MNIST Digit Classification with Convolutional Neural Networks
This project aims to compare the performance of hand-written digit classification Convolutional Neural Network (CNN) models in parallel and sequential modes. Two scripts, classify_parallel.py and classify_sequential.py, are provided for this purpose. The CNN model itself is implemented in the CNN_MNIST.py script.
Getting Started
Prerequisites
Make sure you have the following dependencies installed:
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Python (>=3.6)
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NumPy
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PyTorch
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TorchVision
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Matplotlib
Installation
1.
Clone this repository to your local machine using:
git clone <https://github.com/kcrmin/CNN_Hardware_Comparison.git>
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2.
Install the required Python packages:
pip install -r requirements.txt
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Usage
1.
Run the program in sequential mode:
python classify_sequential.py
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2.
Run the program in parallel mode:
python classify_parallel.py
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Code Structure
CNN_MNIST.py
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Defines a CNN model for MNIST digit classification.
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Provides functions for data loading and model initialization.
classify_parallel.py
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Demonstrates parallel processing for predicting MNIST digits using the CNN model defined in CNN_MNIST.py.
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Utilizes the multiprocessing module to parallelize the prediction process, improving efficiency.
classify_sequential.py
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Demonstrates sequential processing for predicting MNIST digits using the CNN model defined in CNN_MNIST.py.
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Does not use parallelization and processes predictions sequentially.
File Structure
mnist-cnn.pth
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Pre-trained model weights saved in PyTorch format.
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Although model was trained using CNN_MNIST, had to delete the training functions due to the readability.
requirements.txt
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Contains the required Python packages to run the scripts.
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Enhance the usability as it simplifies the setup process.
Configuration
Before running the scripts, you can configure the meta_data to adjust the parameters according to your requirements. Here's a brief overview of the meta_data configuration:
# Define meta_data
meta_data["batch_size"] = 1
meta_data["num_rows"] = 200
meta_data["num_columns"] = 300
meta_data["num_cells"] = meta_data["num_rows"] * meta_data["num_columns"]
meta_data["threads"] = 2
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Results
After running each script, the following information will be printed:
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Number of threads used (only applicable for parallel mode)
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Total number of items classified
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Total runtime
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Accuracy
Additionally, the visualization of the mask image representing the classification results will be displayed.
