From 0f1a31648c5292fa49b35eac90a2ee676d6c13e6 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Sat, 31 Jan 2015 06:05:23 +0000
Subject: [PATCH] idk, probably something changed
---
src/network.c | 82 +++++++++++++++++++++++++++++++++++++----
1 files changed, 74 insertions(+), 8 deletions(-)
diff --git a/src/network.c b/src/network.c
index 829bb6e..b628561 100644
--- a/src/network.c
+++ b/src/network.c
@@ -15,6 +15,33 @@
#include "softmax_layer.h"
#include "dropout_layer.h"
+char *get_layer_string(LAYER_TYPE a)
+{
+ switch(a){
+ case CONVOLUTIONAL:
+ return "convolutional";
+ case CONNECTED:
+ return "connected";
+ case MAXPOOL:
+ return "maxpool";
+ case SOFTMAX:
+ return "softmax";
+ case NORMALIZATION:
+ return "normalization";
+ case DROPOUT:
+ return "dropout";
+ case FREEWEIGHT:
+ return "freeweight";
+ case CROP:
+ return "crop";
+ case COST:
+ return "cost";
+ default:
+ break;
+ }
+ return "none";
+}
+
network make_network(int n, int batch)
{
network net;
@@ -24,14 +51,14 @@
net.types = calloc(net.n, sizeof(LAYER_TYPE));
net.outputs = 0;
net.output = 0;
+ net.seen = 0;
#ifdef GPU
- net.input_cl = calloc(1, sizeof(cl_mem));
- net.truth_cl = calloc(1, sizeof(cl_mem));
+ net.input_gpu = calloc(1, sizeof(float *));
+ net.truth_gpu = calloc(1, sizeof(float *));
#endif
return net;
}
-
void forward_network(network net, float *input, float *truth, int train)
{
int i;
@@ -48,7 +75,7 @@
}
else if(net.types[i] == CROP){
crop_layer layer = *(crop_layer *)net.layers[i];
- forward_crop_layer(layer, input);
+ forward_crop_layer(layer, train, input);
input = layer.output;
}
else if(net.types[i] == COST){
@@ -74,12 +101,16 @@
if(!train) continue;
dropout_layer layer = *(dropout_layer *)net.layers[i];
forward_dropout_layer(layer, input);
+ input = layer.output;
}
else if(net.types[i] == FREEWEIGHT){
if(!train) continue;
- freeweight_layer layer = *(freeweight_layer *)net.layers[i];
- forward_freeweight_layer(layer, input);
+ //freeweight_layer layer = *(freeweight_layer *)net.layers[i];
+ //forward_freeweight_layer(layer, input);
}
+ //char buff[256];
+ //sprintf(buff, "layer %d", i);
+ //cuda_compare(get_network_output_gpu_layer(net, i), input, get_network_output_size_layer(net, i)*net.batch, buff);
}
}
@@ -102,6 +133,7 @@
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
+ //secret_update_connected_layer((connected_layer *)net.layers[i]);
update_connected_layer(layer);
}
}
@@ -119,7 +151,8 @@
softmax_layer layer = *(softmax_layer *)net.layers[i];
return layer.output;
} else if(net.types[i] == DROPOUT){
- return get_network_output_layer(net, i-1);
+ dropout_layer layer = *(dropout_layer *)net.layers[i];
+ return layer.output;
} else if(net.types[i] == FREEWEIGHT){
return get_network_output_layer(net, i-1);
} else if(net.types[i] == CONNECTED){
@@ -153,6 +186,7 @@
softmax_layer layer = *(softmax_layer *)net.layers[i];
return layer.delta;
} else if(net.types[i] == DROPOUT){
+ if(i == 0) return 0;
return get_network_delta_layer(net, i-1);
} else if(net.types[i] == FREEWEIGHT){
return get_network_delta_layer(net, i-1);
@@ -368,6 +402,10 @@
cost_layer *layer = (cost_layer *)net->layers[i];
layer->batch = b;
}
+ else if(net->types[i] == CROP){
+ crop_layer *layer = (crop_layer *)net->layers[i];
+ layer->batch = b;
+ }
}
}
@@ -498,6 +536,9 @@
normalization_layer layer = *(normalization_layer *)net.layers[i];
return get_normalization_image(layer);
}
+ else if(net.types[i] == DROPOUT){
+ return get_network_image_layer(net, i-1);
+ }
else if(net.types[i] == CROP){
crop_layer layer = *(crop_layer *)net.layers[i];
return get_crop_image(layer);
@@ -545,7 +586,7 @@
float *network_predict(network net, float *input)
{
#ifdef GPU
- if(gpu_index >= 0) return network_predict_gpu(net, input);
+ if(gpu_index >= 0) return network_predict_gpu(net, input);
#endif
forward_network(net, input, 0, 0);
@@ -645,6 +686,31 @@
}
}
+void compare_networks(network n1, network n2, data test)
+{
+ matrix g1 = network_predict_data(n1, test);
+ matrix g2 = network_predict_data(n2, test);
+ int i;
+ int a,b,c,d;
+ a = b = c = d = 0;
+ for(i = 0; i < g1.rows; ++i){
+ int truth = max_index(test.y.vals[i], test.y.cols);
+ int p1 = max_index(g1.vals[i], g1.cols);
+ int p2 = max_index(g2.vals[i], g2.cols);
+ if(p1 == truth){
+ if(p2 == truth) ++d;
+ else ++c;
+ }else{
+ if(p2 == truth) ++b;
+ else ++a;
+ }
+ }
+ printf("%5d %5d\n%5d %5d\n", a, b, c, d);
+ float num = pow((abs(b - c) - 1.), 2.);
+ float den = b + c;
+ printf("%f\n", num/den);
+}
+
float network_accuracy(network net, data d)
{
matrix guess = network_predict_data(net, d);
--
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