Search Results for: Labels
torch.utils.data.dataloader(testing, batch_size= , shuffle=false, num_workers= ) classes = (' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ') import matplotlib.pyplot as plt import numpy as np #create an iterator for train_loader # get random training images data_iterator = iter(train_loader) images, labels
= data_iterator.next() #plot images to visualize the data rows = columns = fig=plt.figure() for i in range( ): fig.add_subplot(rows, columns, i+ ) plt.title(classes[labels[i]]) img = images[i] / + # this is for unnormalize the image img = torchvision.transforms.topilimage()(img) plt.imshow(img) plt.show...
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