import torch from torch import nn,optim from torch.autograd import Variable from torch.utils.data import DataLoader import data_preprocess import os os.environ['CUDA_VISIBLE_DEVICES']='0' use_cuda=torch.cuda.is_available() # 将数据划分为训练集和测试集 X_train, X_test, Y_train, Y_test, word_to_inx, inx_to_word = data_preprocess.tensorFromData() trainDataSet = data_preprocess.TextDataSet(X_train, Y_train) testDataSet = data_preprocess.TextDataSet(X_test, Y_test) trainDataLoader = DataLoader(trainDataSet, batch_size=16, shuffle=True) testDataLoader = DataLoader(testDataSet, batch_size=16, shuffle=False) # 获取字典 # word_to_inx, inx_to_word = data_preprocess.get_dic() word_to_inx, inx_to_word = word_to_inx, inx_to_word len_dic = len(word_to_inx) # 定义超参数 MAXLEN = 64 input_dim = MAXLEN emb_dim = 128 num_epoches = 20 batch_size = 16 learning_rate = 0.001
import torch from torch.autograd import Variable from torch.utils.data import DataLoader from torch import optim, nn import data_preprocess import os torch.manual_seed(1) os.environ['CUDA_VISIBLE_DEVICES'] = '0' use_cuda = torch.cuda.is_available() word2index, index2word, tag2index, index2tag = data_preprocess.get_dic() test_x_cut, test_y_cut, test_mask, test_x_len, test_x_cut_word, test_x_fenge = data_preprocess.getTest_xy( './data/test_data') testDataSet = data_preprocess.TextDataSet(test_x_cut, test_y_cut, test_mask) testDataLoader = DataLoader(testDataSet, batch_size=16, shuffle=False) MAXLEN = 100 vcab_size = len(word2index) emb_dim = 128 hidden_dim = 256 num_epoches = 20 batch_size = 16 class BILSTM_CRF(nn.Module): def __init__(self, vcab_size, tag2index, emb_dim, hidden_dim, batch_size): super(BILSTM_CRF, self).__init__() self.vcab_size = vcab_size