jacobian

commit 4ef04e671adbedba987f6cea30e3a7f4f1d66e98
parent bec1678d6f27c9c4e89610fe91b9ffb363c837a1
Author: David Freifeld <freifeld.david@gmail.com>
Date:   Tue, 30 Jun 2020 20:11:01 -0700

Added constructors + funcs for adding layers

Diffstat:
M
cnn.cpp
 | 
58
++++++++++++++++++++++++++++++++++++++++++++++++++++++++--

1 file changed, 56 insertions(+), 2 deletions(-)
diff --git a/cnn.cpp b/cnn.cpp
@@ -8,12 +8,29 @@ class ConvLayer
   Eigen::MatrixXd* input;
   Eigen::MatrixXd* kernel;
   Eigen::MatrixXd* output;
+  int stride_len;
 
 public:
-  ConvLayer(int stride) 
+  ConvLayer(int x, int y, int stride, int kernel_size);
   void convolute();
 };
 
+ConvLayer::ConvLayer(int x, int y, int stride, int kern_size)
+{
+  input = new Eigen::MatrixXd (x, y);
+  for (int i = 0; i < x*y; i++) {
+    (*input)((int)i / y,i%y) = 0;
+  }
+  kernel = new Eigen::MatrixXd (kern_size, kern_size);
+  for (int i = 0; i < kern_size*kern_size; i++) {
+    (*input)((int)i / kern_size,i%kern_size) = 0;
+  }
+  output = new Eigen::MatrixXd (kern_size, kern_size); // We're using valid padding for now.
+  for (int i = 0; i < kern_size*kern_size; i++) {
+    (*input)((int)i / kern_size,i%kern_size) = 0;
+  }
+};
+
 void ConvLayer::convolute()
 {
   for (int i = 0; i < input->cols() - kernel->cols() + 1; i+=stride_len) {
@@ -28,9 +45,28 @@ class PoolingLayer
   Eigen::MatrixXd* input;
   Eigen::MatrixXd* kernel;
   Eigen::MatrixXd* output;
-
+  int stride_len;
+  
 public:
   void pool();
+  PoolingLayer(int x, int y, int stride, int kern_size);
+};
+
+// Will eventually be different from ConvLayer
+PoolingLayer::PoolingLayer(int x, int y, int stride, int kern_size)
+{
+  input = new Eigen::MatrixXd (x, y);
+  for (int i = 0; i < x*y; i++) {
+    (*input)((int)i / y,i%y) = 0;
+  }
+  kernel = new Eigen::MatrixXd (kern_size, kern_size);
+  for (int i = 0; i < kern_size*kern_size; i++) {
+    (*input)((int)i / kern_size,i%kern_size) = 0;
+  }
+  output = new Eigen::MatrixXd (kern_size, kern_size);
+  for (int i = 0; i < kern_size*kern_size; i++) {
+    (*input)((int)i / kern_size,i%kern_size) = 0;
+  }
 };
 
 void PoolingLayer::pool()
@@ -62,6 +98,7 @@ public:
   void process(); // Runs the convolutional and pooling layers.
   void next_batch();
   void add_conv_layer();
+  void add_pool_layer();
 };
 
 ConvNet::ConvNet(char* path, int batch_sz, float learn_rate, float bias_rate)
@@ -75,3 +112,20 @@ ConvNet::ConvNet(char* path, int batch_sz, float learn_rate, float bias_rate)
   data = fopen("./shuffled.txt", "r");
   batches = 0;
 }
+
+void add_conv_layer(int x, int y, int stride, int kern_size)
+{
+  preprocess_length++;
+  conv_layers.empace_back(x,y,stride,kern_size);
+}
+
+void add_pool_layer(int x, int y, int stride, int kern_size)
+{
+  preprocess_length++;
+  pool_layers.empace_back(x,y,stride,kern_size);
+}
+
+void process()
+{
+  
+}