Training logs and accuracy reports are attached in the tail of the files.
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01_cifar10_conv.py
: Train and save a conv network with 4 conv layers, which will be used as a feature extractor. The final accuracy is about 88.70% for 200 epochs. -
01_cifar10_conv_base.py
: Define a new model with the same conv layers as01_cifar10_conv.py
, but with new FC layers. The conv layers are fixed and not updated when training, for 50 epochs, the result is about 87.44%.
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02_cifar10_vgg11.py
: The same as01_cifar10_conv.py
, but using VGG11's conv layers, which have 8 conv layers. When trained 300 epochs, the accuracy is about 90.79%。 -
02_cifar10_vgg11_base.py
: The same as01_cifar10_conv_base.py
, but using the above 8-layer conv networks as a feature extractor. The accuracy is 90.73%.
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03_cifar10_vgg16.py
: The same as02_cifar10_vgg11.py
, but using VGG-16, which has 13 convolution layers. The accuracy is 92.41%. -
03_cifar10_vgg16_base.py
: The same as02_cifar10_vgg11_base.py
, but using VGG-16's conv layers as a feature extractor. The accuracy is also 92.41%%.
- Fixed the Conv Layers as feature extractor.
- Finetune the Conv Layers (VGG16 should be about 92%).
- Add self-contained connections.
- Test some update rules of self-contained layer.