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 didn't 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. -------------------------------- 0x0 0x1 0x(-1) 1x0 1x1 1x(-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 didn't use libraries to show the number of multiplications. "For loop" recognition isn't efficient but useful to learn details of the networks.