Refactor forward propagation code again

src/CMakeLists.txt

@@ -4,6 +4,7 @@ set(HEADER_FILES
     ./activation_function.hpp
     ./neural_net.hpp
     ./utility.hpp
+    ./forward_feed.hpp
 )
 set(SOURCE_FILES
 )

src/cost_function.hpp

@@ -0,0 +1,16 @@
+#include <cmath>
+#include <vector>
+
+/** Categorical cross entropy loss function for multi category categorization
+ * tasks
+ *
+ */
+struct CategoricalCrossEntropy {
+  float static loss(std::vector<float> y, std::vector<float> yhat) {
+    float loss = 0;
+    for (int i; i < y.size(); i++) {
+      loss += y[i] * log(yhat[i]);
+    }
+    return loss;
+  }
+};

src/forward_feed.hpp

@@ -0,0 +1,38 @@
+/** Apply forward feeding to a fully connect neural network.
+ * This struct stores the final output as well as the activations that occur
+ * for use in backpropagation
+ *
+ */
+#include "activation_function.hpp"
+#include "matrix.hpp"
+#include <vector>
+
+template <class ActivationFunction> struct ForwardFeed {
+  std::vector<Matrix<float>> m_activations;
+  std::vector<float> m_yhat;
+
+  ForwardFeed(const std::vector<float> &x,
+              const std::vector<Matrix<float>> &weights) {
+    // Convert input vector to matrix
+    Matrix<float> A = Matrix<float>(x.size(), 1, x);
+
+    // Feed each layer forward except the last layer using the user specified
+    // activation function
+    m_activations.reserve(weights.size());
+    for (size_t i = 0; i < weights.size() - 1; i++) {
+      // Calculate Z = W * A
+      Matrix Z = weights[i] * A;
+
+      // Apply activation function
+      ActivationFunction::apply(Z.data());
+      m_activations.push_back(A);
+      A = Z;
+    }
+
+    // Always use soft max for the final layer
+    Matrix Z = weights.back() * A;
+    SoftMax::apply(Z.data());
+
+    m_yhat = Z.data();
+  };
+};

src/matrix.hpp

@@ -13,7 +13,7 @@ public:
   Matrix(size_t rows, size_t cols, T value)
       : m_rows(rows), m_cols(cols), m_data(rows * cols, value) {}
 
-  // Create a matrix from a 1d vector using move semantics
+  // Create a matrix from a 1d vector
   Matrix(size_t rows, size_t cols, std::vector<T> data)
       : m_rows(rows), m_cols(cols), m_data(data) {
 

src/neural_net.hpp

@@ -1,12 +1,11 @@
 #ifndef NEURAL_NET_H
 #define NEURAL_NET_H
 
-#include "activation_function.hpp"
 #include "matrix.hpp"
 #include <random>
 #include <vector>
 
-template <class ActivationFunction> class NeuralNet {
+template <class ActivationFunction, class LossFunction> class NeuralNet {
 public:
   NeuralNet(std::vector<size_t> &layer_sizes) : m_sizes(layer_sizes) {
     // Create random sampling device
@@ -59,40 +58,9 @@ public:
     m_weights = new_weights;
   };
 
-  /** Pass input vector through the neural network.
-   * This is a fully connected neural network geometry.
-   * @param x Input vector
-   * @return output of feed forward phase
-   */
-  std::vector<float> feed_forward(const std::vector<float> &x) {
-    // Convert input vector to matrix
-    Matrix<float> A = Matrix<float>(x.size(), 1, x);
-
-    // Feed each layer forward except the last layer using the user specified
-    // activation function
-    for (size_t i = 0; i < m_sizes.size() - 2; i++) {
-      // Calculate Z = W * A
-      Matrix Z = m_weights[i] * A;
-
-      // Apply activation function
-      ActivationFunction::apply(Z.data());
-      A = Z;
-    }
-
-    // Always use soft max for the final layer
-    Matrix Z = m_weights.back() * A;
-    SoftMax::apply(Z.data());
-
-    // Convert final output to vector
-    std::vector<float> output(Z.rows());
-    for (size_t i = 0; i < Z.rows(); i++) {
-      output[i] = Z(i, 0);
-    }
-    return output;
-  };
-
 private:
   std::vector<size_t> m_sizes;
   std::vector<Matrix<float>> m_weights;
 };
+
 #endif

