关于#numpy#的问题，如何解决？

from numpy import exp, array, random, dot

class NeuronLayer():
def init(self, number_of_neurons, number_of_inputs_per_neuron):
self.synaptic_weights = 2 * random.random((number_of_inputs_per_neuron, number_of_neurons)) - 1

class NeuralNetwork():
def init(self, layer1, layer2):
self.layer1 = layer1
self.layer2 = layer2

def __sigmoid(self, x):
return 1 / (1 + exp(-x))

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

def train(self, training_set_inputs, training_set_outputs, number_of_training_iterations):
for iteration in range(number_of_training_iterations):
output_from_layer_1, output_from_layer_2 = self.think(training_set_inputs)

    layer2_error = training_set_outputs - output_from_layer_2
    layer2_delta = layer2_error * self.__sigmoid_derivative(output_from_layer_2)

    layer1_error = layer2_delta.dot(self.layer2.synaptic_weights.T)
    layer1_delta = layer1_error * self.__sigmoid_derivative(output_from_layer_1)

    layer1_adjustment = training_set_inputs.T.dot(layer1_delta)
    layer2_adjustment = output_from_layer_1.T.dot(layer2_delta)

    self.layer1.synaptic_weights += layer1_adjustment
    self.layer2.synaptic_weights += layer2_adjustment

def think(self, inputs):
output_from_layer1 = self.__sigmoid(dot(inputs, self.layer1.synaptic_weights))
output_from_layer2 = self.__sigmoid(dot(output_from_layer1, self.layer2.synaptic_weights))
return output_from_layer1, output_from_layer2

def print_weights(self):
print(" Layer 1 (4 neurons, each with 3 inputs):")
print(self.layer1.synaptic_weights)
print(" Layer 2 (1 neuron, with 4 inputs):")
print(self.layer2.synaptic_weights)

if name == "main":
random.seed(1)

layer1 = NeuronLayer(4, 3)

layer2 = NeuronLayer(1, 4)

neural_network = NeuralNetwork(layer1, layer2)

print("Stage 1) Random starting synaptic weights:")
neural_network.print_weights()

training_set_inputs = array([[0, 0, 1], [0, 1, 1], [1, 0, 1], [0, 1, 0], [1, 0, 0], [1, 1, 1], [0, 0, 0]])
training_set_outputs = array([[0, 1, 1, 1, 1, 0, 0]]).T

neural_network.train(training_set_inputs, training_set_outputs, 60000)

print("Stage 2) New synaptic weights after training:")
neural_network.print_weights()

print("Stage 3) Considering a new situation [1, 1, 0] -> ?:")
hidden_state, output = neural_network.think(array([1, 1, 0]))
print(output)

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