jacobian

commit bec1678d6f27c9c4e89610fe91b9ffb363c837a1
parent dac772bf6ffacef14787fc5f3e028dd6f9f4a169
Author: David Freifeld <freifeld.david@gmail.com>
Date:   Tue, 30 Jun 2020 19:55:30 -0700

Some fixes, realized CNNs will be formidable

Uh oh more backprop

Diffstat:
M
cnn.cpp
 | 
16
++++++++++------

1 file changed, 10 insertions(+), 6 deletions(-)
diff --git a/cnn.cpp b/cnn.cpp
@@ -10,13 +10,14 @@ class ConvLayer
   Eigen::MatrixXd* output;
 
 public:
+  ConvLayer(int stride) 
   void convolute();
 };
 
 void ConvLayer::convolute()
 {
-  for (int i = 0; i < input->cols() - kernel->cols() + 1; i++) {
-    for (int j = 0; j < input->rows() - kernel->rows() + 1; j++) {
+  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) {
       output(j, i) = (*kernel * input->block<kernel->rows(), kernel->cols()(j, i)).sum()
     }
   }
@@ -36,8 +37,8 @@ void PoolingLayer::pool()
 {
   // It doesn't look like anything better than O(n^4) is doable for this as kernel needs to go through matrix and you need to index kernel. LOOK INTO ME!! 
   float maxnum = -LARGE_NUM;
-  for (int i = 0; i < input->cols() - kernel->cols() + 1; i++) {
-    for (int j = 0; j < input->rows() - kernel->rows() + 1; j++) {
+  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 k = 0; k < kernel->cols(); k++) {
         for (int l = 0; l < kernel->rows(); l++) {
           if ((input->block<kernel->rows(), kernel->cols()(j, i))(l, k) > maxnum) {
@@ -52,18 +53,21 @@ void PoolingLayer::pool()
 class ConvNet : Network
 {
   int stride_len;
+  int preprocess_length;
                 
   std::vector<ConvLayer> conv_layers;
   std::vector<PoolingLayer> pool_layers;
 public:
   ConvNet(char* path, int batch_sz, float learn_rate, float bias_rate);
+  void process(); // Runs the convolutional and pooling layers.
+  void next_batch();
+  void add_conv_layer();
 };
 
-ConvNet::ConvNet(char* path, int batch_sz, float learn_rate, float bias_rate, int stride)
+ConvNet::ConvNet(char* path, int batch_sz, float learn_rate, float bias_rate)
 {
   learning_rate = learn_rate;
   bias_lr = bias_rate;
-  stride_len = stride;
   instances = prep_file(path, "./shuffled.txt");
   length = 0;
   t = 0;