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num_epochs, learning_rate): for epoch in range(num_epochs): for i in range(len(inputs)): self.backpropagate(inputs[i], expected_outputs[i], learning_rate) def predict(self, inputs): return self.forward_propagate(inputs) def sigmoid(self, x): return 1 / (1 + np.exp(-x)) def sigmoid_derivative(self,