TensorFlow & Deep Learning

Tensorflow& Deep Learning

Irene LiPhD Student @ DITRA @ Barcelona Supercomputer Centre

OutlineDeep Learning ApplicationsTensorFlow- Introduction- Benefit- DemoMaterials

DL topicsImage recognitionSpeech recognitionVideo captioningNLP word embedding, TranslationBiological modelNeural ArtsGamesSelf-Driving CarsDeep Residual LearningGPUs

Neural ArtA Neural Algorithm of Artistic Style Leon A. Gatys, Alexander S. Ecker, Matthias Bethge

Neural Art

CNN for cv

VGG Model

Neural Art Style generationCode by Mark Chang, National Taiwan University https://github.com/ckmarkoh

Neural Art Style generationCode by Mark Chang, National Taiwan University https://github.com/ckmarkoh

Games Deep Q-Network (DQN)https://deepmind.com/dqn.htmlDeep Neural Network + Reinforcement Learninghow the game agents should act in an environment in order to maximize future cumulative reward (e.g., a game score)

These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play.

Games Deep Q-Network (DQN)Naveen Appiah, Sagar Vare, Stanford.Playing FlappyBird with Deep Reinforcement Learning

Develop a CNN model to learn features from snapshots of the game and train the agent to take the right actions at each game instance.

Games Deep Q-Network (DQN)Naveen Appiah, Sagar Vare, Stanford.Playing FlappyBird with Deep Reinforcement Learning

Deep neural network to learn game specific features from raw pixels and decide on what actions to take.Instead, a reinforcement learning setting tries to evaluate the actions at a given state based on the reward it observes by executing it.

Reinforcement learning:Robots

Self-driving cars: NVIDIA DRIVE PX

DualNVIDIA Tegra X1 processorsdelivering a combined 2.3 TeraflopsInterfaces for up to 12 cameras, radar, lidar, and ultrasonic sensorsRich middleware for graphics, computer vision, and deep learning

This allows algorithms to accurately understand the full 360 degree environment around the car to produce a robust representation, including static and dynamic objects. Use of Deep Neural Networks (DNN) for the detection and classification of objects dramatically increases the accuracy of the resulting fused sensor data. - See more at: http://www.nvidia.com/object/drive-px.html#sthash.Vq3ZHH11.dpuf

Self-driving cars: NVIDIA DRIVE PX

This allows algorithms to accurately understand the full 360 degree environment around the car to produce a robust representation, including static and dynamic objects. Use of Deep Neural Networks (DNN) for the detection and classification of objects dramatically increases the accuracy of the resulting fused sensor data. - See more at: http://www.nvidia.com/object/drive-px.html#sthash.Vq3ZHH11.dpuf

Baidu Self Driving

Nvidia self driving

Object Detection

Under circustanes

Semantic Image Segmentation

Under circustanes

Racing cars: NVIDIA DRIVE PX2

150 Macbook Pro

Deep residual networkfor image recognitionMicrosoft Research:ImageNet computer vision challenge championship3.57% error on the ImageNet test set.152 layers of deep networks, 8 times deeper than VGGImage classification, detection and localization

Deep Residual Learning for Image Recognition http://arxiv.org/pdf/1512.03385v1.pdf

NVIDIA DGX-1:Worlds first DL supercomputer

Eight Tesla P100 GPU accelerators, 16GB memory per GPU

The combination of these software capabilities running on Pascal-powered Tesla GPUs allows applications to run 12x faster than with previous GPU-accelerated solutions.

TensorFlow2ed Generation of Distributed ML Systems

DL FrameworksTensorFlow: Google MXNET: dmlc Theano: LISA Torch: Facebook, DeepMindCNTK: Microsoft Caffe: Berkley, Google

TensorFlow:open source dL SystemCore in C++.Supports Python and C++.

Jeff Dean & Oriol Vinyals Google, NIPS, 2015

Computation:a Dataflow graphEdges: Flow of tensors.Graph of Nodes: Operations.

Jeff Dean & Oriol Vinyals Google, NIPS, 2015

DistributedMultiple Devices!Send and Receive Nodes

Jeff Dean & Oriol Vinyals Google, NIPS, 2015

Model Parallelism

Easy LifeEverything is in Tensor

Softmax Function

https://www.tensorflow.org/versions/r0.7/tutorials/mnist/beginners/index.html#mnist-for-ml-beginnersxbWMatmulAddSoftmax

Easy LifeGradient Descent:

Initialization & launch

https://www.tensorflow.org/versions/r0.7/tutorials/mnist/beginners/index.html#mnist-for-ml-beginners

A Few TensorFlow Community Examples DQN: github.com/nivwusquorum/tensorflow-deepq NeuralArt: github.com/woodrush/neural-art-tf Char RNN: github.com/sherjilozair/char-rnn-tensorflow Keras ported to TensorFlow: github.com/fchollet/keras Show and Tell: github.com/jazzsaxmafia/show_and_tell.tensorflow Mandarin translation: github.com/jikexueyuanwiki/tensorflow-zh

Useful linksWant to master DL?

NNDLSGDBPregularizationDropout

http://neuralnetworksanddeeplearning.com/

Deep Learning Book

http://deeplearningbook.org

Reinforcement LearningRichard SuttonAndrew Barto

online courseshttps://www.youtube.com/user/hugolarochelle (python)https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/ (torch)https://www.coursera.org/course/neuralnets (matlab)http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html (rl)

TensorFlowGoogle open tensorflow in last yearhttp://tensorflow.org/https://github.com/tensorflow/tensorflowhttp://download.tensorflow.org/paper/whitepaper2015.pdf

Udacity course on tfhttps://www.udacity.com/course/deep-learning--ud730https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/udacityhttp://ireneli.eu/2016/03/13/tensorflow-04-implement-a-lenet-5-like-nn-to-classify-notmnist-images/

irenelizihui@gmail.comireneli.euThanks

Referenceshttp://indico.cern.ch/event/510372/attachments/1245509/1840815/lecun-20160324-cern.pdfhttp://www.jianshu.com/notebooks/339523/latesthttp://people.idsia.ch/~juergen/blues/IDSIA-07-02.pdfhttp://deepdreamgenerator.com/https://github.com/phanein/deepwalkhttp://www.cs.toronto.edu/~rsalakhu/papers/annrev.pdfhttp://www.jianshu.com/p/8799afd9b7c7 http://www.jianshu.com/p/10d70c5ceb39