神经网络不进行/不输出

Question

一所大学把他的神经网络代码传给了我。我想训练网络识别图像是技术图纸还是其他东西。因为测试csv文件大约是14GB，我别无选择，只能用pandas实现一个分块读入方法。可悲的是，这样做后，神经网络似乎不再工作了。我的pythonshell告诉我，程序已经启动，但是没有提供任何东西。我等了大约一个小时，没有任何进展。因为在任务管理器中，我的RAM或处理器上没有显示正在处理繁重的任务，所以我建议有些东西不能正常工作。由于没有收到错误信息，我完全不知道下一步该怎么办。 代码如下所示：

import numpy as np
import scipy.special
from tqdm import tqdm
import csv
import pandas as pd

class neuralNetwork:

    def __init__(self, inputnodes, hiddennodes, outputnodes, learningrate):
        self.inode = inputnodes
        self.hnode = hiddennodes
        self.onode = outputnodes
# =============================================================================
#         self.wih=np.random.normal(0.0,pow(self.inode,-0.5),(self.hnode,self.inode))
#         self.who=np.random.normal(0.0,pow(self.hnode,-0.5),(self.onode,self.hnode))
# =============================================================================
        self.wih = np.random.normal(0, pow(self.hnode, -0.5), (self.hnode, self.inode))
        self.who = np.random.normal(0, pow(self.onode, -0.5), (self.onode, self.hnode))
        self.lr = learningrate
        self.activation_function = lambda x: scipy.special.expit(x)
        pass

    def train(self, inputs_list, targets_list):
        inputs = np.array(inputs_list, ndmin=2).T
        targets = np.array(targets_list, ndmin=2).T
        hidden_inputs = np.dot(self.wih, inputs)
        hidden_outputs = self.activation_function(hidden_inputs)
        final_inputs = np.dot(self.who, hidden_outputs)
        final_outputs = self.activation_function(final_inputs)
        output_errors = targets - final_outputs
        hidden_errors = np.dot(self.who.T, output_errors) 
        self.who += self.lr * np.dot((output_errors * final_outputs 
        * (1.0 - final_outputs)), np.transpose(hidden_outputs))
        self.wih += self.lr * np.dot((hidden_errors * hidden_outputs 
        * (1.0 - hidden_outputs)), np.transpose(inputs))
        pass

    def test(self, inputs_list):
        inputs = np.array(inputs_list, ndmin=2).T
        hidden_inputs = np.dot(self.wih, inputs)
        hidden_outputs = self.activation_function(hidden_inputs)
        final_inputs = np.dot(self.who, hidden_outputs)
        final_outputs = self.activation_function(final_inputs)
        return final_outputs

input_nodes = 784
hidden_nodes = 500
output_nodes = 10
learning_rate = 0.1
epochs = 5
chunksize = 10**8

n = neuralNetwork(input_nodes,hidden_nodes,output_nodes, learning_rate)
#training_data_file = open('C:/Users/Anwender/Documents/Uni/KI/Python/train.csv', 'r')
#training_data_list = training_data_file.readlines()
#training_data_file.close()

for e in range(epochs):
    for chunk in pd.read_csv('C:/Users/Anwender/Documents/Uni/KI/Python/train.csv', chunksize=chunksize):
        process(chunk)
    for record in tqdm(get_chunk()): # train on this record
        all_values = record.split(',')
        inputs = (np.asfarray(all_values[1:]) / 255.0 * 0.99) + 0.01
        targets = np.zeros(output_nodes) + 0.01
        targets[int(float(all_values[0]))] = 0.99
        n.train(inputs, targets)
        pass
    pass

#test_data_file = open('C:/Users/Anwender/Documents/Uni/KI/Python/test.csv', 'r')
#test_data_list = test_data_file.readlines()
#test_data_file.close()

scorecard = []

for record in test_data_list:
    for chunk in pd.read_csv('C:/Users/Anwender/Documents/Uni/KI/Python/test.csv', chunksize=chunksize):
        process(chunk)
    for record in tqdm(get_chunk()):
        all_values = record.split(',')
        correct_label = int(all_values[0])
        inputs = (np.asfarray(all_values[1:]) / 255.0 * 0.99) + 0.01
        outputs = n.test(inputs)
        label = np.argmax(outputs)
    if (label == correct_label):
        scorecard.append(1)
    else:
        scorecard.append(0)
        pass
    pass

scorecard_array = np.asarray(scorecard)
print ("Genauigkeit = ", scorecard_array.sum() / scorecard_array.size)