commit eeccc528d0ba582a703025f1452ce938669e77ff
parent 39dc88911e59f80e5654037a504d7e4a88c11606
Author: David Freifeld <freifeld.david@gmail.com>
Date: Sat, 11 Jul 2020 11:39:06 -0700
CNN with custom kernel now compiles
Diffstat:
| M | src/cnn.cpp | | | 82 | ++++++++++++++++++++++++++++++++++++++++---------------------------------------- |
1 file changed, 41 insertions(+), 41 deletions(-)
diff --git a/src/cnn.cpp b/src/cnn.cpp
@@ -8,14 +8,14 @@ class ConvLayer
public:
int stride_len;
int padding;
- Eigen::MatrixXd* input;
- Eigen::MatrixXd* kernel;
- Eigen::MatrixXd* output;
- double bias;
+ Eigen::MatrixXf* input;
+ Eigen::MatrixXf* kernel;
+ Eigen::MatrixXf* output;
+ float bias;
ConvLayer(int x, int y, int stride, int kern_x, int kern_y, int pad);
void convolute();
- void set_input(Eigen::MatrixXd* matrix);
+ void set_input(Eigen::MatrixXf* matrix);
};
ConvLayer::ConvLayer(int x, int y, int stride, int kern_x, int kern_y, int pad)
@@ -23,15 +23,15 @@ ConvLayer::ConvLayer(int x, int y, int stride, int kern_x, int kern_y, int pad)
padding = pad;
pad*=2;
stride_len = stride;
- input = new Eigen::MatrixXd (x+pad,y+pad);
+ 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::MatrixXd (kern_size, kern_size);
- for (int i = 0; i < kern_size*kern_size; i++) {
- (*kernel)((int)i / kern_size,i%kern_size) = (double) rand() / RAND_MAX;
+ 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;
}
- output = new Eigen::MatrixXd ((x-kern_size+1+pad/stride_len), (y-kern_size+1+pad/stride_len)); // We're using valid padding for now.
+ output = new Eigen::MatrixXf ((x-kern_x+1+pad/stride_len), (y-kern_y+1+pad/stride_len));
for (int i = 0; i < (x-kern_y+1+pad/stride_len)*(y-kern_x+1+pad/stride_len); i++) {
(*output)((int)i / (y-kern_y+1+pad/stride_len),i%(y-kern_y+1+pad/stride_len)) = 0;
}
@@ -48,7 +48,7 @@ void ConvLayer::convolute()
*output = (output->array() + bias).matrix();
}
-void ConvLayer::set_input(Eigen::MatrixXd* matrix)
+void ConvLayer::set_input(Eigen::MatrixXf* matrix)
{
input->block(padding, padding, matrix->rows(), matrix->cols()) = *matrix;
}
@@ -58,26 +58,26 @@ class PoolingLayer
public:
int stride_len;
int padding;
- Eigen::MatrixXd* input;
- Eigen::MatrixXd* kernel;
- Eigen::MatrixXd* output;
+ Eigen::MatrixXf* input;
+ Eigen::MatrixXf* kernel;
+ Eigen::MatrixXf* output;
void pool();
- PoolingLayer(int x, int y, int stride, int kern_size, int pad);
+ PoolingLayer(int x, int y, int stride, int kern_x, int kern_y, int pad);
};
// Will eventually be different from ConvLayer
-PoolingLayer::PoolingLayer(int x, int y, int stride, int kern_size, int pad)
+PoolingLayer::PoolingLayer(int x, int y, int stride, int kern_x, int kern_y, int pad)
{
padding = pad;
stride_len = stride;
- kernel = new Eigen::MatrixXd (kern_size, kern_size);
- for (int i = 0; i < kern_size*kern_size; i++) {
- (*kernel)((int)i / kern_size,i%kern_size) = (double) rand()/RAND_MAX;
+ 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;
}
- output = new Eigen::MatrixXd (x-kern_size+1, y-kern_size+1);
- for (int i = 0; i < (x-kern_size+1)*(y-kern_size+1); i++) {
- (*output)((int)i / (y-kern_size+1),i%(y-kern_size+1)) = (double) rand()/RAND_MAX;
+ output = new Eigen::MatrixXf (x-kern_x+1, y-kern_y+1);
+ for (int i = 0; i < (x-kern_x+1)*(y-kern_y+1); i++) {
+ (*output)((int)i / (y-kern_y+1),i%(y-kern_y+1)) = (float) rand()/RAND_MAX;
}
};
@@ -106,32 +106,32 @@ public:
std::vector<ConvLayer> conv_layers;
std::vector<PoolingLayer> pool_layers;
- ConvNet(char* path, int batch_sz, float learn_rate, float bias_rate, float ratio);
+ ConvNet(char* path, int batch_sz, float learn_rate, float bias_rate, float l, float ratio);
void list_net();
void process(); // Runs the convolutional and pooling layers.
