neuralnet.py

import numpy as np

class NeuralNetwork:
    def __init__(self, input_size, hidden_size, output_size, learning_rate):
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.learning_rate = learning_rate

        # Initialize weights and biases with random values
        self.weights_input_hidden = np.random.randn(self.input_size, self.hidden_size)
        self.bias_hidden = np.zeros((1, self.hidden_size))
        self.weights_hidden_output = np.random.randn(self.hidden_size, self.output_size)
        self.bias_output = np.zeros((1, self.output_size))

    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))

    def sigmoid_derivative(self, x):
        return x * (1 - x)

    def forward(self, X):
        self.hidden_input = np.dot(X, self.weights_input_hidden) + self.bias_hidden
        self.hidden_output = self.sigmoid(self.hidden_input)
        self.output_input = np.dot(self.hidden_output, self.weights_hidden_output) + self.bias_output
        self.output = self.sigmoid(self.output_input)
        return self.output

    def backward(self, X, y, output):
        error_output = y - output
        delta_output = error_output * self.sigmoid_derivative(output)

        error_hidden = delta_output.dot(self.weights_hidden_output.T)
        delta_hidden = error_hidden * self.sigmoid_derivative(self.hidden_output)

        self.weights_hidden_output += self.hidden_output.T.dot(delta_output) * self.learning_rate
        self.bias_output += np.sum(delta_output, axis=0, keepdims=True) * self.learning_rate
        self.weights_input_hidden += X.T.dot(delta_hidden) * self.learning_rate
        self.bias_hidden += np.sum(delta_hidden, axis=0, keepdims=True) * self.learning_rate

    def train(self, X, y, epochs):
        for epoch in range(epochs):
            output = self.forward(X)
            self.backward(X, y, output)
            loss = np.mean(np.square(y - output))
            if epoch % 1000 == 0:
                print(f"Epoch {epoch}, Loss: {loss:.4f}")

# Sample data for XOR problem
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y = np.array([[0], [1], [1], [0]])

# Create a neural network
nn = NeuralNetwork(input_size=2, hidden_size=4, output_size=1, learning_rate=0.1)

# Train the neural network
nn.train(X, y, epochs=10000)

# Test the trained neural network
test_output = nn.forward(X)
print("Predicted Output:")
print(test_output)