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np.dot(self.weights[i], activations[i]) + self.biases[i] activation = self.sigmoid(z) activations.append(activation) return activations[-1] def backpropagate(self, inputs, expected_outputs, learning_rate): activations = self.forward_propagate(inputs) # Calculate output layer error output_error =