tests/unit_tests/CMakeLists.txt

@@ -1,8 +1,8 @@
 include_directories(${gtest_SOURCE_DIR}/include ${gtest_SOURCE_DIR})
 
 set(TEST_SOURCES
-    test_activation_functions.cpp
-    test_neural_net.cpp
+    ./test_activation_functions.cpp
+    ./test_feed_forward.cpp
 )
 
 add_executable(Unit_Tests_run

tests/unit_tests/test_feed_forward.cpp

@@ -0,0 +1,134 @@
+#include "../../src/activation_function.hpp"
+#include "../../src/forward_feed.hpp"
+#include "../../src/matrix.hpp"
+#include <gtest/gtest.h>
+#include <stdexcept>
+#include <vector>
+
+class ForwardFeedTest : public ::testing::Test {
+protected:
+  void SetUp() override {
+    // Create simple weights for testing
+    weights = {Matrix<float>(2, 2, {0.5, 0.5, 0.5, 0.5}),
+               Matrix<float>(2, 2, {0.5, 0.5, 0.5, 0.5})};
+  }
+
+  std::vector<Matrix<float>> weights;
+};
+
+TEST_F(ForwardFeedTest, BasicForwardFeed) {
+
+  // Create input data
+  std::vector<float> input = {1.0, 2.0};
+
+  // Create ForwardFeed with ReLU activation
+  ForwardFeed<ReLU> feed(input, weights);
+
+  // Verify output size
+  EXPECT_EQ(feed.m_yhat.size(), 2);
+
+  // Verify number of activations stored
+  EXPECT_EQ(feed.m_activations.size(), 1); // Only one hidden layer
+
+  // Verify input was stored as first activation
+  EXPECT_EQ(feed.m_activations[0].rows(), 2);
+  EXPECT_EQ(feed.m_activations[0].cols(), 1);
+  EXPECT_FLOAT_EQ(feed.m_activations[0](0, 0), 1.0);
+  EXPECT_FLOAT_EQ(feed.m_activations[0](1, 0), 2.0);
+}
+
+TEST_F(ForwardFeedTest, DifferentActivationFunctions) {
+  // Test with different activation functions
+  std::vector<float> input = {1.0, 2.0};
+
+  // Test with Sigmoid
+  ForwardFeed<Sigmoid> sigmoid_feed(input, weights);
+  EXPECT_EQ(sigmoid_feed.m_yhat.size(), 2);
+
+  // Test with ReLU
+  ForwardFeed<ReLU> relu_feed(input, weights);
+  EXPECT_EQ(relu_feed.m_yhat.size(), 2);
+
+  // Test with different input values
+  std::vector<float> neg_input = {-1.0, -2.0};
+  ForwardFeed<ReLU> neg_feed(neg_input, weights);
+  EXPECT_EQ(neg_feed.m_yhat.size(), 2);
+}
+
+TEST_F(ForwardFeedTest, ActivationStorage) {
+  // Test that activations are properly stored
+  std::vector<float> input = {1.0, 2.0};
+  ForwardFeed<ReLU> feed(input, weights);
+
+  // Verify first activation (input)
+  EXPECT_EQ(feed.m_activations[0].rows(), 2);
+  EXPECT_EQ(feed.m_activations[0].cols(), 1);
