如何修复：（ipython）类名定义和名称错误：名称未定义

Question

我正在尝试构建可以识别数字（0~9）的nuerualnetwork

但由于某些错误，它不起作用。

那我该怎么办？

当我尝试构建该代码时，会出现此错误消息。

---------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-3-bdb6f33f1c38> in <module>
      5 get_ipython().run_line_magic('matplotlib', 'inline')
      6 
----> 7 class neuralNetwork:
      8     def __init__(self, inputnodes, hiddennodes, outputnodes, learningrate):
      9         self.inodes = inputnodes

<ipython-input-3-bdb6f33f1c38> in neuralNetwork()
     56     learing_rate = 0.1
     57 
---> 58     n = neuralNetwork(input_nodes, hidden_nodes, output_nodes, learning_rate)
     59 
     60     training_data_file = open("C:/ProgramData/Anaconda3/pkgs/notebook-6.0.0-py37_0/Lib/site-packages/notebook/mnist/mnist_trian.csv", 'r')

NameError: name 'neuralNetwork' is not defined

导入 numpy 导入 scipy.special 导入 matplotlib.pyplot

%matplotlib 内联

类神经网络： def init(self, inputnodes, hiddennodes, outputnodes, learningrate): self.inodes = 输入节点 self.hnodes = hiddennodes self.onodes = 输出节点 self.lr =学习率

    self.wih = numpy.random.normal(0.0, pow(self.hnodes, -0.5), (self.hnodes, self.inodes))
    self.who = numpy.random.normal(0.0, pow(self.onondes, -0.5), (self.onodes, self,hnodes))

    self.activation_function = lambda x: scipy.special.expit(x)

    pass

def train(self, inputs_list, targets_list):

    inputs = numpy.array(inputs_list, ndmin=2).T
    tagets = numpy.array(targets_list, ndmin=2).T

    hidden_inputs = numpy.dot(self.wih, inputs)
    hidden_outputs = self.activation_function(hidden_inputs)

    final_inputs = numpy.array(self.who, hidden_outputs)
    final_outputs = self.activation_function(final_inputs)

    output_errors = targets - final_outputs
    hidden_errors = numpy.dot(self.who.T, output_errors)

    self.who += self.lr * numpy.dot((output_errors * final_outputs * (1.0 - final_outputs)), numpy.transpose(hidden_outputs))
    self.wih += self.lr * numpy.dot((hidden_errors * hidden_outputs * (1.0 - hidden_outputs)), numpy.transpose(inputs))

    pass

def query(self, inputs_list):

    inputs = numpy.array(inputs_list, ndmin=2).T

    hidden_inputs = numpy.dot(self.wih, inputs)
    hidden_outputs = self.activation_function(hidden_inputs)

    final_inputs = numpy.array(self.who, hidden_outputs)
    final_outputs = self.activation_function(final_inputs)

    return final_outputs

input_nodes = 784
hidden_nodes = 200
output_nodes = 10

learing_rate = 0.1

n = neuralNetwork(input_nodes, hidden_nodes, output_nodes, learning_rate)

training_data_file = open("C:/ProgramData/Anaconda3/pkgs/notebook-6.0.0-py37_0/Lib/site-packages/notebook/mnist/mnist_trian.csv", 'r')
training_data_list = training_data_file.readlines()
traning_data_file.close()

epochs = 5

for e in range(epochs):
    for record in training_data_list:
        all_values = record.split(',')
        inputs - (numpy.asfarray(all_values[1:])/255.0 * 0.99) + 0.01
        targets = numpy.zeros(output_nodes)+0.01
        targets[int(all_values[0])] = 0.99
        n.train(inputs, targets)
        pass
    pass

test_data_file = open("C:/ProgramData/Anaconda3/pkgs/notebook-6.0.0-py37_0/Lib/site-packages/notebook/mnist/mnist_test.csv")
test_data_list = test_data_file.readlines()
test_data_file.close()

scorecard = []

for record in test_data_list:
    all_vales - record.split(',')
    correct_label = int(all_values[0])
    inputs = (numpy.asfarray(all_values[1:]) / 255.0 * 0.99) + 0.01
    outputs - n.query(inputs)
    label = numpy.argmax(outputs)

    if(lavel == correct_label):
        scorecard.append(1)
    else :
        scorecard.append(0)
        pass
    pass

scorecard_array = numpy.asarray(scorecard) print("性能 = ", scorecard_array.sum() / scorecard_array.size)

Answer 1

您正在尝试在类中初始化您的神经网络。确保你的缩进是正确的，并确保你用正确的值初始化你的类（所以不要简单地复制参数名称）