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Introduction to Generative Adversarial Networks (GAN) with Apache MXNet

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Amazon Web Services
Amazon Web Services

GANs are a type of deep neural network that allow us to generate data. In this webinar, we’ll take a look at the concept and theory behind GANs, which can be used to train neural nets with data that is generated by the network. We’ll explore the GAN framework along with its components -- generator and discriminator networks. We’ll then learn how to use Apache MXNet on AWS using the popular MNIST dataset, which contains images of handwritten numbers. In the end, we’ll create a GAN model that is able to generate similar images of handwritten numbers from our test dataset.

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|Webinars© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Sunil Mallya & Yash Pant, Amazon AI September 2017 Introduction to Generative Adversarial Networks using Apache MXNet
|Webinars • Intro to AI and Deep Learning • Intro to GANs (Generative Adversarial Networks) • GAN Research & Applications • GAN Implementation & Demo Agenda
|Webinars Tons of GPUs and CPUs Serverless At the Edge, On IoT Devices Prediction The Challenge For Artificial Intelligence: SCALE Tons of GPUs Elastic capacity Training Pre-built images Aggressive migration New data created on AWS Data PBs of existing data
|Webinars Amazon AI
|Webinars 0.2 -0.1 ... 0.7 Input Output 1 1 1 1 0 1 0 0 0 3 mx.sym.Pooling(data, pool_type="max", kernel=(2,2), stride=(2,2) lstm.lstm_unroll(num_lstm_layer, seq_len, len, num_hidden, num_embed) 4 2 2 0 4=Max 1 3 ... 4 0.2 -0.1 ... 0.7 mx.sym.FullyConnected(data, num_hidden=128) 2 mx.symbol.Embedding(data, input_dim, output_dim = k) Queen 4 2 2 0 2=Avg Input Weights cos(w, queen) = cos(w, king) - cos(w, man) + cos(w, woman) mx.sym.Activation(data, act_type="xxxx") "relu" "tanh" "sigmoid" "softrelu" Neural Art Face Search Image Segmentation Image Caption “People Riding Bikes” Bicycle, People, Road, Sport Image Labels Image Video Speech Text “People Riding Bikes” Machine Translation “Οι άνθρωποι ιππασίας ποδήλατα” Events mx.model.FeedForward model.fit mx.sym.SoftmaxOutput Anatomy of a Deep Learning Model mx.sym.Convolution(data, kernel=(5,5), num_filter=20) Deep Learning Models
|Webinars Artificial Neuron output synaptic weights input Input Vector of training data x Output Linear functions of inputs Nonlinearity Transform output into desired range of values, e.g. for classification we need probabilities [0, 1] Training Learn the weights w and bias b
|Webinars Deep Neural Network hidden layers The optimal size of the hidden layer (number of neurons) is usually between the size of the input and size of the output layers Input layer output
|Webinars The “Learning” in Deep Learning 0.4 0.3 0.2 0.9 ... back propogation (gradient descent) X1 != X 0.4 ± 𝛿 0.3 ± 𝛿 new weights new weights 0 1 0 1 1 . . - - X input label ... X1
|Webinars Gradient Descent
|Webinars Convolution Neural Network (CNN) CNN Layers Convolutional Layer Pooling Layer Activation Fully-Connected Layer
|Webinars GAN (Generative Adversarial Networks) Input: Noise Generator Generated (“Fake”) Data Data From Dataset (“Real”) Discriminator Real or Fake? Framework for creating generative models
|Webinars Image Generation Unsupervised Representation Learning With Deep Convolutional Generative Adversarial Networks (Radford et. al 2016)
|Webinars Image Arithmetic Unsupervised Representation Learning With Deep Convolutional Generative Adversarial Networks (Radford et. al 2016)
|Webinars SRGAN: Making Images High Resolution Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (Ledig et. al 2016 )
|Webinars StackGAN: Create Images from Text StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks (Zhang et. al 2016)
|Webinars 3D-GAN: 3D Models from Images Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling (Wu et al 2017)
|Webinars CycleGAN: Image to Image Translation Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks (Zhu et al 2017)
|Webinars DiscoGAN: Discover Relationships Between Images Learning to Discover Cross-Domain Relations with Generative Adversarial Networks (Kim et al 2017) Gender conversion Hair color conversion
|Webinars Implementation: DCGAN (Deep Convolutional Generative Adversarial Network) Goal: Create a model that is able to generate realistic looking images from a given dataset MNIST Dataset
|Webinars DCGAN: Concept Overview [ .76 .14 .83 -.06 ] Z “Fake” Image “Real” Image “Fake” Image
|Webinars DCGAN: Implementation Walkthrough Code available at: https://github.com/yash1/mxnet- notebooks/blob/master/python/tutorials/dcgan_create_images.ipynb
|Webinars AWS Deep Learning AMI: One-Click Deep Learning Kepler, Volta & Skylake Apache MXNet Python 2/3 Notebooks & ExamplesTensorflow, Keras, Caffe2.. https://aws.amazon.com/amazon-ai/amis/
|Webinars Thank you!

