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An implementation of a neural network from scratch
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import numpy as np | |
def sigmoid(x): | |
return 1 / (1 + np.exp(-x)) | |
def neural_network(X, y): | |
learning_rate = 0.1 | |
W1 = np.random.rand(2, 4) | |
W2 = np.random.rand(4, 1) | |
for epoch in range(10000): | |
layer1 = sigmoid(np.dot(X, W1)) | |
output = sigmoid(np.dot(layer1, W2)) | |
error = (y - output) | |
delta2 = 2 * error * (output * (1 - output)) | |
delta1 = delta2.dot(W2.T) * (layer1 * (1 - layer1)) | |
W2 += learning_rate * layer1.T.dot(delta2) | |
W1 += learning_rate * X.T.dot(delta1) | |
return np.round(output).flatten() | |
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]]) | |
print("OR", neural_network(X, np.array([[0, 1, 1, 1]]).T)) | |
print("AND", neural_network(X, np.array([[0, 0, 0, 1]]).T)) | |
print("XOR", neural_network(X, np.array([[0, 1, 1, 0]]).T)) | |
print("NAND", neural_network(X, np.array([[1, 1, 1, 0]]).T)) | |
print("NOR", neural_network(X, np.array([[1, 0, 0, 0]]).T)) |
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