Building High Performance Convolutional Neural Networks with TensorFlow
This model is an improvement of the original LeNet-5 model. Features:
- Image Sub-sampling with Convolutional (CNN) Layers
- Reguralization Layers (Batch Normalization, Dropouts)
- Yogi Optimizer (An improved version of Adam optimizer: https://proceedings.neurips.cc/paper/2018/file/90365351ccc7437a1309dc64e4db32a3-Paper.pdf)
- AdaBelief + LookAhead Optimizer (State of the Art Adam extension optimizer: https://arxiv.org/abs/2010.07468)
- Output noise with Label Smoothing (https://arxiv.org/pdf/2011.12562.pdf)
- Early Stopping Mechanism
- Learning Rate Decay
- Weight Decay
- UMAP - Dimension Reduction
| Depth | Layer (Type) | Output Shape | Param # |
|---|---|---|---|
| 1 | RandomRotation | (None, 28, 28, 1) | 0 |
| 1 | Conv2D(32, 3, 1) | (None, 26, 26, 1) | 288 |
| 1 | BatchNormalization | (None, 26, 26, 1) | 128 |
| 2 | Conv2D(32, 3, 1) | (None, 24, 24, 1) | 9216 |
| 2 | BatchNormalization | (None, 24, 24, 1) | 128 |
| 3 | Conv2D(32, 5, 2) | (None, 12, 12, 1) | 25632 |
| 3 | BatchNormalization | (None, 12, 12, 1) | 0 |
| 4 | Conv2D(64, 3, 1) | (None, 10, 10, 64)) | 18432 |
| 4 | BatchNormalization | (None, 10, 10, 64) | 256 |
| 5 | Conv2D(64, 3, 1) | (None, 8, 8, 64) | 36864 |
| 5 | BatchNormalization | (None, 8, 8, 64) | 256 |
| 6 | Conv2D(64, 5, 2) | (None, 4, 4, 64) | 102464 |
| 6 | BatchNormalization | (None, 4, 4, 64) | 0 |
| 7 | Conv2D(128, 3, 1) | (None, 2, 2, 128) | 204928 |
| 7 | Flatten | (None, 512) | 204928 |
| 8 | Dense(10) | (None, 10) | 5130 |
- UMAP - KNN
- Ensemble (CNN + UMAP - KNN)
| Algorithm | Accuracy |
|---|---|
| UMAP + KNN | 0.9595 |
| CNN (Yogi) | 0.9956 |
| CNN (AdaBelief) + LookAhead | 0.9963 |
| Ensemble (CNN + UMAP -KNN) | 0.9596 |
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