Deep Learning with PyTorch: Guide for Beginners and Intermediate by P. Jerry N

Author:P., Jerry N. [P., Jerry N.]
Language: eng
Format: epub, pdf
Published: 2019-01-27T16:00:00+00:00

use_gpu = torch.cuda.is_available()

Visualizing some Images

To be able to understand the augmentations of the data, let us visualize some of the training images. This is the code for this:

def imshow(inp, title=None):

inp = inp.numpy().transpose((1, 2, 0))

mean = np.array([0.485, 0.456, 0.406])

std = np.array([0.229, 0.224, 0.225])

inp = std * inp + mean

plt.imshow(inp)

if title is not None:

plt.title(title)

plt.pause(0.001) # pause a bit so that plots are updated

# Obtain a batch of the training data

inputs, classes = next(iter(dataloders['train']))

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