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...
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...
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2) testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform) testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2...
torchvision.utils.save_image(tensor, fp, format=None) → None 1. 直接将给定的Tensor保存成图片。 参数的含义: tensor:Tensor或list类型。表示需要被保存的图片,如果给定的是一个mini-batch的tensor,则会自动调用make_grid函数将这些图片组合成网格形式再保存。 fp:string类型或文件对象(file object)类型。将图...
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 ...
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(...
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...
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 通过本教程,你学会了如何使用Python和PyTorch库实现一个简单的生成对抗网络(GAN),并在MNIST数据集上进行训练和生成图像。生成对抗网络是一...
dataiter=iter(test_loader)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: ...
imagesdataiter = iter(trainloader)images, labels = dataiter.next()print(images.shape)# show imagesimshow(torchvision.utils.make_grid(images))# print labelsprint(' '.join('%5s'% classes[labels[j]]forj in range(4)))class Net(nn.Module):def __init__(...