README.md

Exercise Denoise Cosine

The goal of this exercise is to implement a multilayer dense neuralnetwork from scratch using C++. Specifically, you will implement gradient descent and use it to learn a cosine function.

• First, take a look and understand array datatype defined in line 8 in src/utils.cpp.
• Further now, code the linear algebra operations such as matrix multplication, addition, subtraction, hadamard (element-wise) product, matrix elements sum, transpose, and matrix power (element-wise) in src/utils.cpp.
• You can check your implementation of functions in src/utils.cpp by running tests with following commands

cd <exercise_folder>/tests
g++ -o test_utils_executable test_utils.cpp
./test_utils_executable

• If you see no output by running the executable, this means the implementation is right.
• Navigate to src/mlutils.cpp and code sigmoid activation function as first step.

$$\sigma (x)=\frac{1}{1+e^{-x}}$$

• In next step, given ground truth and predictions, compute Mean Square Error (MSE) in cost function as follows

$$MSE=\frac{1}{2}\sum (y-h{)}^2$$

• Similarly you can test your implementation of src/mlutils.cpp by executing test_mlutils.cpp.
• Navigate to src/denoise_cosine.cpp with the above custom datatype, declare $W_1,W_2,bias$ variables and intialise the weights using the corresponding initialisation function in src/utils.cpp. Similarly declare the gradient variables.
• Code the forward pass in network function in src/denoise_cosine.cpp and call this forward pass in main function training followed by above implemented loss function.
• As a next step, derive the gradients for each variable and implement in compute_gradients function in src/mlutils.cpp. Note: compute_gradients function needs the above declared gradient variables as arguments and they are pass by reference, so no return type is necessary.
• As a final training step implement the gradient descent step using the following formula.

$$W_{new}=W_{old}-lr\ast \mathrm{\nabla }{W}_{old}$$

• Finally compute the network predictions and assign it to y_hat variable. Now you can run the C++ program by runnig the following commands

cd <exercise_folder>/src
g++ -o denoise_executable denoise_cosing.cpp
./denoise_executable

• Finally, to check our cosine fit, you need to run the following commands

cd <exercise_folder>/src
python plot.py