+  EXPECT_FLOAT_EQ(feed.m_activations[0](0, 0), 1.0);
+  EXPECT_FLOAT_EQ(feed.m_activations[0](1, 0), 2.0);
+
+  // Verify final output (after softmax)
+  EXPECT_EQ(feed.m_yhat.size(), 2);
+  float sum = 0.0;
+  for (float val : feed.m_yhat) {
+    sum += val;
+  }
+  EXPECT_NEAR(sum, 1.0, 1e-6); // Softmax outputs should sum to 1
+}
+
+TEST_F(ForwardFeedTest, EdgeCases) {
+  // Test with zero input
+  std::vector<float> zero_input = {0.0, 0.0};
+  ForwardFeed<ReLU> zero_feed(zero_input, weights);
+  EXPECT_EQ(zero_feed.m_yhat.size(), 2);
+
+  // Test with negative input
+  std::vector<float> neg_input = {-1.0, -2.0};
+  ForwardFeed<ReLU> neg_feed(neg_input, weights);
+  EXPECT_EQ(neg_feed.m_yhat.size(), 2);
+
+  // Test with large input
+  std::vector<float> large_input = {100.0, 200.0};
+  ForwardFeed<ReLU> large_feed(large_input, weights);
+  EXPECT_EQ(large_feed.m_yhat.size(), 2);
+}
+
+TEST_F(ForwardFeedTest, DifferentNetworkSizes) {
+  // Test with different network architectures
+  std::vector<std::vector<Matrix<float>>> test_weights = {
+      // Single hidden layer
+      {Matrix<float>(2, 2, {0.5, 0.5, 0.5, 0.5}),
+       Matrix<float>(2, 2, {0.5, 0.5, 0.5, 0.5})},
+      // Multiple hidden layers
+      {Matrix<float>(3, 2, {0.5, 0.5, 0.5, 0.5, 0.5, 0.5}),
+       Matrix<float>(2, 3, {0.5, 0.5, 0.5, 0.5, 0.5, 0.5}),
+       Matrix<float>(2, 2, {0.5, 0.5, 0.5, 0.5})}};
+
+  for (const auto &w : test_weights) {
+    std::vector<float> input(2, 1.0);
+    ForwardFeed<ReLU> feed(input, w);
+
+    // Verify number of activations matches number of hidden layers
+    EXPECT_EQ(feed.m_activations.size(), w.size() - 1);
+
+    // Verify final output size
+    EXPECT_EQ(feed.m_yhat.size(), w.back().rows());
+  }
+}
+
+TEST_F(ForwardFeedTest, WeightMatrixDimensions) {
+  // Test with invalid weight matrix dimensions
+  std::vector<float> input = {1.0, 2.0};
+
+  // Test with mismatched dimensions
+  std::vector<Matrix<float>> invalid_weights = {
+      Matrix<float>(2, 3, {0.5, 0.5, 0.5, 0.5, 0.5, 0.5}), // 3x2 instead of 2x2
+      Matrix<float>(2, 2, {0.5, 0.5, 0.5, 0.5})};
+
+  EXPECT_THROW(ForwardFeed<ReLU> feed(input, invalid_weights),
+               std::invalid_argument);
+}
+
+int main(int argc, char **argv) {
+  ::testing::InitGoogleTest(&argc, argv);
+  return RUN_ALL_TESTS();
+}

