# coding=utf-8
import torch
import os
import torch.nn as nn
import numpy as np
import time
from classifier.nets.textcnn import textCNN
import sen2inds
import textCNN_data
word2ind, ind2word = sen2inds.get_worddict('wordLabel.txt')
label_w2n, label_n2w = sen2inds.read_labelFile('label.txt')
textCNN_param = {
'vocab_size': len(word2ind),
'embed_dim': 60,
'class_num': len(label_w2n),
"kernel_num": 16,
"kernel_size": [3, 4, 5],
"dropout": 0.5,
}
dataLoader_param = {
'batch_size': 128,
'shuffle': True,
}
#init dataset
print('init dataset...')
dataLoader = textCNN_data.textCNN_dataLoader(dataLoader_param)
valdata = textCNN_data.get_valdata()
import torch
import os
import torch.nn as nn
import numpy as np
import time
import matplotlib.pyplot as plt
from model import textCNN
import sen2inds
import textCNN_data
trainDataVecFile = 'data/txt/train_data_vec.txt'
testDataVecFile = 'data/txt/test_data_vec.txt'
#word2ind字典：字符为key，角标为value
#ind2word字典：角标为key，字符为value
word2ind, ind2word = sen2inds.get_worddict('data/txt/train_wordLabel.txt')
#label_w2n字典：字符（子板块名）为key，角标为value
#label_n2w字典：角标为key，字符（子板块名）为value
label_w2n, label_n2w = sen2inds.read_labelFile('data/txt/train_label.txt')
weightFile = 'data/pkl/weight.pkl'
textCNN_param = {
'vocab_size': len(word2ind),
'embed_dim': 60,
'class_num': len(label_w2n),
"kernel_num": 16,
"kernel_size": [3, 4, 5],
"dropout": 0.5,
}
dataLoader_param = {
'batch_size': 128,
'shuffle': True,
}