Basic demo showing how to include and run a custom TensorFlow model.
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Created a Classifier class to help speed up adding new models. The classifier interface and abstract class can be extended to use any method of classifying, not necessarily TensorFlow or even machine learning classification.
The v1 model is based on the no pooling CNN.
The classification is sometimes off and I believe this has to do with the way we pull the pixels from the Android Bitmap.
- Color values: Model was trained with gray scale (1 dimension)
- Shape: Model was trained with a [1, 784] single array that gets reshaped to a 28 x 28. In this app I do the reshaping on the device and just pass a 28 x 28 image to the classifier.
The closer I can get the user input (drawn on image view) to the input data that the model was trained on then the classification results should be better. (Or try to see if the models are too over-fit to the input data)