def train():
# global epoch
epochs = 500
embeded_dim = 100
lr = 0.0001
batch_size = 1000
use_cuda = torch.cuda.is_available()
# use_cuda = False
loss_func = nn.CrossEntropyLoss()
word_index_dic, index_word_dic = data.word_index()
length = len(word_index_dic)
print(length)
cnn_model1 = CNNModel(int(length), int(embeded_dim), 2)
if use_cuda:
cnn_model1.cuda()
optimizer = torch.optim.Adam(cnn_model1.parameters(), lr=lr)
train_size = 100
result_list = []
targer_list = []
num = 0
F = 0
for epoch in range(epochs):
for data_list in get_data_list(train_size):
for data_item in data_list:
input_tensor = []
for item in data_item[0].lower().replace(".", "").replace(",", "").replace("<br", "") \
.replace("/>", "").replace("?", "").replace(";", "").strip().split(" "):
try:
input_tensor.append(word_index_dic[item])
except KeyError:
# print(item)
pass
try:
if len(input_tensor) > 400:
input_tensor = input_tensor[:400]
else:
while len(input_tensor) < 400:
input_tensor = input_tensor.append(0)
except:
continue
input_tensor = torch.LongTensor([input_tensor])
targer = torch.LongTensor([data_item[1]])
targer_list.append(data_item[1])
if use_cuda:
input_tensor = input_tensor.cuda()
targer = targer.cuda()
cnn_model1.zero_grad()
predict = cnn_model1(input_tensor)
# print(predict.argmax())
# print(int(predict.argmax()))
# print(targer)
result_list.append(int(predict.argmax()))
loss = loss_func(predict, targer)
# print(loss)
loss.backward()
optimizer.step()
num += 1
# if num % batch_size == 0:
# num = 0
# new_f1 = f1_score(result_list, targer_list, average='weighted')
# print(new_f1)
# # if new_f1 > F:
# # F = new_f1
# # print("F1:\t" + str(F) + "\n")
# # parmeter = cnn_model1.state_dict()
# # torch.save(parmeter, "./checkpoints_new3/CNN_model_" + str(epoch + 1) + "_f1_" + str(F) + ".pt")
# result_list = []
# targer_list = []
parmeter = cnn_model1.state_dict()
torch.save(parmeter,
"./checkpoints_new4/CNN_model_" + str(epoch + 1) + ".pt")
def test():
import os
print("开始测试")
# 定义使用的显卡
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
num = 0
number = 0
use_cuda = torch.cuda.is_available()
word_index_dic, index_word_dic = data.word_index()
length = len(word_index_dic)
embeded_dim = 100
bach_size = 100
torch.cuda.set_device(0)
# 创建模型类
cnn_model1 = CNNModel(int(length), int(embeded_dim), 2)
if use_cuda:
# 将模型转换成GPU版本
cnn_model1.cuda()
# 加载模型中的参数
# print(list(os.walk("./checkpoints_new2")))
# for model_file in list(os.walk("./checkpoints_new2"))[0][2]:
# print(model_file)
# path = os.path.join("./checkpoints_new1",model_file)
# 176
# P: 0.8079631852741096
# F: 0.8076630195182558
# for index in range(150, 500):
for index in range(151, 152):
result_list = []
targer_list = []
path = "./checkpoints_new/CNN_model_" \
+ str(index) + ".pt"
print("加载模型")
print("CNN_model_" + str(index) + ".pt")
try:
cnn_model1.load_state_dict(torch.load(path))
except:
print("error")
# 开始测试
for data_list in get_data_list(bach_size):
for data_item in data_list:
# try:
input_tensor = []
# 清除一些没用的字符 包括英文的逗号等
for item in data_item[0].lower().replace(".", "").replace(",", "").replace("<br", "") \
.replace("/>", "").replace("?", "").replace(";", "").strip().split(" "):
try:
# 将单词映射成数字
key = word_index_dic[item]
input_tensor.append(key)
except KeyError:
pass
if len(input_tensor) > 400:
input_tensor = input_tensor[:400]
else:
# print(input_tensor)
# print(data_item[0])
# print(type(input_tensor))
while len(input_tensor) < 400:
input_tensor.append(0)
# 将输入转换成tensor的格式
input_tensor = torch.LongTensor([input_tensor])
# 加载计算图
with torch.no_grad():
if use_cuda:
input_tensor = input_tensor.cuda()
# 将输入放到模型中 获取输出预测值
predict = cnn_model1(input_tensor)
# argmax()函数用来获取 列表数值最大的数字的 “位置”
predict_label = int(predict.argmax())
# print("predict_label:", predict_label, "True:", data_item[1])
# 把预测结果和正确的结果记录下来
result_list.append(int(predict_label))
targer_list.append(data_item[1])
# number += 1
# except TypeError:
# num+=1
# print(data_item)
new_f1 = f1_score(result_list, targer_list, average='binary')
P = accuracy_score(
result_list,
targer_list,
)
# with open("result_file_end.txt", "a+", encoding='utf8') as f:
# f.write(str(index) + "\n")
# f.write("P:\t" + str(P) + "\n")
# f.write("F:\t" + str(new_f1) + "\n")
# f.write("________________________________________________\n")
# print("N:", index)
print("P:", P)
print("F:", new_f1)
print("==================================")