commit d3b6ee7546635fe8cf18969329bdf6af048963ee
parent a92faf16c14b08ff92066b0630f71474b9fbe3a1
Author: David Freifeld <freifeld.david@gmail.com>
Date: Wed, 15 Jul 2020 16:40:00 -0700
CNN pooling is broken
Diffstat:
2 files changed, 22 insertions(+), 19 deletions(-)
diff --git a/example.cpp b/example.cpp
@@ -14,7 +14,7 @@
double bench(int batch_sz)
{
auto start = std::chrono::high_resolution_clock::now();
- Network net ("./data_banknote_authentication.txt", batch_sz, 0.1, 0.03, 0, 0.9);
+ Network net ("./data_banknote_authentication.txt", batch_sz, 0.05, 0.03, 0, 0.9);
net.add_layer(4, "linear");
net.add_layer(6, "lecun_tanh");
net.add_layer(2, "linear");
diff --git a/src/cnn.cpp b/src/cnn.cpp
@@ -105,10 +105,9 @@ public:
};
ConvLayer::ConvLayer(int x, int y, int stride, int kern_x, int kern_y, int pad)
+ :padding(pad), stride_len(stride)
{
- padding = pad;
pad*=2;
- stride_len = stride;
input = new Eigen::MatrixXf (x+pad,y+pad);
for (int i = 0; i < (x+pad)*(y+pad); i++) {
(*input)((int)i / (y+pad),i%(y+pad)) = 0;
@@ -126,8 +125,9 @@ ConvLayer::ConvLayer(int x, int y, int stride, int kern_x, int kern_y, int pad)
void ConvLayer::convolute()
{
- for (int i = 0; i < input->cols() - kernel->cols()+1; i+=stride_len) {
- for (int j = 0; j < input->rows() - kernel->rows()+1; j+=stride_len) {
+ for (int i = 0; i < output->cols(); i+=stride_len) {
+ for (int j = 0; j < output->rows(); j+=stride_len) {
+ std::cout << j << ","<< i<< " vs "<< output->rows() << "," << output->cols() << "\n";
(*output)(j, i) = (*kernel * (input->block(j, i, kernel->rows(), kernel->cols()))).sum();
}
}
@@ -154,9 +154,12 @@ public:
// Will eventually be different from ConvLayer
PoolingLayer::PoolingLayer(int x, int y, int stride, int kern_x, int kern_y, int pad)
+ :padding(pad), stride_len(stride)
{
- padding = pad;
- stride_len = stride;
+ input = new Eigen::MatrixXf (x+pad,y+pad);
+ for (int i = 0; i < (x+pad)*(y+pad); i++) {
+ (*input)((int)i / (y+pad),i%(y+pad)) = 0;
+ }
kernel = new Eigen::MatrixXf (kern_x, kern_y);
for (int i = 0; i < kern_x*kern_y; i++) {
(*kernel)((int)i / kern_y,i%kern_y) = (float) rand()/RAND_MAX;
@@ -247,13 +250,13 @@ void ConvNet::process()
// Assumes pooling is immediately after any conv layer.
