RUCK 2017 MxNet과 R을 연동한 딥러닝 소개

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한재근

INTRODUCTION TO DEEP LEARNING WITH R AND MXNET

NVIDIA Corporation

R User Conference KoreaDATE 28/03/2017

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TOPICS

• MXNet and R

• Heart Disease

• MRI Dataset and Evaluation

• Lab

• Discussion / Overview

• Launching the Lab Environment

• Lab Review

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MXNET (and R)

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What is MXNET?mxnet.io / github.com/apache/incubator-mxnet

—“Open source software library for efficiency and flexibility”. Available on GitHub

—Scales up to multi GPUs and distributed setting with auto parallelism

—Cloud-friendly and directly compatible with S3, HDFS, and Azure

—Amazon’s main DL platform / Accepted to Apache Incubator on Jan, 2017

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MXNET and R• MXNet library and read data into R structure from CSV

• Normalize data

train <- read.csv(“train.csv”, header=TRUE)test <- read.csv(“test.csv”, header=TRUE)train <- data.matrix(train)test <- data.matrix(test)

train.x <- train[,-1]train.y <- train[,1]train.x <- t(train.x/255)test <- t(test/255)

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MXNET and R• MXNet library and read data into R structure from record file

data.shape <- c(args$data_shape, args$data_shape, 3)train = mx.io.ImageRecordIter(

path.imgrec = file.path(args$data_dir, args$train_dataset),

batch.size = args$batch_size,data.shape = data.shape,optimizer = "sgd",initializer = mx.init.Xavier(factor_type="in", magnitude=2),mean.r = 123.68,mean.g = 116.779,mean.b = 103.939,rand.crop = TRUE,rand.mirror = TRUE

)

data normalization

augmentation

※ rec file generation: ./bin/im2rec train.txt ${dataset} train.rec resize=256python im2rec.py --resize 256 --num-thread 16 train.rec ${dataset}

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MXNET and R• Architect a network

library(mxnet)get_symbol <- function(num_classes = 1000) {

network <- lenet()data <- mx.symbol.Variable(“data”)

conv1 <- mx.symbol.Convolution(data = data, kernel = c(5, 5), num_filter = 20)tanh1 <- mx.symbol.Activation(data = conv1, act_type = "tanh")pool1 <- mx.symbol.Pooling(data = tanh1, pool_type = "max", kernel = c(2, 2), stride = c(2, 2))

conv2 <- mx.symbol.Convolution(data = pool1, kernel = c(5, 5), num_filter = 50)tanh2 <- mx.symbol.Activation(data = conv2, act_type = "tanh")

pool2 <- mx.symbol.Pooling(data = tanh2, pool_type = "max", kernel = c(2, 2), stride = c(2, 2))flatten <- mx.symbol.Flatten(data = pool2)fc1 <- mx.symbol.FullyConnected(data = flatten, num_hidden = 500)

tanh3 <- mx.symbol.Activation(data = fc1, act_type = "tanh")fc2 <- mx.symbol.FullyConnected(data = tanh3, num_hidden = num_classes)lenet <- mx.symbol.SoftmaxOutput(data = fc2, name = 'softmax')return(lenet)

}

1st convolution

2nd convolution

first full conn.

second full conn.

loss

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MXNET and R• Train a network

mx.set.seed(0)model <- mx.model.FeedForward.create(

softmax, X=train.x, y=train.y,ctx=mx.gpu(0),num.round=10, array.batch.size=100, learning.rate=0.07,momentum=0.9,eval.metric=mx.metric.accuracy,initializer=mx.init.uniform(0.07),batch.end.callback = mx.callback.log.train.metric(100))

GPU select with index, array for multi-gpublank or mx.cpu() runs cpu only

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Inference

Inference from trained parameter

predictions <- predict(model, data)

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Model Weights Save & Load

Network snapshot

Load Pre-trained Network

mx.model.save(model, "model_snapshot", 5)

model <- mx.model.load("model_snapshot", 5)

parameter iterationsfile name

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HEART DISEASE

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HEART DISEASE

• Cardiovascular (Heart) disease

• Heart attack – blood flow to a part of the heart is blocked by a blood clot

• Stroke – blood to brain is blocked (ischemic) or blood vessel is blocked (hemorrhagic)

• Heart failure – heart is not pumping blood as well as the body needs

• Arrhythmia – abnormal rhythm of the heart

• About 610,000 Americans die from heart disease each year1

1 Mozzafarian D, Benjamin EJ, Go AS, et al. on behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics—2016 update: a report from the American Heart Association. Circulation. 2016;133:e38-e360.