tests/unit_tests/test_neural_net.cpp

@@ -1,119 +0,0 @@
-#include "../src/activation_function.hpp"
-#include "../src/neural_net.hpp"
-#include <cmath>
-#include <gtest/gtest.h>
-#include <stdexcept>
-
-class NeuralNetTest : public ::testing::Test {
-protected:
-  void SetUp() override {
-    // Create a simple neural network with 2 input neurons, 2 hidden neurons,
-    // and 2 output neurons
-    std::vector<size_t> layer_sizes = {2, 2, 2};
-    net = std::make_unique<NeuralNet<Sigmoid>>(layer_sizes);
-  }
-
-  std::unique_ptr<NeuralNet<Sigmoid>> net;
-};
-
-TEST_F(NeuralNetTest, FeedForward_SimpleNetwork) {
-  // Test a simple network with known weights and inputs
-  std::vector<float> input = {0.5f, 0.5f};
-
-  // Set known weights for testing
-  std::vector<Matrix<float>> weights = {
-      Matrix<float>(2, 2, 0.5f), // First layer weights
-      Matrix<float>(2, 2, 0.5f)  // Output layer weights
-  };
-
-  // Replace the network's weights with our test weights
-  net->set_weights(weights);
-
-  // Calculate expected output manually
-  // First layer: Z1 = W1 * X
-  Matrix<float> X(2, 1, 0.0);
-  X(0, 0) = input[0];
-  X(1, 0) = input[1];
-
-  Matrix<float> Z1 = weights[0] * X;
-  // Apply sigmoid activation
-  Sigmoid::apply(Z1.data());
-
-  // Second layer: Z2 = W2 * A1
-  Matrix<float> Z2 = weights[1] * Z1;
-  SoftMax::apply(Z2.data());
-
-  // Convert to output vector
-  std::vector<float> expected_output(Z2.cols());
-  for (size_t i = 0; i < Z2.rows(); i++) {
-    expected_output[i] = Z2(i, 0);
-  }
-
-  // Get actual output from feed_forward
-  std::vector<float> output = net->feed_forward(input);
-
-  // Compare actual and expected outputs
-  for (size_t i = 0; i < output.size(); i++) {
-    EXPECT_NEAR(output[i], expected_output[i], 1e-6);
-  }
-}
-
-TEST_F(NeuralNetTest, FeedForward_DifferentLayerSizes) {
-  // Create a network with different layer sizes
-  std::vector<size_t> layer_sizes = {3, 4, 2};
-  NeuralNet<Sigmoid> net2(layer_sizes);
-
-  std::vector<float> input = {0.1f, 0.2f, 0.3f};
-  std::vector<float> output = net2.feed_forward(input);
-
-  // Output should have 2 elements (size of last layer)
-  EXPECT_EQ(output.size(), 2);
-}
-
-TEST_F(NeuralNetTest, FeedForward_InvalidInputSize) {
-  std::vector<float> input = {0.1f}; // Only 1 input, but network expects 2
-
-  // This should throw an exception since input size doesn't match first layer
-  // size
-  EXPECT_THROW(net->feed_forward(input), std::invalid_argument);
-}
-
-TEST_F(NeuralNetTest, FeedForward_IdentityTest) {
-  // Create a network with identity weights (1.0) and no bias
-  std::vector<size_t> layer_sizes = {2, 2};
-  NeuralNet<Sigmoid> net2(layer_sizes);
-
-  // Set weights to identity matrix
-  std::vector<Matrix<float>> weights = {Matrix<float>(2, 2, 1.0f)};
-
-  net2.set_weights(weights);
-
-  std::vector<float> input = {0.5f, 0.5f};
-  std::vector<float> output = net2.feed_forward(input);
-
-  // Since we're using sigmoid activation, the output should be
-  // sigmoid(0.5 + 0.5) = sigmoid(1.0) for each neuron
-  std::vector<float> expected_output = input;
-  SoftMax::apply(expected_output);
-
-  for (float val : output) {
-    EXPECT_NEAR(val, expected_output[0], 1e-6);
-  }
-}
-
-TEST_F(NeuralNetTest, FeedForward_SoftmaxOutput) {
-  std::vector<float> input = {1.0f, -1.0f};
-  std::vector<float> output = net->feed_forward(input);
-
-  // Verify that the output sums to 1 (property of softmax)
-  float sum = 0.0f;
-  for (float val : output) {
-    sum += val;
-  }
-  EXPECT_NEAR(sum, 1.0f, 1e-6);
-
-  // Verify that all outputs are positive
-  for (float val : output) {
-    EXPECT_GT(val, 0.0f);
-  }
-}