I’m recently doing image classification with TensorFlow/Keras,using CNN model.I used this blog post as reference: http://ift.tt/2b1UmMA This is written by the author of Keras(Keras is a DL library,by the way.You can get the code from here : http://ift.tt/22MULTu) I modified the code,and used it to classify some signal images,each class of them containing thousands of images. But when I wanted to classify them into three classes,I found that the test accuracy became very low —- Only 0.55 or so. If I improve some parameters such as dropout,or layers(I increased the number of layers into 15),I found that the result didn’t seem to improve much. So I wonder if there is any method I can take to improve the accuracy? For example,Keras’ MNIST hand-written digits recognization example can reach nearly 99% accuracy,which I think is really high. But their networks are actually quite simple.I can’t figure out,and I might be wrong at some point.
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May 27, 2017 at 06:17AM
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