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HEART DISEASE• Systole – contracts and

pumps blood from the chambers into the arteries

• Diastole – relaxation of the heart muscle allowing the chamber to fill with blood

Image from 2015 Data Science Bowl

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HEART DISEASE• Ejection Fraction

• Percentage of blood leaving the heart each time it contracts

• VS = volume at systole

• VD = volume at diastole

Image from 2015 Data Science Bowl

100×𝑉& − 𝑉(𝑉&

PROBLEMS BEING SOLVEDAutomated heart volume and ejection fraction analysis for disease prediction

AUTOMATEMANUAL

Skilled cardiologist

Long time, up to 20 minutes to complete

Cardiologist’s time spend with the patients

Impediments for heart disease treatment research

Easy diagnosis

Shorten procedure time

Advanced heart disease treatment

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MRI DATASET AND EVALUATION

IMAGE EXAMPLEHeartbeat sample

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MRI DATASET

• MRI images from more than 500 patients

• The National Heart, Lung, and Blood Institute (NHLBI) provided images for the 2015 Data Science Bowl1

• National Institutes of Health

• Children’s National Medical Center

1Data Science Bowl is a joint effort between Booz Allen Hamilton & Kaggle

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MRI DATASET

• MRI images in DICOM format

• About 30 images across the cardiac cycle

• Goal is to create an automated method capable of determining the left ventricle volume at two points in time

• After systole – when the heart is contracted and the ventricles are at their minimum volume

• After diastole – when the heart is at its largest volume

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MRI DATASET• The main view for assessing

ventricle size is the short axis stack (PSAX)

• Contains images taken in a plane perpendicular to the long axis (PLAX) of the left ventricle

• These have the prefix "sax_" in the dataset

• Most cases also have alternative views

DATASETfrom Second Data Science Bowl MRI dataset

https://www.kaggle.com/c/second-annual-data-science-bowl/data

x500 training studies (systole & diastole)

x10 SAX observations (average, different view)

x30 DICOM images for 1 heart beat

64x64 pixel sized image

X 10 X 50030

64x64

Systole

Diastole

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EVALUATION• Continuous Rank Probability Score

(CRPS)

• P = predicted distribution• N = number of rows in the test set

• Equal to twice the number of cases

• V = actual volume in (ml)• H(x) = Heavyside step function

• H(x < 0) = 0 and H(x > 0) = 1

𝐶𝑅𝑃𝑆 = 1

600𝑁 0 0(𝑃 𝑦 ≤ 𝑛 − 𝐻 𝑛 − 𝑉6 )89::

;<=

>

?<@

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LAB OVERVIEW

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LAB OVERVIEW• Build an end-to-end deep learning system to help diagnose heart

disease

• Dataset contains 500 training studies• Each study averages 10 unique SAX observations at various points along the

PLAX of the left ventricle• Each SAX observation of a study is the same heart

• Roughly 5000 observations in total

• Each SAX observation usually contain 30 DICOM images (“frames”) in a time sequence

• Captures an entire heartbeat (expansion and contraction)

• Pack each of the 30 image frames of an observation into a 64x64x30 tensor

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LAB OVERVIEW• Same 64x64x30 deck of images used to predict systole and diastole

• Build a network for systole (code provided)

• Build a network for diastole (students create)

• Will use LeNet style CNN with batch normalization and dropout

• Predict a CDF value of 600 data-point

• This is a regression problem

• One predicted value for each of the 600 points in the CDF

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LAB OVERVIEW1. Load MXNet and other libraries

2. Architect the network

3. Load the training data

4. Create loss (cost) function

5. Train the model (specify hyperparameters)

6. Save the trained model

7. Make inference using new data

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LAB & CODE

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LOAD MXNET

Load MXNet library using either library(mxnet) or require(mxnet)

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LOAD TRAINING DATA

train-64x64-data.csv train-systole.csv

# of

sam

ples

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ARCHITECT THE NETWORK

Convolution layer

Activation layer

Drop out layer

pre-process

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CREATE LOSS FUNCTION

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TRAIN THE MODEL

Hyperparameters

Loss value per round (epoch)

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SAVE THE TRAINED MODEL

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INFERENCES for SYSTOLE

Predicting systolic volume for one of the studies

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INFERENCES for DIASTOLE

Predicting diastolic volume for one of the studies

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Evaluation

Achieved CRPS score were 0.025 ~ 0.05 > 80th % in competition

CRPS score related to the predict resolution

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WHAT ELSE?• Many ways to explore and improve model

• Try removing batch normalization layers [hint: modify get.lenet()]

• Try incr./decr. number of features in the convolution layer [hint: modify num.filter in get.lenet()]

• Modify the learning rate of the training phase [hint: learning.rate]

• CRPS function uses the residual squared [hint: try abs in costfun]

• Try using different activation function (i.e. other than relu)

• Try using different pooling functions (i.e. other than max)

• Try removing dropout layer or modifying the % dropout (i.e. default is 50%)

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www.nvidia.com/dli

Thanks