This project uses CNN (TensorFlow, Keras) to recognize digits from the MNIST dataset.
The neural network is built using the Sequential API of Keras.
Convolutional and Pooling Layers:
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The first convolutional layer with 32 filters of size (3, 3), ReLU activation, and input shape (28, 28, 1).
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Max pooling layer with a pool size of (2, 2).
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The second convolutional layer with 64 filters of size (3, 3) and ReLU activation.
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Another max pooling layer with a pool size of (2, 2).
Flatten and Dense Layers:
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A flatten layer to convert the 2D feature maps into a 1D vector.
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A dense layer with 64 neurons and ReLU activation.
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The final dense layer with 10 neurons (number of classes) and softmax activation for multi-class classification.
Model Compilation
The model is compiled using the Adam optimizer, categorical crossentropy as the loss function, and accuracy as the metric to monitor during training.
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
Model Training
The training process involves fitting the model to the training data for 5 epochs with a batch size of 64. The validation split is set to 20% to monitor performance on a validation subset.
model.fit(train_images.reshape(-1, 28, 28, 1), train_labels, epochs=5, batch_size=64, validation_split=0.2)
Test accuracy: 0.9891999959945679
Test loss:0.034049030393362045
I also tested the model to see if it can predict the digits written by myself.
While exploring the data, I observed that most sevens in the dataset were written without a horizontal bar. There was a vsible lack of horizontal-bar-sevens in the dataset (the way I personally write it).
And it turned out to be more difficult for a model to recognize such sevens (on the left).
I also presented an extreme case of writing a '3' digit to the model and it had hard time recognizing it to (also possibly due to poor picture quality)
