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Add CNN Model for MNIST Classification (✓ Sandbox Passed) #161

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@sweep-nightly sweep-nightly bot commented Nov 25, 2023

Description

This pull request adds a Convolutional Neural Network (CNN) model for MNIST classification. It also updates the import statements and file references accordingly.

Summary

  • Updated import statements and file references in src/api.py and src/main.py.
  • Added src/cnn.py which contains the CNN model for MNIST classification.
  • Updated the model instantiation in src/api.py and src/main.py to use the new CNN model.
  • Updated the model saving in src/main.py to save the CNN model.

Fixes #9.


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sweep-nightly bot commented Nov 25, 2023

Sandbox Executions

  • Ran sandbox for src/cnn.py. ✓
Sandbox logs for
trunk fmt src/cnn.py || exit 0 1/2 ✓
Checked 1 file
✔ No issues
Run trunk upgrade to upgrade 1 linter
trunk check --fix --filter=-ruff --print-failures src/cnn.py 2/2 ✓
Checked 1 file
✔ No issues
  • Ran sandbox for src/main.py. ✓
Sandbox logs for https://github.com/sweepai/evals/commit/f5578c50e0aabaea9bea0becbda1fcab90da7f3a
trunk fmt src/main.py || exit 0 1/2 ✓
 ✔ Formatted src/main.py
Re-checking autofixed files...

 ✔ Formatted src/main.py
Re-checking autofixed files...


Checked 1 file
✔ No issues
Run trunk upgrade to upgrade 1 linter
trunk check --fix --filter=-ruff --print-failures src/main.py 2/2 ✓
Checked 1 file
✔ No issues
  • Ran sandbox for src/api.py. ✓
Sandbox logs for https://github.com/sweepai/evals/commit/153a0589902dda50605d3c0b19475baa080fb5d9
trunk fmt src/api.py || exit 0 1/2 ✓
 ✔ Formatted src/api.py
Re-checking autofixed files...

 ✔ Formatted src/api.py
Re-checking autofixed files...


Checked 1 file
✔ No issues
Run trunk upgrade to upgrade 1 linter
trunk check --fix --filter=-ruff --print-failures src/api.py 2/2 ✓
Checked 1 file
✔ No issues

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sweep-nightly bot commented Nov 25, 2023

Apply Sweep Rules to your PR?

  • Apply: All docstrings and comments should be up to date.
  • Apply: Code should be properly formatted and indented.
  • Apply: Variable and function names should be descriptive and follow a consistent naming convention.
  • Apply: Imports should be organized and grouped together.
  • Apply: There should be no unused imports or variables.
  • Apply: Code should be properly commented and include docstrings for functions and classes.

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Sweep: add a new cnn class that defines AND trains the cnn to handle mnist in cnn.py and import it to main.py
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