Exemplos de tensorFromData em Python

Exemplo n.º 1

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

Exemplo n.º 2

Arquivo: CNN_BiLSTM_Concat_model.py Projeto: MoherX/text-classification-pytorch

#!/usr/bin/env python
# -*- coding: utf-8 -*-
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 = 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()
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