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Scenario: You have multiple GPUs on a single machine running Linux, but you want to use just one. By default, Keras allocates memory to all GPUs unless you specify otherwise. You use a Jupyter Notebook to run Keras with the Tensorflow backend.
Run this bit of code in a cell right at the start of your notebook (before importing tensorflow or keras).
Awhile ago I presented and attempted to explain this work to our reading group:
van den Oord, A., Kalchbrenner, N., Vinyals, O., Espeholt, L., Graves, A., & Kavukcuoglu, K. (2016). Conditional Image Generation with PixelCNN Decoders. In D. D. Lee, M. Sugiyama, U. V Luxburg, I. Guyon, & R. Garnett (Eds.), NIPS (pp. 4790–4798). Retrieved from http://arxiv.org/abs/1606.05328
And also dived a bit into their previous work,
van den Oord, A., Kalchbrenner, N., & Kavukcuoglu, K. (2016). Pixel Recurrent Neural Networks. Arxiv, 48. Retrieved from http://arxiv.org/abs/1601.06759
While I usually post slides to the web shortly after, this time I’ve been scared to do so. There are a few critical points from this paper that I still don’t understand. And while I told myself that I would spend some time to figure this out, it is now months later, and I’ve taken no action. So as now is always the time to continue on in spite of the fear, I’ll let you, dear Internet, have these slides in all there erroneous ways.
Continue reading “Conditional Image Generation with PixelCNN Decoders – slides”
We had our work, BrainNetCNN, published in NeuroImage awhile ago,
Kawahara, J., Brown, C. J., Miller, S. P., Booth, B. G., Chau, V., Grunau, R. E., Zwicker, J., G., Hamarneh, G. (2017). BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage, 146(Feb), 1038–1049. http://doi.org/10.1016/j.neuroimage.2016.09.046
and I’ve meant to do a blog writeup about this. We recently released our code for BrainNetCNN on GitHub (based on Caffe), which implements the proposed filters designed for adjacency matrices.
We called this library Ann4Brains. In hindsight, we could have called this something more general and cumbersome like Ann4AdjacencyMatrcies, but I still like the zombie feel that Ann4Brains has.
We designed BrainNetCNN specifically with brain connectome data in mind. Thus the tag line of,
“Convolutional Neural Networks for Brain Networks”
seemed appropriate. However, after receiving some emails about using BrainNetCNN for other types of (non-connectome) data, I’ll emphasize that this approach can be applied to any sort of adjacency matrix, and not just brain connectomes.
The core contribution of this work is the filters designed for adjacency matrices themselves. So we’ll go through each of them. But first, let’s make sure we are clear on what the brain connectome (or adjacency matrix) is.
Continue reading “Convolutional Neural Networks for Adjacency Matrices”
This week I presented to our weekly reading group, this work:
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., … Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489.
To quickly summarize this work…
Basically, they create a policy network, which is a convolutional neural network, that predicts the next move a human player would do from a board state. They create a value network, also a convolutional neural network, that predicts the outcome (win or lose) of the game given the current board state.
Continue reading “Mastering the Game of Go – slides [paper explained]”
Scenario: You’re trying to get your GPU to work in TensorFlow on a Ubuntu Laptop. You’ve already installed Tensorflow, Cuda, and Nvidia drivers.
You run python and import TensorFlow:
import tensorflow as tf
And you see encouraging messages like: "successfully opened CUDA library libcublas.so locally"
But in Python, when you run,
tf.Session()
You get this cryptic error:
failed call to cuInit: CUDA_ERROR_UNKNOWN
Here’s how to fix this.
Continue reading “TensorFlow – failed call to cuInit: CUDA_ERROR_UNKNOWN”
You suddenly get this error when training/saving a model in Caffe or saving a model in pycaffe.
io.cpp:69] Check failed: proto.SerializeToOstream(&output) *** Check failure stack trace: ***
Continue reading “caffe – Check failed: proto.SerializeToOstream(&output)”
Our paper entitled: “Multi-resolution-Tract CNN with Hybrid Pretrained and Skin-Lesion Trained Layers” was accepted and presented as an oral talk in the Machine Learning in Medical Imaging (MLMI) Workshop (part of the MICCAI conference).
In this work, we used a convolutional neural network (CNN) to classify 10 different types of skin lesions, including melanoma and non-melanoma types.
The key technical contribution was to use multiple tracts (or paths) within the neural network, to train (and test) the network on an image using multiple image resolutions simultaneously. Additionally, we extended a CNN pretrained on a single image resolution to work for multiple image resolutions.
Here are our slides presented at MLMI (thanks Aïcha!) showing our deep learning approach to classify skin disease:
Continue reading “multi-resolution-tract CNN with hybrid pretrained and skin-lesion trained layers”
There’s this really neat new idea on how to train neural networks that recently came out know as generative adversarial nets (GAN).
The basic idea of a GAN is to train two networks to compete with each other (hence the name “adversarial“). One network (called the generator) creates images that look just like real images. The other network (called the discriminator) distinguishes between real images and those images the generator produced.
Thus the two networks compete with each other, where the generator generates images to fool the discriminator, and the discriminator discriminates between the generator’s images and real images.
Continue reading “(deep convolutional) generative adversarial nets – slides”
Perhaps you saw an earlier post I wrote about deep dreaming Prague pictures, and you said to your self, “self, I wish I knew more about the techniques to make those crazy looking pictures.”
Well you are in luck since I’ve now posted the slides where I attempted to explain these two works to our reading group: 1) Google’s DeepDream, and 2) A Neural Algorithm of Artistic Style.
Continue reading “Deep Dreams and a Neural Algorithm of Artistic Style – slides and explanations”
![Mastering the Game of Go – slides [paper explained]](https://kawahara.ca/wp-content/uploads/mastering-the-game-of-go_paper-explained.jpg)
