Demonstration of MNIST using Neural Networks with ternary weights and inputs
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.
Burmese To Croatian Translator for Text,Voice,Image from gallery & camera
Tajweed Made Easy E-Book App
500+ HSPT practice test help you pas your test at the first time.
Connect with SIVAG in an efficient and transparent manner
JNY School mobile application
Study languages automatically and unconsciously from your lock screen.
Created with AppPage.net
Similar Apps - visible in preview.