Ternary Neural Networks MNIST

Features Ternary Neural Networks MNIST

Deep learning recognizes handwritten digits with high recognition rate.
We usually use MNIST data sets (60,000 train-images and 10,000 test-images) to demonstrate neural network models.
Original MNIST didnt suitable for this applications and I made my own handwritten data sets.
When you write a number in the blank space, ternary neural networks will recognize the number.
You can see the prediction, probability, time cost and number of multiplications.Ternary Neural Networks have only 9 multiplication patterns.--------------------------------0x00x10x(-1)1x01x11x(-1)(-1)x0(-1)x1(-1)x(-1)--------------------------------In this applications, details of multiplications in networks are obvious.If I used Tensor Flow lite, it would be easier to implement neural networks model in Android application.
I didnt use libraries to show the number of multiplications.
"For loop" recognition isnt efficient but useful to learn details of the networks.