NameError: name 'trainloader' is not defined

2023-03-14 11:27| 来源: 网络整理| 查看: 265

[1] import torch import torchvision import torchvision.transforms as transforms import torch.utils.data as data import torchvision.datasets as datasets import torch.nn as nn

[2] transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

[3] train_data = datasets.CIFAR10(root = “./data”, train = True, download = True, transform = transform)

train_data, val_data = torch.utils.data.random_split(train_data, [int(len(train_data) * 0.8), int(len(train_data)*0.2)])

test_data = datasets.CIFAR10(root = “./data”, train = False, download = True, transform = transform)

[4] classes = test_data.classes dic_classes = {} for i in range(len(classes)): dic_classes[i] = classes[i]

print(dic_classes)

[5] trainloader = torch.utils.data.DataLoader(train_data, batch_size=16, shuffle=True) valloader = torch.utils.data.DataLoader(val_data, batch_size=16, shuffle=True) testloader = torch.utils.data.DataLoader(test_data, batch_size=16, shuffle=False)

[6] import matplotlib.pyplot as plt import numpy as np

def imshow(img, labels, dic): num = len(labels) rows = int(np.sqrt(num)) cols = int(np.sqrt(num))

fig = plt.figure(figsize=(20,20)) for i in range(rows*cols): ax = fig.add_subplot(rows, cols, i+1) tmp = img[i] tmp = tmp / 2 + 0.5 # unnormalize npimg = tmp.numpy() ax.imshow(np.transpose(npimg, (1, 2, 0)), cmap = "bone") ax.title.set_text(dic[labels[i].item()]) #plt.show() ax.axis('off') 학습용 이미지를 무작위로 가져오기

dataiter = iter(trainloader) images, labels = dataiter.next()

이미지 보여주기

imshow(images, labels, dic_classes)

정답(label) 출력

‘[6]’ is issued code. I hope this helps. [1]~[5] all worked normally. The mentioned error occurs in [6].

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