Comments (5)
Pull requests are welcome. If you have something you'd like to improve, having code ready that implements the change helps a lot, and it's more likely it'll be done sooner (once there's consensus that the general idea is good -- you might wanna ask if unsure). You'll probably want to add a test for a new feature.
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Daniel
I already have in my nolearn dev, and am using a function to load weights by layer type.
I wanted to add that if it is helpful to others.
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I've added a little comment to the tutorial where you've mentioned the same thing; I think the recent addition of a 'name' for Lasagne layers is good news for us, and it means that layer weights can be copied maybe more reliably than by type. Check my comments there. I think it'll be useful to see your code anyway, also to see how you've implemented the AE, something that others might want to try as well.
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Here it is https://gist.github.com/run2/e7162610e759a3453156. It is nothing fancy. I can improve it further if it is helpful to others
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Thanks for sharing this. I think it'll ultimately be more useful to match layers by name here. Lasagne just recently added names to layers. We should use these, and then copying weights becomes just a matter of matching names.
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Related Issues (20)
- RememberBestWeights does not honor the verbose parameter HOT 2
- A replayable fit() method - diff/patch attached HOT 1
- remove('trainable') Lasagne's command doesn't work in nolearn HOT 6
- flip_filters and pad parameter not used by NeuralNet's class HOT 5
- OSError: could not read bytes when trying to fetch mldata HOT 2
- CUDA error, possibly related to network size? HOT 2
- Trained on GPU, inference on CPU doesn't make sense
- Install nolearn with Lasagne dependance not working HOT 2
- Bug in calculating average scores
- nolearn is not installing
- Bug when using Lasagne `mask_input` parameter
- 'NeuralNet' object has no attribute 'layers_' HOT 1
- Weights sum up to zero
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- Dependency on both backends in requirements.txt switches off GPU support HOT 3
- Enable to reproduce the last value of trainning when predicting CNN
- enable to reproduce loss value of training when predicting CNN HOT 1
- python 3 support not working with Lasagne? HOT 12
- TypeError: Failed to instantiate <class 'lasagne.layers.pool.MaxPool2DLayer'> with args {'name': 'pool1', 'ds': (2, 2), 'incoming': <lasagne.layers.conv.Conv2DLayer object at 0x7ff765fa29e8>}. Maybe parameter names have changed?
- nolearn now on conda-forge HOT 1
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