Sony's deep learning software "Neural Network Libraries/Console and its use cases in Sony

Transcription

1 GTC 2018 Sony's deep learning software "Neural Network Libraries/Console and its use cases in Sony Yoshiyuki Kobayashi Senior Machine Learning Researcher Sony Network Communications Inc. / Sony Corporation

2 Sony's deep learning software Neural Network Libraries nnabla.org Neural Network Console dl.sony.com Deep learning framework with Python API Open source (Apache 2.0 license) GUI based deep learning IDE Windows version is now available for free. 2

3 Used since 2011 and more than 1,000 users in Sony group. Just released in ~ 1st gen framework 2013~ 2nd gen framework 2016~ 3rd gen framework Neural Network Libraries Open Sourced at Jul ~ GUI Tool Neural Network Console Released Windows Version at Aug for Free Already utilized in many products and services. Sony real estate AR Effect Xperia Ear Xperia Hello aibo 3

4 Motivation Deep Learning is great and demand is explosively growing Deep learning engineer is lacking worldwide SOLD Need to make deep learning research & development more efficient Also, human resource development needs to be accelerated 4

5 Introduction of Neural Network Console 5

6 Application Example #1:Sony Real Estate Real Estate Price Estimate Neural Network Libraries is used in Real Estate Price Estimate Engine of Sony Real Estate Corporation. the Library realizes the solution that statistically estimates signed price in buying and selling real estate, analyzing massive data with unique algorism developed based on evaluation know-how and knowledge of Sony Real Estate Corporation. The solution is utilized in various businesses of Sony Real Estate Corporation such as Ouchi Direct, Real Estate Property Search Map and Automatic Evaluation. Input Bunch of features about a real estate Output Price of a real estate Total floor area Floor plan Street address 6

7 Application Example #2:Xperia Ear Gesture Sensitivity The Library is used in an intuitive gesture sensitivity function of Sony Mobile Communications Xperia Ear. Based on data from several sensors embedded in Xperia Ear, you can just use a nod of the head to confirm a command - answering Yes/No, answering/declining the phone call, cancelling text-to-speech reading of notifications, skipping/rewinding a song track. Input Sensor data Output Head gestures such as Yes and No 7

8 Application Example #3:aibo Image Recognition (User Identification, Face Tracking, etc.) The Library is used to realize image recognition of Sony s Entertainment Robot "aibo" ERS In the image recognition through fish-eye cameras installed at the nose, the Library is actively utilized for user identification, face tracking, charge stand recognition, generic object recognition, etc. These features and various inbuilt sensors enable its adaptable behavior. 入力 Image Output Face, object, Charge stand, etc 8

9 Application of DL spreading depending on input / output Input Function Output Function Input Output Image recognition Image Category of image Image filtering Source image target image Speech recognition Speech String Machine translation (En->Ja) English word string Japanese word string Chat-bot Input word string String of response Sensor error detection Sensor signal Abnormality level Robot control Robot's sensor Robot actuator The possibilities of application are infinite 9

10 Feature 1. Ideal for deep learning beginners Easy to setup Just unzip the downloaded file and run the application Select GPU in Setup window to use NVIDIA GPU You can learn deep learning visually Trial and error in a short time Convolutional Neural Networks Multi layer perceptron Logistic regression Provides shortcut for skill improvement on deep learning 10

11 Feature 2. Realize very efficient development Easy debugging Instantly locate the error on GUI Automatic structure search search for better neural network structure automatically Easy to deploy Created model can be deployed immediately by using Neural Network Libraries NNL offers both Python and C++ API 11

12 More features Functions Management of trial and error history Visualization of weights Export python code Supports Various data as well as images, matrices and vectors Various objectives such as classification, detection, signal processing, regression, etc. Multiple inputs and outputs for multi-modal application Training of huge neural networks like ResNet-152 Recurrent neural networks (RNN) Generative Adversarial Networks (GAN) Semi-supervised learning Transfer learning etc 12

13 Neural Network Libraries Lightweight C++ core library with Python API Flexible for arbitrary NNs Cross-platform & Cross-device Easy to add a new NN operator Portable to C++ Sophisticated Easy-to-write & easy-to-read Fast training and inference with CUDA/cuDNN Supports both static and dynamic NNs Supports distributed training Supports fp16 and Tensor Cores Resources NNabla-Examles ResNet, GANs, CapsNet, Quantized Nets, Jupyter Notebook Tutorials pip install nnabla import nnabla as nn import nnabla.functions as F import nnabla.parametric_functions as PF Suitable for both research and production use x = nnabla.variable((batch_size, 1, 28, 28)) c1 = PF.convolution(x, 16, (5, 5), name='c1') c1 = F.relu(F.max_pooling(c1, (2, 2))) c2 = PF.convolution(c1, 16, (5, 5), name='c2') c2 = F.relu(F.max_pooling(c2, (2, 2))) f3 = F.relu(PF.affine(c2, 50, name='f3')) y = PF.affine(f3, 50, name='f4') t = Variable((batch_size, 1)) loss = F.mean(F.softmax_cross_entropy(y, t)) 13

14 Sony's deep learning software Neural Network Libraries Neural Network Console nnabla.org dl.sony.com Deep learning framework with Python API Open source (Apache 2.0 license) GUI based deep learning IDE Windows version is now available for free. Sony is providing an integrated development environment with hopes that a wider range of developers and researchers will build on its programs, and with the aim of contributing to the development of society. 14

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16 Appendix 16

17 Tutorial Image classification (hand-writing digits recognition) Input : image Classifier (Neural Network) Output : Classification result 2 28x28 pixel Number of input neuron is size of input data Number of output neural is number of categories of target data Take a typical application Hand-writing digits recognition as an example 17

18 Work required to create a image classifier 1. Prepare dataset Prepare as much data as possible Each data includes image and its class index Design neural network structure You can use sample project 3. Train designed neural network using prepared dataset Input : Image Classifier Output : (Neural Network) Classification result 2 Basically you can create image classifier with these three steps 18

19 1. Prepare dataset Create dataset CSV files according to a predetermined format File name of hand-writing digit class Index x:image y:label./training/5/0.png 5./training/0/1.png 0./training/4/2.png 4./training/1/3.png 1./training/9/4.png 9./training/2/5.png 2./training/1/6.png 1 It can be created with Excel or simple script 19

20 2. Design neural network structure Convolutional Neural Networks Put layers as it is by drag & drop, and edit properties of layers 5 x 5 Convolution Activation 2 x 2 Pooling 3 x 3 Convolution Activation 2 x 2 Pooling Affine Activation Fully connected Softmax Categorical Loss Add layers by drag & drop Edit property of layer You can design the network structure very easily and intuitively 20

21 3. Train designed neural network using prepared dataset Training starts just by pushing the training button loss When the training is complete, an image classifier is obtained training iteration 21