Neural Network Basics Gaming Project in C++

Neural Network Code:

Explanation:

  1. Activation Function:
    • sigmoid(double x): Sigmoid activation function which squashes the input to the range [0, 1].
    • sigmoid_derivative(double x): Derivative of the sigmoid function used in backpropagation.
  2. Neural Network Class:
    • Constructor: Initializes weights and biases randomly for both the hidden and output layers.
    • forward(const std::vector<double>& input): Performs a forward pass through the network to compute the output given an input.
    • train(const std::vector<std::vector<double>>& inputs, const std::vector<std::vector<double>>& targets): Trains the network using backpropagation. It updates the weights and biases based on the error.
  3. Main Function:
    • Defines a simple XOR dataset for training.
    • Creates an instance of NeuralNetwork, trains it on the XOR dataset, and then tests the network with the training data to display the results.

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