6.6.1 Generative Models Roadmap: Sample, Decode, Review

Generative models create new samples instead of only predicting labels. The practical loop is: sample a latent code, decode it, review the output, and compare versions.

Look at the Generation Flow First

Concept	First meaning
latent vector	compact hidden input used for generation
decoder / generator	turns latent code into an output
discriminator	judges real vs generated in GANs
VAE	learns a smoother latent space
review	generated output still needs human and metric checks

Run One Tiny Decoder

Create generative_first_loop.py and run it after installing torch.

import torch

torch.manual_seed(0)
latent = torch.randn(2, 4)
decoder = torch.nn.Sequential(torch.nn.Linear(4, 6), torch.nn.Tanh())
generated = decoder(latent)

print("latent_shape:", tuple(latent.shape))
print("generated_shape:", tuple(generated.shape))
print("value_range:", round(generated.min().item(), 3), round(generated.max().item(), 3))

Expected output:

latent_shape: (2, 4)
generated_shape: (2, 6)
value_range: -0.863 0.695

This is not a real generator yet. It shows the core shape idea: small latent vectors can be decoded into larger outputs.

Learn in This Order

Order	Read	What to focus on
1	6.6.2 GAN	generator, discriminator, adversarial balance
2	6.6.3 VAE	encoder, decoder, latent space

Pass Check

You pass this roadmap when you can explain the difference between predicting a label and generating a sample, and describe why generated outputs need review rather than blind trust.