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[P] I’m stuck designing a neural net architecture for my project


A few weeks ago I had the idea to write an app that counts of players in finished game of go. Instead of using pure computer vision I wanted to use deep learning.

The idea was to translate a picture of a go game into a 19x19x3 px image representing the board position and then count the points. I created all the data for the training. This is how an image looks and its corresponding label. So I am training on like 7000 images/labels like this.

I tried out some architectures like multiple conv/pool layers without any FC layers. I am using mostly categorical cross entropy. Sizes of the kernel are 3×3. I played around with different number of filters. Often the training gets like to 60% accuracy and then stops to learn, so no accuracy progress is visible. I am thinking that the architecure is not suitable for my task. Seems like my data is fine although its weird that I always have to cast the data to tf.float32 before training, because tf doesnt let me to use tf.uint8.

Any tips how I could better my architecture for this specific task or maybe even a different approach?

submitted by /u/jthat92
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.