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Introduction to Generative Adversarial Networks (GAN) with Apache MXNet

  • 1. |Webinars© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Sunil Mallya & Yash Pant, Amazon AI September 2017 Introduction to Generative Adversarial Networks using Apache MXNet
  • 2. |Webinars • Intro to AI and Deep Learning • Intro to GANs (Generative Adversarial Networks) • GAN Research & Applications • GAN Implementation & Demo Agenda
  • 3. |Webinars Tons of GPUs and CPUs Serverless At the Edge, On IoT Devices Prediction The Challenge For Artificial Intelligence: SCALE Tons of GPUs Elastic capacity Training Pre-built images Aggressive migration New data created on AWS Data PBs of existing data
  • 5. |Webinars 0.2 -0.1 ... 0.7 Input Output 1 1 1 1 0 1 0 0 0 3 mx.sym.Pooling(data, pool_type="max", kernel=(2,2), stride=(2,2) lstm.lstm_unroll(num_lstm_layer, seq_len, len, num_hidden, num_embed) 4 2 2 0 4=Max 1 3 ... 4 0.2 -0.1 ... 0.7 mx.sym.FullyConnected(data, num_hidden=128) 2 mx.symbol.Embedding(data, input_dim, output_dim = k) Queen 4 2 2 0 2=Avg Input Weights cos(w, queen) = cos(w, king) - cos(w, man) + cos(w, woman) mx.sym.Activation(data, act_type="xxxx") "relu" "tanh" "sigmoid" "softrelu" Neural Art Face Search Image Segmentation Image Caption “People Riding Bikes” Bicycle, People, Road, Sport Image Labels Image Video Speech Text “People Riding Bikes” Machine Translation “Οι άνθρωποι ιππασίας ποδήλατα” Events mx.model.FeedForward model.fit mx.sym.SoftmaxOutput Anatomy of a Deep Learning Model mx.sym.Convolution(data, kernel=(5,5), num_filter=20) Deep Learning Models
  • 6. |Webinars Artificial Neuron output synaptic weights input Input Vector of training data x Output Linear functions of inputs Nonlinearity Transform output into desired range of values, e.g. for classification we need probabilities [0, 1] Training Learn the weights w and bias b
  • 7. |Webinars Deep Neural Network hidden layers The optimal size of the hidden layer (number of neurons) is usually between the size of the input and size of the output layers Input layer output
  • 8. |Webinars The “Learning” in Deep Learning 0.4 0.3 0.2 0.9 ... back propogation (gradient descent) X1 != X 0.4 ± 𝛿 0.3 ± 𝛿 new weights new weights 0 1 0 1 1 . . - - X input label ... X1
  • 10. |Webinars Convolution Neural Network (CNN) CNN Layers Convolutional Layer Pooling Layer Activation Fully-Connected Layer
  • 11. |Webinars GAN (Generative Adversarial Networks) Input: Noise Generator Generated (“Fake”) Data Data From Dataset (“Real”) Discriminator Real or Fake? Framework for creating generative models
  • 12. |Webinars Image Generation Unsupervised Representation Learning With Deep Convolutional Generative Adversarial Networks (Radford et. al 2016)
  • 13. |Webinars Image Arithmetic Unsupervised Representation Learning With Deep Convolutional Generative Adversarial Networks (Radford et. al 2016)
  • 14. |Webinars SRGAN: Making Images High Resolution Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (Ledig et. al 2016 )
  • 15. |Webinars StackGAN: Create Images from Text StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks (Zhang et. al 2016)
  • 16. |Webinars 3D-GAN: 3D Models from Images Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling (Wu et al 2017)
  • 17. |Webinars CycleGAN: Image to Image Translation Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks (Zhu et al 2017)
  • 18. |Webinars DiscoGAN: Discover Relationships Between Images Learning to Discover Cross-Domain Relations with Generative Adversarial Networks (Kim et al 2017) Gender conversion Hair color conversion
  • 19. |Webinars Implementation: DCGAN (Deep Convolutional Generative Adversarial Network) Goal: Create a model that is able to generate realistic looking images from a given dataset MNIST Dataset
  • 21. |Webinars DCGAN: Implementation Walkthrough Code available at: https://github.com/yash1/mxnet- notebooks/blob/master/python/tutorials/dcgan_create_images.ipynb
  • 22. |Webinars AWS Deep Learning AMI: One-Click Deep Learning Kepler, Volta & Skylake Apache MXNet Python 2/3 Notebooks & ExamplesTensorflow, Keras, Caffe2.. https://aws.amazon.com/amazon-ai/amis/