First run of CNNmodel.py (nothing changed)
step 0, training accuracy 0.18
step 100, training accuracy 0.88
step 200, training accuracy 0.94
step 300, training accuracy 0.96
step 400, training accuracy 0.92
test accuracy 0.9433
Time for building convnet:
103588

First run of CNmodel.py (dataset changed to fashion-mnist-master/data/fashion)]
step 0, training accuracy 0.08
step 100, training accuracy 0.62
step 200, training accuracy 0.7
step 300, training accuracy 0.86
step 400, training accuracy 0.84
test accuracy 0.8282
Time for building convnet:
94340

Fifth run of CNmodel.py (AdaGrad Optimizer, no change)]
step 0, training accuracy 0.14
step 100, training accuracy 0.72
step 200, training accuracy 0.9
step 300, training accuracy 0.8
step 400, training accuracy 0.82
test accuracy 0.8171
Time for building convnet:
111625

Seventeenth run of CNmodel.py (GradientDescent - 16,36,128 in/out channels)
step 0, training accuracy 0.22
step 100, training accuracy 0.78
step 200, training accuracy 0.8
step 300, training accuracy 0.78
step 400, training accuracy 0.8
test accuracy 0.8268
Time for building convnet:
46309

Process finished with exit code 0
Eighteenth run of CNmodel.py (GradientDescent - 8,45,128 in/out channels)
step 0, training accuracy 0.26
step 100, training accuracy 0.74
step 200, training accuracy 0.92
step 300, training accuracy 0.8
step 400, training accuracy 0.88
test accuracy 0.8124
Time for building convnet:
34380

Nineteenth run of CNmodel.py (GradientDescent - 8,45,200 in/out channels)
step 0, training accuracy 0.14
step 100, training accuracy 0.7
step 200, training accuracy 0.88
step 300, training accuracy 0.8
step 400, training accuracy 0.9
test accuracy 0.8379
Time for building convnet:
35690

Run # 20 (Final run) (GradientDescent --  8,45,500 -- steps = 900 (from 500))
step 0, training accuracy 0.34
step 100, training accuracy 0.9
step 200, training accuracy 0.88
step 300, training accuracy 0.86
step 400, training accuracy 0.9
step 500, training accuracy 0.86
step 600, training accuracy 0.86
step 700, training accuracy 0.76
step 800, training accuracy 0.9
test accuracy 0.8584
Time for building convnet:
64197

From my observations the GitHub ReadMe for A MNIST-like fashion product database was completely accurate in that it is easy
to get a 93% accuracy from the minst dataset, once the data was changed it hovered around approx 82%, as you can see it wasn't
until I changed the step count to almost 1000, the in/out channels to 8,45,500 and applied the gradient descent optimizer
that I was able to achieve an 86% accuracy. I strongly believe that if I kept recording data and the separation of channels
and increased the amount of steps significantly that I might achieve a 90%, but 94% as in the MINST dataset is not achievable
at my level of knowledge...