem b = layer.delta_cl;
+ cl_mem c = layer.weight_updates_cl;
+ gemm_ongpu(1,0,m,n,k,1,a,m,b,n,1,c,n);
+
+ m = layer.batch;
+ k = layer.outputs;
+ n = layer.inputs;
+
+ a = layer.delta_cl;
+ b = layer.weights_cl;
+ c = delta;
+
+ if(c) gemm_ongpu(0,1,m,n,k,1,a,k,b,k,0,c,n);
+}
+ #endif
--
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