Pytorch训练集和测试集划分及训练集批次的划分与加载—torch.utils.data.TensorDataset、torch.utils.data.random

import torch from torch.utils.data import random_split import torch.utils.data as Data train_x = torch.randn(10,8) train_y = torch.randn(10,2) print(train_x) print(train_y)

输出结果： tensor([[ 0.5008, 0.9868, -0.7672, -1.1820, -2.6178, 1.4705, -1.7990, -1.1078], [-1.5842, 1.5206, 0.7272, -2.5919, 0.8682, 0.8757, 0.4569, -0.2744], [-0.2444, -0.5412, -0.1766, 1.2055, -0.3636, 0.7021, -1.1178, 0.0898], [ 0.4265, 0.0072, 0.0930, -0.6339, -0.9330, 0.5838, 0.0063, -1.0317], [-0.5715, -0.0705, -1.4860, -0.6964, -0.6595, -0.1626, -0.9456, -1.3202], [ 0.6300, 0.5818, -0.9379, 0.9910, -0.9728, -0.4468, 0.9327, 1.1673], [-1.4601, -0.2334, 0.4478, 0.9095, -0.3818, 0.4027, 0.4042, 0.0059], [-0.0446, -1.7432, -0.6294, -0.4040, 0.2583, -0.3803, 0.0877, 0.5360], [ 2.0558, 1.5085, 0.5044, 0.3813, -0.7915, -1.5292, 0.2047, -1.0494], [ 0.8640, 0.3738, 1.4807, 0.9262, 0.3545, 0.9699, -2.2665, 0.3594]]) tensor([[-1.0027, 0.1449], [ 0.2390, -1.5291], [ 0.1028, 0.3678], [-0.1806, 2.0617], [ 0.0627, -0.7183], [-1.7710, -0.2113], [ 1.3260, 0.6122], [-0.3938, 0.5924], [ 1.3044, 0.8457], [ 0.3679, -1.9822]]) 打包训练数据和训练数据的标签

dataset = Data.TensorDataset(train_x,train_y) #把训练集和标签继续封装

把训练数据和训练数据的标签一起划分为8:2

train_data,eval_data=random_split(dataset,[round(0.8*train_x.shape[0]),round(0.2*train_x.shape[0])],generator=torch.Generator().manual_seed(42)) for i in train_data: print(i)

输出结果：(8份,说明划分成功） (tensor([-0.2444, -0.5412, -0.1766, 1.2055, -0.3636, 0.7021, -1.1178, 0.0898]), tensor([0.1028, 0.3678])) (tensor([-1.4601, -0.2334, 0.4478, 0.9095, -0.3818, 0.4027, 0.4042, 0.0059]), tensor([1.3260, 0.6122])) (tensor([-1.5842, 1.5206, 0.7272, -2.5919, 0.8682, 0.8757, 0.4569, -0.2744]), tensor([ 0.2390, -1.5291])) (tensor([ 2.0558, 1.5085, 0.5044, 0.3813, -0.7915, -1.5292, 0.2047, -1.0494]), tensor([1.3044, 0.8457])) (tensor([-0.5715, -0.0705, -1.4860, -0.6964, -0.6595, -0.1626, -0.9456, -1.3202]), tensor([ 0.0627, -0.7183])) (tensor([ 0.6300, 0.5818, -0.9379, 0.9910, -0.9728, -0.4468, 0.9327, 1.1673]), tensor([-1.7710, -0.2113])) (tensor([ 0.5008, 0.9868, -0.7672, -1.1820, -2.6178, 1.4705, -1.7990, -1.1078]), tensor([-1.0027, 0.1449])) (tensor([ 0.8640, 0.3738, 1.4807, 0.9262, 0.3545, 0.9699, -2.2665, 0.3594]), tensor([ 0.3679, -1.9822])) 按照批次划分与加载

loader = Data.DataLoader(dataset = train_data, batch_size = 2, shuffle = True, num_workers = 0 , drop_last=False) for step,(train_x,train_y) in enumerate(loader): print(step,':',(train_x,train_y))

输出结果：（输出4个批次，每个批次中2个样本，因为batch_size=2) 0 : (tensor([[ 0.5008, 0.9868, -0.7672, -1.1820, -2.6178, 1.4705, -1.7990, -1.1078], [-0.5715, -0.0705, -1.4860, -0.6964, -0.6595, -0.1626, -0.9456, -1.3202]]), tensor([[-1.0027, 0.1449], [ 0.0627, -0.7183]])) 1 : (tensor([[ 0.6300, 0.5818, -0.9379, 0.9910, -0.9728, -0.4468, 0.9327, 1.1673], [ 2.0558, 1.5085, 0.5044, 0.3813, -0.7915, -1.5292, 0.2047, -1.0494]]), tensor([[-1.7710, -0.2113], [ 1.3044, 0.8457]])) 2 : (tensor([[-0.2444, -0.5412, -0.1766, 1.2055, -0.3636, 0.7021, -1.1178, 0.0898], [ 0.8640, 0.3738, 1.4807, 0.9262, 0.3545, 0.9699, -2.2665, 0.3594]]), tensor([[ 0.1028, 0.3678], [ 0.3679, -1.9822]])) 3 : (tensor([[-1.5842, 1.5206, 0.7272, -2.5919, 0.8682, 0.8757, 0.4569, -0.2744], [-1.4601, -0.2334, 0.4478, 0.9095, -0.3818, 0.4027, 0.4042, 0.0059]]), tensor([[ 0.2390, -1.5291], [ 1.3260, 0.6122]]))

代码：

import torch from torch.utils.data import random_split import torch.utils.data as Data train_x = torch.randn(10,8) train_y = torch.randn(10,2) print(train_x) print(train_y) dataset = Data.TensorDataset(train_x,train_y) #把训练集和标签继续封装 train_data,eval_data=random_split(dataset,[round(0.8*train_x.shape[0]),round(0.2*train_x.shape[0])],generator=torch.Generator().manual_seed(42)) #把数据机随机切分训练集和验证集 for i in train_data: print(i) loader = Data.DataLoader(dataset = train_data, batch_size = 2, shuffle = True, num_workers = 0 , drop_last=False) for step,(train_x,train_y) in enumerate(loader): print(step,':',(train_x,train_y))