Training a CNN model on MNIST dataset with GitHub Actions integration for automated testing and deployment.
- model.py - Contains Model Architecture
- utils.py - Contains training utilities and helper functions
- tests/ - Test files directory
- images/ - Generated plots and visualizations
- .github/workflows/ - GitHub Actions workflow file
- train.ipynb - DEMO Training of the model
- train.py - Training script for actions
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 4, 26, 26] 36
ReLU-2 [-1, 4, 26, 26] 0
BatchNorm2d-3 [-1, 4, 26, 26] 8
Dropout-4 [-1, 4, 26, 26] 0
Conv2d-5 [-1, 10, 24, 24] 360
ReLU-6 [-1, 10, 24, 24] 0
BatchNorm2d-7 [-1, 10, 24, 24] 20
Dropout-8 [-1, 10, 24, 24] 0
MaxPool2d-9 [-1, 10, 12, 12] 0
Conv2d-10 [-1, 8, 12, 12] 80
ReLU-11 [-1, 8, 12, 12] 0
BatchNorm2d-12 [-1, 8, 12, 12] 16
Dropout-13 [-1, 8, 12, 12] 0
Conv2d-14 [-1, 4, 12, 12] 32
ReLU-15 [-1, 4, 12, 12] 0
BatchNorm2d-16 [-1, 4, 12, 12] 8
Dropout-17 [-1, 4, 12, 12] 0
Conv2d-18 [-1, 10, 10, 10] 360
ReLU-19 [-1, 10, 10, 10] 0
BatchNorm2d-20 [-1, 10, 10, 10] 20
Dropout-21 [-1, 10, 10, 10] 0
Conv2d-22 [-1, 16, 8, 8] 1,440
ReLU-23 [-1, 16, 8, 8] 0
BatchNorm2d-24 [-1, 16, 8, 8] 32
Dropout-25 [-1, 16, 8, 8] 0
Conv2d-26 [-1, 12, 6, 6] 1,728
ReLU-27 [-1, 12, 6, 6] 0
BatchNorm2d-28 [-1, 12, 6, 6] 24
Dropout-29 [-1, 12, 6, 6] 0
Conv2d-30 [-1, 16, 4, 4] 1,728
ReLU-31 [-1, 16, 4, 4] 0
BatchNorm2d-32 [-1, 16, 4, 4] 32
Dropout-33 [-1, 16, 4, 4] 0
AvgPool2d-34 [-1, 16, 1, 1] 0
Conv2d-35 [-1, 10, 1, 1] 160
================================================================
Total params: 6,084
Trainable params: 6,084
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.40
Params size (MB): 0.02
Estimated Total Size (MB): 0.43
----------------------------------------------------------------Training: 100%|██████████| 938/938 [00:39<00:00, 23.51it/s, Loss=0.0079, Accuracy=93.67%]
Training Progress: 100%|██████████| 1/1 [00:45<00:00, 45.05s/it, Train Loss=0.2454, Test Loss=0.0506, Train Acc=93.67%, Test Acc=98.50%]
Test set: Average loss: 0.0506, Accuracy: 9850/10000 (98.50%)
| Transformation | Training Values | Visualization Values |
|---|---|---|
| Rotation Angle | -5.0 to 5.0 |
-10.0 to 10.0 |
| Center Crop Size | 24x24 |
20x20 |
The adjustments ensure that the visualization highlights the effects of the transformations clearly. These changes are not used during the actual training process.
{' zero': 98.77300613496932,
' one': 98.37837837837837,
' two': 97.0059880239521,
' three': 100.0,
' four': 97.91666666666667,
' five': 99.1869918699187,
' six': 98.70967741935483,
' seven': 98.93048128342247,
' eight': 96.18320610687023,
' nine': 97.24137931034483}- use uv instead of pip for faster installation of dependencies
- run test cases before training the model
- run test cases after training the model