I started by experimenting with the example from the lecture having one Convolutional layer and one Pooling layer.
I tried multiple times by changing number of filters in the Convolutional layer experimenting with different pool sizes and varing the
nodes and number of hidden layers but my accuracy was always less that 0.85 and by loss was sometimes 100+
I was unable to understand why that was the case...
I kept trying by increasing the number of hidden layers, changing activation functions and tried changing the Dropout rate.
But the problem still persisted, I had less that 0.90 accuracy and incredibly high loss (100+).
There were times when with each epoch the loss kept increasing..
After that I tried to remove the Dropout completely to see what happens. To my surprise the model did not overfit and for the first time I got loss less than 1!!
Now with loss less than one, I further tried changing number of Convolutional and Pooling layers
until I finally reached accuracy greater than 0.95
After going through tensorflow documentation (https://www.tensorflow.org/tutorials/images/classification) I discovered rescaling
which would standardize values to be in the [0, 1] range.
This made data easier to work with for the neural network and resulted in accuracy of over 0.98 and loss less that 0.09!
Now I tried experimenting with various option in tensorflow api docs (https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPool2D). Adding strides in pooling futher increased my accuracy but increased the execution time. Doubling number of Convolutional filters also increased efficiency marginally.
I further tried increasing the number of activation layers but it did not increase accuracy.