MNIST Classification Model
An improved CNN model for handwritten digit recognition, trained on the MNIST dataset.
Model Architecture
- Uses Convolutional layers (CNN)
- Data Augmentation for improved performance
- Batch Normalization
- Dropout for preventing Overfitting
- Dense layers with ReLU activation
Parameters
- Optimizer: Adam (lr=0.001)
- Loss: Sparse Categorical Crossentropy
- Metrics: Accuracy
- Epochs: 20 (with Early Stopping)
- Batch Size: 32
Performance
Test Accuracy: 0.9884
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