Created
September 13, 2022 13:53
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variational_autoencoder5
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def inference(digit, num_examples=1): | |
""" | |
Generates (num_examples) of a particular digit. | |
Specifically we extract an example of each digit, | |
then after we have the mu, sigma representation for | |
each digit we can sample from that. | |
After we sample we can run the decoder part of the VAE | |
and generate examples. | |
""" | |
images = [] | |
idx = 0 | |
for x, y in dataset: | |
if y == idx: | |
images.append(x) | |
idx += 1 | |
if idx == 10: | |
break | |
encodings_digit = [] | |
for d in range(10): | |
with torch.no_grad(): | |
mu, sigma = model.encode(images[d].view(1, 784)) | |
encodings_digit.append((mu, sigma)) | |
mu, sigma = encodings_digit[digit] | |
for example in range(num_examples): | |
epsilon = torch.randn_like(sigma) | |
z = mu + sigma * epsilon | |
out = model.decode(z) | |
out = out.view(-1, 1, 28, 28) | |
save_image(out, f"generated_{digit}_ex{example}.png") | |
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