Basic DeepLearning model in Python that predicts numbers form handwritten number dataset

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It uses:

• Rectified Linear Unit (ReLU) as an activation function
• Softmax funtion to transform the output to a probability distribution. It is also differentiable, which means that it can be used in conjunction with optimization algorithms such as stochastic gradient descent (SGD) or Adam (Adaptive Moment Estimation)
• Adam optimizer. unlike SGD learning-rate is adaptive instead of fixed which results in a more rapid convergence rate
• Sparse categorical crossentropy as loss function which is best used when target labels are integers
• Accuracy metric computes acccuracy using log cosh Error Regression (commonly used built in func)

Output:

• 3 epochs (iterations) resulted in accuracy ≈ 0.9762

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P.S. Trained model is also inluded in source files as 'demo.model'