Comments (2)
The GoogLeNet example only reproduces the predictions of the pretrained network, for which the dropout layer is not needed (it would be disabled when doing predictions with get_output(net['prob'], deterministic=True)
). For training it's not only missing dropout, but also the auxiliary classifiers that branch off from the main tower. We were thinking about optionally adding what's missing for training with an additional keyword argument to build_model()
, but right now you'll just have to adapt the model manually if you plan to train it (or continue training it). Thanks for your report anyway!
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Okay great. I didn't realise it was just for testing. Thanks for your reply!
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Related Issues (20)
- 3D UNet implementation HOT 7
- reason behind low value of parameters in VGG19 HOT 1
- error when set values for vgg-19 HOT 2
- modelzoo resnet50.py incompatible to Python 3 HOT 3
- Implementation of Convolutional Spatial Transformer and Siamese network HOT 3
- no sandbox.cuda
- Bad argument to Theano: No. of dimensions changes in the error after reshaping
- Question not an Issue: fliping the arrays HOT 1
- cifar100 with resnet HOT 1
- pretrained network for small images HOT 1
- https://s3.amazonaws.com/lasagne/recipes/pretrained/imagenet/vgg16.pkl HOT 6
- vgg16.pkl without aws cli
- Need help with S3 Browser based downloads HOT 1
- Dice coeff is not changing since the first epoch and binary accuracy changes and is increased to 1?
- Problem with op.grad in OpFromGraph - Guided Backpropagation
- Wrong order of stride and pad arguments in build_simple_block HOT 3
- Broken links in Video features with C3D.ipynb example HOT 5
- Training C3D
- Wrong pretrained weights for UNet example HOT 1
- DICE coefficient loss function HOT 23
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