Neural network classificator stuck at 85% accuracy for mnist dataset

Questions & Answers
1

I am writing a back propagation neural network from scratch on c# to classify images of mnist dataset. All neurons in the nn are connected. My nn looks like that (784,800,10) 784 neurons in input layer, one hidden layer with 800 neurons and 10 neurons in output layer. As activation function I use sigmoid(values from 0 to 1) . I don’t use biases. My learning rate is 0.002. Mini batch size is 60. My accuracy is rising from 75% to 85%
1epoch - 75%
2epoch - 79%
3epoch - 81%
4epoch - 83%
5epoch - 84.5%
6epoch - 85%
7epoch - 84.5%
10epoch - 80%

My question is can I go beyond that without a CNN and if yes, then how?
(I tried using different learning rates from 0.01 to 0.0001, different batch sizes (from 10 to 1000)which didn’t improved the accuracy.)

2

85% is not that bad. If your network was useless, this number would be more like 50%.

Anyway, have you tried altering the number of neurons in your hidden layer? When I create a hidden layer, I generally give it sqrt(num_input_neurons*num_output_neurons). Let me know how that works out for you.