for (int i = 0; i < preprocess_length-1; i++) {
conv_layers[i].convolute();
- // pool_layers[i].input = conv_layers[i].output;
- // pool_layers[i].pool();
+ pool_layers[i].input = conv_layers[i].output;
+ pool_layers[i].pool();
conv_layers[i+1].input = conv_layers[i].output;
}
conv_layers[preprocess_length-1].convolute();
- //pool_layers[preprocess_length-1].input = conv_layers[preprocess_length-1].output;
- //pool_layers[preprocess_length-1].pool();
+ pool_layers[preprocess_length-1].input = conv_layers[preprocess_length-1].output;
+ pool_layers[preprocess_length-1].pool();
// std::cout << "Output:\n" << *pool_layers[preprocess_length-1].output << "\n\n";
Eigen::Map<Eigen::RowVectorXf> flattened (conv_layers[preprocess_length-1].output->data(), conv_layers[preprocess_length-1].output->size());
// std::cout << "Flattened:\n" << flattened << "\n\n";
@@ -271,7 +274,7 @@ void ConvNet::list_net()
{
for (int i = 0; i < preprocess_length; i++) {
std::cout << "-----------------------\nCONVOLUTIONAL LAYER " << i << "\n-----------------------\n\n\u001b[31mGENERAL INFO:\x1B[0;37m\nStride: " << conv_layers[i].stride_len << "\nPadding: " << conv_layers[i].padding << "\n\n\u001b[31mINPUT:\x1B[0;37m\n" << *conv_layers[i].input << "\n\n\u001b[31mKERNEL:\x1B[0;37m\n" << *conv_layers[i].kernel << "\n\n\u001b[31mOUTPUT:\x1B[0;37m\n" << *conv_layers[i].output << "\n\n\u001b[31mBIAS:\x1B[0;37m\n" << conv_layers[i].bias << "\n\n\n";
- //std::cout << "-----------------------\nPOOLING LAYER " << i << "\n-----------------------\n\n\u001b[31mGENERAL INFO:\x1B[0;37m\nStride: " << pool_layers[i].stride_len << "\nPadding: " << conv_layers[i].padding << "\n\n\u001b[31mINPUT:\x1B[0;37m\n" << *pool_layers[i].input << "\n\n\u001b[31mKERNEL:\x1B[0;37m\n-" << *pool_layers[i].kernel << "\n\n\u001b[31mOUTPUT:\x1B[0;37m\n" << *pool_layers[i].output << "\n\n\n";
+ std::cout << "-----------------------\nPOOLING LAYER " << i << "\n-----------------------\n\n\u001b[31mGENERAL INFO:\x1B[0;37m\nStride: " << pool_layers[i].stride_len << "\nPadding: " << conv_layers[i].padding << "\n\n\u001b[31mINPUT:\x1B[0;37m\n" << *pool_layers[i].input << "\n\n\u001b[31mKERNEL:\x1B[0;37m\n-" << *pool_layers[i].kernel << "\n\n\u001b[31mOUTPUT:\x1B[0;37m\n" << *pool_layers[i].output << "\n\n\n";
}
std::cout << "-----------------------\nINPUT LAYER (LAYER 0)\n-----------------------\n\n\u001b[31mGENERAL INFO:\x1B[0;37m\nActivation Function: " << layers[0].activation_str << "\n\n\u001b[31mACTIVATIONS:\x1B[0;37m\n" << *layers[0].contents << "\n\n\u001b[31mWEIGHTS:\x1B[0;37m\n" << *layers[0].weights << "\n\n\u001b[31mBIASES:\x1B[0;37m\n" << *layers[0].bias << "\n\n\n";
for (int i = 1; i < length-1; i++) {
@@ -375,19 +378,19 @@ int main()
{
ConvNet net ("../data_banknote_authentication.txt", 0.05, 0.01, 5, 0.9);
Eigen::MatrixXf labels (1,1);
- labels << 2;
- net.set_label(labels);
- net.add_conv_layer(28,28,1,15,15,0);
- net.add_conv_layer(14,14,1,8,8,0);
- // net.add_pool_layer(5,5,1,2,0);
- net.add_layer(49, "sigmoid");
+ net.add_conv_layer(28,28,1,9,9,0);
+ net.add_pool_layer(20,20,1,6,6,0);
+ net.add_conv_layer(15,15,1,6,6,0);
+ net.add_pool_layer(10,10,1,2,2,0);
+ net.add_layer(81, "sigmoid");
net.add_layer(5, "lecun_tanh");
net.add_layer(10, "resig");
+ // net.list_net();
// net.init_decay("step", 1, 2);
net.initialize();
//net.list_net();
- for (int i = 0; i < 5; i++) {
+ for (int i = 0; i < 1; i++) {
net.train();
}
net.list_net();
