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https://github.com/The-Art-of-Hacking/h4cker.git
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63 lines
2.7 KiB
Markdown
63 lines
2.7 KiB
Markdown
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import numpy as np
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import matplotlib.pyplot as plt
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# Create a simple neural network with one input layer, one hidden layer, and one output layer
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class NeuralNetwork:
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def __init__(self):
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self.weights1 = np.random.rand(3, 4) # weight matrix between input and hidden layer
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self.weights2 = np.random.rand(4, 1) # weight matrix between hidden and output layer
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self.bias1 = np.random.rand(1, 4) # bias matrix for hidden layer
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self.bias2 = np.random.rand(1, 1) # bias matrix for output layer
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def sigmoid(self, x):
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# Sigmoid activation function
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return 1 / (1 + np.exp(-x))
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def forward_propagation(self, X):
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# Perform forward propagation
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self.hidden_layer = self.sigmoid(np.dot(X, self.weights1) + self.bias1) # calculate hidden layer activations
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self.output_layer = self.sigmoid(np.dot(self.hidden_layer, self.weights2) + self.bias2) # calculate output layer activations
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return self.output_layer
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def backward_propagation(self, X, y, output):
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# Perform backward propagation to update weights and biases
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self.error = y - output # calculate error
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self.delta_output = self.error * (output * (1 - output)) # calculate output gradient
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self.delta_hidden = np.dot(self.delta_output, self.weights2.T) * (self.hidden_layer * (1 - self.hidden_layer)) # calculate hidden gradient
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self.weights2 += np.dot(self.hidden_layer.T, self.delta_output) # update weights between hidden and output layer
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self.weights1 += np.dot(X.T, self.delta_hidden) # update weights between input and hidden layer
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self.bias2 += np.sum(self.delta_output, axis=0) # update bias for output layer
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self.bias1 += np.sum(self.delta_hidden, axis=0) # update bias for hidden layer
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def train(self, X, y, epochs):
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# Train the neural network
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for _ in range(epochs):
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output = self.forward_propagation(X)
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self.backward_propagation(X, y, output)
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def predict(self, X):
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# Make predictions
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return self.forward_propagation(X)
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# Create a sample dataset for XOR gate
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X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
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y = np.array([[0], [1], [1], [0]])
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# Create and train the neural network
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nn = NeuralNetwork()
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nn.train(X, y, epochs=10000)
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# Make predictions on the same dataset
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predictions = nn.predict(X)
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# Print the predictions
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print("Predictions:")
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for i in range(len(predictions)):
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print(f"Input: {X[i]}, Predicted Output: {predictions[i]}")
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# Plot the predictions
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plt.scatter(X[:, 0], X[:, 1], c=predictions.flatten(), cmap='viridis')
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plt.xlabel("Input 1")
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plt.ylabel("Input 2")
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plt.title("Neural Network Predictions for XOR Gate")
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plt.show()
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