Python Deep Learning Cookbook: Over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python by Indra den Bakker

Author:Indra den Bakker [Bakker, Indra den]
Language: eng
Format: azw3
Tags: COM037000 - COMPUTERS / Machine Theory, COM004000 - COMPUTERS / Intelligence (AI) and Semantics, COM044000 - COMPUTERS / Neural Networks
Publisher: Packt Publishing
Published: 2017-10-27T04:00:00+00:00

transforms.Scale(image_size)])

figure, (lr_plot, hr_plot, fake_plot) = plt.subplots(1,3)

i = random.randint(0, inputs_G.size(0) -1)

lr_image = transform(inputs_G[i])

hr_image = transform(inputs_D_real[i])

fake_hr_image = transform(inputs_D_fake[i])

lr_image_ph = lr_plot.imshow(lr_image)

hr_image_ph = hr_plot.imshow(hr_image)

fake_hr_image_ph = fake_plot.imshow(fake_hr_image)

figure.canvas.draw()

plt.show()

We can now start training our network with the following code block:

inputs_G = torch.FloatTensor(batch_size, 3, image_size, image_size)

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