3,28,28)# 将图像们排列成网格,grid_img 就是将 6 张图片拼接后的网格图像# torchvision.utils.make_grid() 函数生成的网格图像,它的形状是 (C, H, W)grid_img = torchvision.utils.make_grid(images)print("grid_img.shape",grid_img.shape)# grid_img.shape torch.Size([3, 32, 182])# 显示...
z=torch.randn(64,input_size)fake_images=G(z)fake_images=fake_images.view(fake_images.size(0),1,28,28)# 可视化生成的图像 grid=torchvision.utils.make_grid(fake_images,nrow=8,normalize=True)plt.imshow(grid.permute(1,2,0).detach().numpy())plt.title('Generated Images')plt.show() 总结 ...
plt.imshow(example_data[i][0], cmap='gray')#plt.show()### TENSORBOARD ###img_grid= torchvision.utils.make_grid(example_data)writer.add_image('mnist_images', img_grid)#writer.close()#sys.exit()### Fully connected neural network with one hidden layerclassNeuralNet(nn.Module): def __i...
(img):img=img/2+0.5# 反标准化 npimg=img.numpy()plt.imshow(np.transpose(npimg,(1,2,0)))plt.show()# 随机获取一批数据 dataiter=iter(trainloader)images,labels=next(dataiter)# 显示图片imshow(torchvision.utils.make_grid(images))# 打印标签print(' '.join(f'{classes[labels[j]]}'forjin...
# 显示 labels_try 的5张图片,即valid里第一个batch的5张图片 out = torchvision.utils.make_grid(inputs_try) imshow(out, title=[dset_classes[x] for x in labels_try]) 4.创建VGG Model !wget https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json ...
import torchvision import numpy as np import matplotlib.pyplot as plt import torch.nn as nn import torch.nn.functional as F from torchvision.datasets import CIFAR10 from torchvision.transforms import ToTensor from torchvision.utils import make_grid ...
imshow(torchvision.utils.make_grid(images)) # print labels print(' '.join('%5s' % classes[labels[j]] for j in range(4))) 我收到此错误消息: Files already downloaded and verified Files already downloaded and verified Files already downloaded and verified Files already downloaded and verified ...
sample = sample.view(64,1,28,28)# 可视化生成的图像grid = torchvision.utils.make_grid(sample, nrow=8, normalize=True) plt.imshow(grid.permute(1,2,0).numpy(), cmap='gray') plt.title('Generated Images') plt.show() 总结 通过本教程,你学会了如何使用Python和PyTorch库实现一个简单的变分自编...
imshow(torchvision.utils.make_grid(images)) # 打印类标 print(' '.join('%5s'% classes[labels[j]] for j inrange(4))) 图片二 输出: cat car dog cat 2.定义一个卷积神经网络 在这之前先 从神经网络章节 复制神经网络,并修改它为3通道的图片(在此之前它被定义为1通道) ...
images, labels = dataiter.next()# 打印图像imshow(torchvision.utils.make_grid(images))# 打印标签print('GroundTruth: ',' '.join(f'{classes[labels[j]]}'forjinrange(4)))# 打印预测结果outputs = model(images.to(device)) _, predicted = torch.max(outputs,1)print('Predicted: ',' '.join(...