From 76ee68f96d864a27312c9aa09856ddda559a5cd9 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 28 Aug 2014 02:11:46 +0000
Subject: [PATCH] Trying some stuff w/ dropout
---
src/cnn.c | 115 ++++++++++++++++++++++++++++++++++++++++++++++++++-------
1 files changed, 101 insertions(+), 14 deletions(-)
diff --git a/src/cnn.c b/src/cnn.c
index f866194..0cd6da3 100644
--- a/src/cnn.c
+++ b/src/cnn.c
@@ -32,6 +32,51 @@
show_image_layers(edge, "Test Convolve");
}
+#ifdef GPU
+
+void test_convolutional_layer()
+{
+ int i;
+ image dog = load_image("data/dog.jpg",256,256);
+ network net = parse_network_cfg("cfg/convolutional.cfg");
+// data test = load_cifar10_data("data/cifar10/test_batch.bin");
+// float *X = calloc(net.batch*test.X.cols, sizeof(float));
+// float *y = calloc(net.batch*test.y.cols, sizeof(float));
+ int in_size = get_network_input_size(net)*net.batch;
+ int size = get_network_output_size(net)*net.batch;
+float *X = calloc(in_size, sizeof(float));
+ for(i = 0; i < in_size; ++i){
+ X[i] = dog.data[i%get_network_input_size(net)];
+ }
+// get_batch(test, net.batch, X, y);
+ clock_t start, end;
+ cl_mem input_cl = cl_make_array(X, in_size);
+
+ forward_network_gpu(net, input_cl, 1);
+ start = clock();
+ forward_network_gpu(net, input_cl, 1);
+ end = clock();
+ float gpu_sec = (float)(end-start)/CLOCKS_PER_SEC;
+ float *gpu_out = calloc(size, sizeof(float));
+ memcpy(gpu_out, get_network_output(net), size*sizeof(float));
+
+ start = clock();
+ forward_network(net, X, 1);
+ end = clock();
+ float cpu_sec = (float)(end-start)/CLOCKS_PER_SEC;
+ float *cpu_out = calloc(size, sizeof(float));
+ memcpy(cpu_out, get_network_output(net), size*sizeof(float));
+
+ float sum = 0;
+ for(i = 0; i < size; ++i) {
+ //printf("%f, %f\n", gpu_out[i], cpu_out[i]);
+ sum += pow(gpu_out[i] - cpu_out[i], 2);
+ }
+ printf("gpu: %f sec, cpu: %f sec, diff: %f, size: %d\n", gpu_sec, cpu_sec, sum, size);
+}
+
+#endif
+
void test_convolve_matrix()
{
image dog = load_image("dog.jpg",300,400);
@@ -240,9 +285,22 @@
void test_cifar10()
{
- srand(222222);
+
+ network net = parse_network_cfg("cfg/cifar10_part5.cfg");
+ data test = load_cifar10_data("data/cifar10/test_batch.bin");
+ clock_t start = clock(), end;
+ float test_acc = network_accuracy(net, test);
+ end = clock();
+ printf("%f in %f Sec\n", test_acc, (float)(end-start)/CLOCKS_PER_SEC);
+ visualize_network(net);
+ cvWaitKey(0);
+}
+
+void train_cifar10()
+{
+ srand(555555);
network net = parse_network_cfg("cfg/cifar10.cfg");
- //data test = load_cifar10_data("data/cifar10/test_batch.bin");
+ data test = load_cifar10_data("data/cifar10/test_batch.bin");
int count = 0;
int iters = 10000/net.batch;
data train = load_all_cifar10();
@@ -250,12 +308,20 @@
clock_t start = clock(), end;
float loss = train_network_sgd(net, train, iters);
end = clock();
- //visualize_network(net);
- //cvWaitKey(1000);
+ visualize_network(net);
+ cvWaitKey(5000);
//float test_acc = network_accuracy(net, test);
//printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
- printf("%d: Loss: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, (float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
+ if(count%10 == 0){
+ float test_acc = network_accuracy(net, test);
+ printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
+ char buff[256];
+ sprintf(buff, "/home/pjreddie/cifar/cifar2_%d.cfg", count);
+ save_network(net, buff);
+ }else{
+ printf("%d: Loss: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, (float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
+ }
}
free_data(train);
}
@@ -281,10 +347,10 @@
void test_nist_single()
{
srand(222222);
- network net = parse_network_cfg("cfg/nist.cfg");
+ network net = parse_network_cfg("cfg/nist_single.cfg");
data train = load_categorical_data_csv("data/mnist/mnist_tiny.csv", 0, 10);
normalize_data_rows(train);
- float loss = train_network_sgd(net, train, 5);
+ float loss = train_network_sgd(net, train, 1);
printf("Loss: %f, LR: %f, Momentum: %f, Decay: %f\n", loss, net.learning_rate, net.momentum, net.decay);
}
@@ -292,25 +358,43 @@
void test_nist()
{
srand(222222);
+ network net = parse_network_cfg("cfg/nist_final.cfg");
+ data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
+ translate_data_rows(test, -144);
+ clock_t start = clock(), end;
+ float test_acc = network_accuracy_multi(net, test,16);
+ end = clock();
+ printf("Accuracy: %f, Time: %lf seconds\n", test_acc,(float)(end-start)/CLOCKS_PER_SEC);
+}
+
+void train_nist()
+{
+ srand(222222);
network net = parse_network_cfg("cfg/nist.cfg");
data train = load_categorical_data_csv("data/mnist/mnist_train.csv", 0, 10);
data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
- translate_data_rows(train, -144);
- scale_data_rows(train, 1./128);
- translate_data_rows(test, -144);
- scale_data_rows(test, 1./128);
+ translate_data_rows(train, -144);
+ //scale_data_rows(train, 1./128);
+ translate_data_rows(test, -144);
+ //scale_data_rows(test, 1./128);
//randomize_data(train);
int count = 0;
//clock_t start = clock(), end;
int iters = 10000/net.batch;
- while(++count <= 100){
+ while(++count <= 2000){
clock_t start = clock(), end;
float loss = train_network_sgd(net, train, iters);
end = clock();
float test_acc = network_accuracy(net, test);
//float test_acc = 0;
printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
- //save_network(net, "cfg/nist_basic_trained.cfg");
+ /*printf("%f %f %f %f %f\n", mean_array(get_network_output_layer(net,0), 100),
+ mean_array(get_network_output_layer(net,1), 100),
+ mean_array(get_network_output_layer(net,2), 100),
+ mean_array(get_network_output_layer(net,3), 100),
+ mean_array(get_network_output_layer(net,4), 100));
+ */
+ //save_network(net, "cfg/nist_final2.cfg");
//printf("%5d Training Loss: %lf, Params: %f %f %f, ",count*1000, loss, lr, momentum, decay);
//end = clock();
@@ -770,8 +854,11 @@
//test_split();
//test_ensemble();
//test_nist_single();
- test_nist();
+ //test_nist();
+ train_nist();
+ //test_convolutional_layer();
//test_cifar10();
+ //train_cifar10();
//test_vince();
//test_full();
//tune_VOC();
--
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