Module 5 Assignment: Sequence modeling design note

Module 5 Assignment: Sequence modeling design note#

Theme#

Sequence models: RNNs and LSTMs

Exercises#

  1. Define a sequence prediction task and identify the input/output alignment.

  2. Compare when a simple RNN, LSTM, GRU, or one-dimensional convolution would be appropriate.

  3. Run the starter LSTM on synthetic ordered data and inspect tensor shapes.

  4. Explain one long-range dependency risk and one mitigation strategy.

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(5)
X = torch.randn(12, 6, 3)  # batch, time, features
lstm = nn.LSTM(input_size=3, hidden_size=8, batch_first=True)
head = nn.Linear(8, 1)
sequence_output, (h_n, c_n) = lstm(X)
prediction = head(sequence_output[:, -1, :])
print("sequence output shape:", tuple(sequence_output.shape))
print("final prediction shape:", tuple(prediction.shape))

sequence output shape: (12, 6, 8)
final prediction shape: (12, 1)

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?