Я пытаюсь построить nuerualnetwork, который может идентифицировать число (0 ~ 9)
, но это не работает из-за какой-то ошибки.
так что мне делать?
при попытке создать этот код появляется сообщение об ошибке.
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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
import numpy import scipy.special import matplotlib.pyplot
% matplotlib inline
класс neuralNetwork: def init (self, inputnodes, hiddennodes, outputnodes, learningrate): self.inodes = inputnodes self.hnodes = hiddennodes self.onodes = outputnodes self.lr = уровень обучения
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 ("performance =", Scorecard_array.sum () / Scorecard_array.size)