Multi-Layer Perceptron (MLP) Implementation

Overview: This C++ code implements a Multi-Layer Perceptron (MLP) neural network. It consists of input, hidden, and output layers, with the ability to specify the number of hidden layers during runtime. The program utilizes multi-threading for concurrent computation within layers and inter-process communication (IPC) using named pipes for communication between layers.

Dependencies:

• C++11 compiler
• POSIX compliant operating system (Linux/Unix)
• Compile the code using any C++11 compliant compiler: (g++ -o NN NN.cpp -lpthread)
• Run the compiled executable: (./NN)

Features:

• Input Layer: Takes input values and distributes them to the neurons in the layer.
• Hidden Layer: Processes input data using multi-threading and forwards results to subsequent layers.
• Output Layer: Generates output based on processed data from the preceding layers.
• Concurrency: Utilizes pthreads for parallel computation within layers.
• Inter-Process Communication (IPC): Communicates between layers using named pipes for data transfer.
• Backward Propagation: Implements backward propagation for updating weights during training. Classes:
• Neuron: Represents a single neuron in the network with a data value and associated weights.
• Layer: Abstract class representing a layer in the network, with mutex for thread safety.
• InputLayer: Subclass of Layer, initializes input data and distributes it to neurons.
• HiddenLayer: Subclass of Layer, processes data using multi-threading.
• OutputLayer: Subclass of Layer, generates output based on processed data. Functions:
• createHiddenLayers: Creates and initializes hidden layers with specified weights.
• displayHiddenLayers: Displays the neurons and weights in hidden layers.
• computeInnerLayer: Computes the inner layer's output values using multi-threading.
• computeLayer: Computes output for hidden and output layers, implementing forward and backward propagation.
• computeLayerThread: Thread function for computing neuron output values.
• main: Entry point of the program, orchestrates the execution of the MLP.