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MNIST Dataset - Classifying Handwritten Digits

Last Updated: 06/09/24

Outstanding Tasks

  1. None

Index

  1. Analysis Goal
  2. Modeling
    1. Simple feed-forward neural network
    2. Feed-forward neural network with data augmentation
    3. Convolutional neural network with data augmentation

1. Analysis Goal

Classify handwritten digits from the MNIST database

2. Modeling

A. Simple feed-forward neural network

98.44% Accuracy

A fully connected neural network, taking a 28x28 greyscale image input, flattening, passing it through three hidden dense layers with ReLU activation, outputing a probability distribution over 10 classes using softmax activation:

Model Architecture

  1. Input: (28, 28, 1)
  2. Flatten: (784)
  3. Dense: (784) to (512), ReLU
  4. Dense: (512) to (256), ReLU
  5. Dense: (256) to (128), ReLU
  6. Output Dense: (128) to (10), softmax

The model was compiled with Adam optimizer, categorical cross-entropy loss, and accuracy as the evaluation metric. The model was trained for 20 epochs.

B. Feed-forward neural network with data augmentation

98.71% Accuracy

Using the same neural network described above, data augmentation is applied to improve the model's generalizability:

Data Augmentation

  • Rotation: Randomly rotate up to 10 degrees
  • Zoom: Randomly zoom in up to 10%
  • Width shift: Randomly shift horizontally up to 10% of width
  • Height shift: Randomly shift vertically up to 10% of height

The model was compiled with Adam optimizer, categorical cross-entropy loss, and accuracy as the evaluation metric. The model was trained for 20 epochs.

C. Covolutional neural network with data augmentation

99.43% Accuracy

A convolutional neural network (CNN), taking a 28x28 grayscale image input, processing it through several convolutional and pooling layers, followed by dense layers, and outputing a probability distribution over 10 classes using softmax activation. Data augmentation is applied to the training data to improve the model's generalization. The same data augmentation applied to the feed-forward neural network was used to improve the model's generalizability:

Model Architecture

  1. Input: (28, 28, 1), Conv2D with 32 filters, kernel size (3, 3), ReLU
  2. MaxPooling2D: Pool size (2, 2)
  3. Conv2D: 64 filters, kernel size (3, 3), ReLU
  4. MaxPooling2D: Pool size (2, 2)
  5. Conv2D: 64 filters, kernel size (3, 3), ReLU
  6. Flatten: (None, 64)
  7. Dense: (64) to (128), ReLU
  8. Droupout Layer: Regularization rate 0.50
  9. Output Dense: (128) to (10), softmax

The model was compiled with Adam optimizer, categorical cross-entropy loss, and accuracy as the evaluation metric. The model was trained for 20 epochs.