Module 7 Assignment: Discriminative vs generative comparison

Module 7 Assignment: Discriminative vs generative comparison#

Theme#

Generative models and applications

Exercises#

  1. Choose one task and describe both a discriminative and a generative modeling approach.

  2. Use the starter code to sample from a simple latent-variable generator.

  3. Explain how evaluation differs for classifiers, autoencoders, diffusion models, or language models.

  4. Identify one misuse risk and one documentation safeguard for generated outputs.

Submission#

Submit a 600-900 word technical memo plus any code, plots, or shape traces needed to support your claims. Use the starter cell as a minimum reproducible experiment, then make at least one meaningful modification.

Rubric#

  • Correct use of module vocabulary and notation

  • Clear connection between design choices and data/problem structure

  • Evidence from the starter experiment or your own extension

  • Concise reflection on limitations, failure modes, or next steps

import torch
from torch import nn

torch.manual_seed(7)
generator = nn.Sequential(nn.Linear(2, 8), nn.Tanh(), nn.Linear(8, 2))
z = torch.randn(6, 2)
samples = generator(z).detach()
print(samples.round(decimals=3))
print("These are untrained synthetic samples; describe what training objective would make them useful.")

tensor([[ 0.2320, -0.5090],
        [ 0.5230, -0.0620],
        [ 0.5890, -0.3290],
        [ 0.4260, -0.4030],
        [ 0.1220, -0.5300],
        [ 0.0290, -0.5160]])
These are untrained synthetic samples; describe what training objective would make them useful.

Reflection prompts#

  • What changed when you modified the starter experiment?

  • Which result surprised you, and what diagnostic would you run next?

  • What assumption would you document before handing this model to another practitioner?