void backpropagate();
- void add_conv_layer(int x, int y, int stride, int kern_x, int kern_y int pad);
+ void add_conv_layer(int x, int y, int stride, int kern_x, int kern_y, int pad);
void add_pool_layer(int x, int y, int stride, int kern_x, int kern_y, int pad);
- void set_label(Eigen::MatrixXd newlabels);
+ void set_label(Eigen::MatrixXf newlabels);
void initialize();
};
-ConvNet::ConvNet(char* path, int batch_sz, float learn_rate, float bias_rate, float ratio) : Network(path, batch_sz, learn_rate, bias_rate, ratio)
+ConvNet::ConvNet(char* path, int batch_sz, float learn_rate, float bias_rate, float l, float ratio) : Network(path, batch_sz, learn_rate, bias_rate, l, ratio)
{
preprocess_length = 0;
- labels = new Eigen::MatrixXd (batch_sz, 1);
+ labels = new Eigen::MatrixXf (batch_sz, 1);
}
-void ConvNet::add_conv_layer(int x, int y, int stride, int kern_size, int pad)
+void ConvNet::add_conv_layer(int x, int y, int stride, int kern_x, int kern_y, int pad)
{
preprocess_length+=1;
- conv_layers.emplace_back(x,y,stride,kern_size,pad);
+ conv_layers.emplace_back(x,y,stride,kern_x, kern_y,pad);
}
// May make this inaccessible to user code and just have it called from add_conv_layer as pooling is basically always paired with conv.
-void ConvNet::add_pool_layer(int x, int y, int stride, int kern_size, int pad)
+void ConvNet::add_pool_layer(int x, int y, int stride, int kern_x, int kern_y, int pad)
{
- pool_layers.emplace_back(x,y,stride,kern_size,pad);
+ pool_layers.emplace_back(x,y,stride,kern_x,kern_y,pad);
}
void ConvNet::initialize()
@@ -155,14 +155,14 @@ void ConvNet::process()
//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::RowVectorXd> flattened (conv_layers[preprocess_length-1].output->data(), conv_layers[preprocess_length-1].output->size());
+ 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";
for (int i = 0; i < flattened.cols(); i++) {
(*layers[0].contents)(0, i) = flattened[i];
}
}
-void ConvNet::set_label(Eigen::MatrixXd newlabels)
+void ConvNet::set_label(Eigen::MatrixXf newlabels)
{
*labels = newlabels;
}
@@ -182,9 +182,9 @@ void ConvNet::list_net()
void ConvNet::backpropagate()
{
- std::vector<Eigen::MatrixXd> gradients;
- std::vector<Eigen::MatrixXd> deltas;
- Eigen::MatrixXd error = ((*layers[length-1].contents) - (*labels));
+ std::vector<Eigen::MatrixXf> gradients;
+ std::vector<Eigen::MatrixXf> deltas;
+ Eigen::MatrixXf error = ((*layers[length-1].contents) - (*labels));
gradients.push_back(error.cwiseProduct(*layers[length-1].dZ));
deltas.push_back((*layers[length-2].contents).transpose() * gradients[0]);
int counter = 1;
@@ -205,7 +205,7 @@ void ConvNet::backpropagate()
// for (int i = 0; i < gradients.size(); i++) {
// std::cout << gradients[i] << "\n\n";
// }
- Eigen::Map<Eigen::MatrixXd> reshaped(gradients[gradients.size()-1].data(), conv_layers[conv_layers.size()-1].output->rows(),conv_layers[conv_layers.size()-1].output->cols());
+ Eigen::Map<Eigen::MatrixXf> reshaped(gradients[gradients.size()-1].data(), conv_layers[conv_layers.size()-1].output->rows(),conv_layers[conv_layers.size()-1].output->cols());
gradients[gradients.size()-1] = reshaped;
//std::cout << gradients[gradients.size()-1].cols() << " " << conv_layers[0].input->cols() << " " << conv_layers[0].input->cols() - gradients[length-1].cols()+1 << "\n";
for (int i = 0; i < conv_layers[0].input->cols() - gradients[length-1].cols()+1; i+=conv_layers[0].stride_len) {
@@ -218,17 +218,17 @@ void ConvNet::backpropagate()
int main()
{
- ConvNet net ("./data_banknote_authentication.txt", 1, 0.05, 0.01, 0.9);
- Eigen::MatrixXd labels (1,1);
+ ConvNet net ("../data_banknote_authentication.txt", 1, 0.05, 0.01, 0, 0.9);
+ Eigen::MatrixXf labels (1,1);
labels << 1;
net.set_label(labels);
- net.add_conv_layer(8,8,1,4,0);
+ net.add_conv_layer(8,8,1,4,2,0);
//net.add_pool_layer(5,5,1,2,0);
net.add_layer(25, "linear");
net.add_layer(5, "relu");
net.add_layer(1, "resig");
net.initialize();
- Eigen::MatrixXd* input = new Eigen::MatrixXd (8,8);
+ Eigen::MatrixXf* input = new Eigen::MatrixXf (8,8);
*input <<
0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,
