Keras: Difference between revisions
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[https://keras.io/layers/embeddings/ Embeddings] |
[https://keras.io/layers/embeddings/ Embeddings] |
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[https://github.com/fastai/courses fast.ai] |
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=General= |
=General= |
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[http://picard.github.keplr.io/ Picard] - "Picard lets you easily declare large spaces of (keras) neural networks and run (hyperopt) optimization experiments on them." |
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[http://blog.evjang.com/2017/01/nips2016.html This has lots of good infos] - Includes an AndrewNG talk and recommended papers. |
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[http://ankivil.com/kaggle-first-steps-with-julia-chars74k-first-place-using-convolutional-neural-networks/ Some Image Augmentation info on Kaggle] |
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[https://www.reddit.com/r/MachineLearning/comments/5kxfkb/d_rmachinelearnings_2016_best_paper_award/ Reddit best papers] |
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[http://online.cambridgecoding.com/notebooks/cca_admin/neural-networks-tuning-techniques Deep learning for complete beginners: neural network fine-tuning techniques by Cambridge Coding Academy] |
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<strike>Look into using PRELU ELU, etc...</strike> |
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Look into [https://github.com/fchollet/keras/pull/2887 MPELU] |
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[https://keras.io/applications/ keras.io/applications/] - Inbuilt pre-trained Models. |
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[https://keras.io/getting-started/sequential-model-guide/ keras.io/getting-started/sequential-model-guide/] - Sequential Model Guide. Shows examples! |
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predict_on_batch(self, x) |
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fit_generator(self, generator, samples_per_epoch, nb_epoch, verbose=1, callbacks=None, validation_data=None, nb_val_samples=None, class_weight=None, max_q_size=10, nb_worker=1, pickle_safe=False, initial_epoch=0) |
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[https://arxiv.org/pdf/1512.07108v5.pdf Paper with Overview of loss/optimisation functions] |
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[https://uwaterloo.ca/data-science/sites/ca.data-science/files/uploads/files/keras_tutorial.pdf Keras Tutorial] |
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[https://github.com/fchollet/deep-learning-models Pretrained Models] |
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==Visualizing== |
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[https://blog.keras.io/how-convolutional-neural-networks-see-the-world.html how-convolutional-neural-networks-see-the-world] |
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[http://ankivil.com/visualizing-deep-neural-networks-classes-and-features/ visualizing-deep-neural-networks-classes-and-features] |
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=RNN= |
=RNN= |
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[https://keras.io/preprocessing/sequence/ Keras Sequence Preprocessing] - keras.preprocessing.sequence.pad_sequences |
[https://keras.io/preprocessing/sequence/ Keras Sequence Preprocessing] - keras.preprocessing.sequence.pad_sequences |
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[https://stats.stackexchange.com/questions/184104/using-keras-lstm-rnn-for-variable-length-sequence-prediction Using Keras LSTM RNN for variable length sequence prediction] - Recomends either zero-padding or batches of 1... |
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[http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ Good?] - Specifically talks about sliding window. Alice in wonderland. |
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=GANs= |
=GANs= |
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[https://arxiv.org/abs/1701.00160 GANS] |
[https://arxiv.org/abs/1701.00160 GANS] |
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=Font Aliasing= |
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Can a CNN be trained to alias font glyphs. Can it work with 3D rotations? |
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* [https://www.reddit.com/r/rust/comments/5m20al/github_jwilmalacritty_a_crossplatform_gpu/ This Reddit post] About a GPU based terminal. |
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* [http://wdobbie.com/post/gpu-text-rendering-with-vector-textures/ This post about distance fields + vector textures] [https://vimeo.com/83732058 This video] |
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* [https://erikbern.com/2016/01/21/analyzing-50k-fonts-using-deep-neural-networks/ Analyzing 50k fonts using deep neural networks] [https://github.com/erikbern/deep-fonts github] |
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* [https://github.com/PistonDevelopers/freetype-rs freetype-rs] |
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* [https://lambdacube3d.wordpress.com/2014/11/12/playing-around-with-font-rendering/ Playing around with distance field font rendering] |
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* [http://www.valvesoftware.com/publications/2007/SIGGRAPH2007_AlphaTestedMagnification.pdf Valve paper] |
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* [http://jogamp.org/doc/gpunurbs2011/p70-santina.pdf Resolution Independent NURBS Curves Rendering using Programmable Graphics Pipeline] |
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Latest revision as of 10:57, 9 February 2017
Keras Callbacks how-can-i-interrupt-training-when-the-validation-loss-isnt-decreasing-anymore
General[edit | edit source]
Picard - "Picard lets you easily declare large spaces of (keras) neural networks and run (hyperopt) optimization experiments on them."
Some Image Augmentation info on Kaggle
Look into using PRELU ELU, etc...
Look into MPELU
keras.io/applications/ - Inbuilt pre-trained Models.
keras.io/getting-started/sequential-model-guide/ - Sequential Model Guide. Shows examples!
predict_on_batch(self, x)
fit_generator(self, generator, samples_per_epoch, nb_epoch, verbose=1, callbacks=None, validation_data=None, nb_val_samples=None, class_weight=None, max_q_size=10, nb_worker=1, pickle_safe=False, initial_epoch=0)
Paper with Overview of loss/optimisation functions
Visualizing[edit | edit source]
how-convolutional-neural-networks-see-the-world visualizing-deep-neural-networks-classes-and-features
RNN[edit | edit source]
Dimension mismatch in LSTM - Your input should be in this format (sequences, timesteps, dimensions). So based on your example, your input should be in (None, 8, 2). Your input now is (8,2).
Building Autoencoders in Keras - Includes LTSM!!!, Also shows a webserver graph showing progress. How to make a 2d graph. And how to interpolate between numbers...
Help: 'Wrong number of dimensions: expected 3, got 2 with shape (...)
cs231n-CNNs 10 - Recurrent Nerual Networks Lecture
Time Series Prediction with LSTMs
Keras Sequence Preprocessing - keras.preprocessing.sequence.pad_sequences
Using Keras LSTM RNN for variable length sequence prediction - Recomends either zero-padding or batches of 1...
Good? - Specifically talks about sliding window. Alice in wonderland.
GANs[edit | edit source]
Font Aliasing[edit | edit source]
Can a CNN be trained to alias font glyphs. Can it work with 3D rotations?
- This Reddit post About a GPU based terminal.