MiniTorch Module 4

• Docs: https://minitorch.github.io/

• Overview: https://minitorch.github.io/module4.html

This module requires fast_ops.py, cuda_ops.py, scalar.py, tensor_functions.py, tensor_data.py, tensor_ops.py, operators.py, module.py, and autodiff.py from Module 3.

Additionally you will need to install and download the MNist library.

(On Mac, this may require installing the wget command)

pip install python-mnist
mnist_get_data.sh

• Tests:

python run_tests.py

This assignment requires the following files from the previous assignments. You can get these by running

python sync_previous_module.py previous-module-dir current-module-dir

The files that will be synced are:

    minitorch/tensor_data.py minitorch/tensor_functions.py minitorch/tensor_ops.py minitorch/fast_ops.py minitorch/cuda_ops.py minitorch/operators.py minitorch/module.py minitorch/autodiff.py minitorch/module.py project/run_manual.py project/run_scalar.py project/run_tensor.py project/run_fast_tensor.py project/parallel_check.py tests/test_tensor_general.py

Results

Link to folder with output logs for both MNIST and sentiment analysis test on lr 0.01 and 0.05. Results

MNIST Training Logs

With learning rate 0.01 we get a final accuracy of 16/16

With learning rate 0.05 we get a final accuracy of 16/16

Sentiment Training Logs

79% Validation Accuracy with lr = 0.01

79% Validation Accuracy with lr = 0.05