Zalando’s images classification using H2O with R

Fashion-MNIST

About three weeks ago the Fashion-MNIST dataset of Zalando’s article images, which is a great replacement of classical MNIST dataset, was released. In the following article we will try to build a strong classifier using H2O and R.

Each example is a 28×28 grayscale image, associated with a label from 10 classes:

1. T-shirt/top
2. Trouser
3. Pullover
4. Dress
5. Coat
6. Sandal
7. Shirt
8. Sneaker
9. Bag
10. Ankle boot

You can download it here https://www.kaggle.com/zalando-research/fashionmnist

The first column is an image label and the other 784 pixel columns are associated with the darkness of that pixel.

Quick reminder: what is H2O?

H2O is an open-source, fast, scalable platform for machine learning written in Java. It allows access to all of its capabilities from Python, Scala and most importantly from R via REST API.

Overview of available algorithms:

1. Supervised:
   • Deep Learning (Neural Networks)
   • Distributed Random Forest (DRF)
   • Generalized Linear Model (GLM)
   • Gradient Boosting Machine (GBM)
   • Naive Bayes Classifier
   • Stacked Ensembles
   • XGBoost
2. Unsupervised
   • Generalized Low Rank Models (GLRM)
   • K-Means Clustering
   • Principal Component Analysis (PCA)

Instalation is easy:

install.packages("h2o")library(h2o)

Building a neural network for image classification

Let’s start by running an H2O cluster:

h2o.init(ip="localhost",port=54321,nthreads=-1,min_mem_size="20g")

H2Oisnotrunningyet,startingitnow...Note:Incaseoferrorslookatthefollowinglogfiles:/tmp/RtmpQEf3RX/h2o_maju116_started_from_r.out/tmp/RtmpQEf3RX/h2o_maju116_started_from_r.erropenjdkversion"1.8.0_131"OpenJDKRuntimeEnvironment(build1.8.0_131-8u131-b11-2ubuntu1.16.04.3-b11)OpenJDK64-BitServerVM(build25.131-b11,mixedmode)StartingH2OJVMandconnecting:..Connectionsuccessful!RisconnectedtotheH2Ocluster:H2Oclusteruptime:1seconds906millisecondsH2Oclusterversion:3.13.0.3973H2Oclusterversionage:1monthand5daysH2Oclustername:H2O_started_from_R_maju116_cuf927H2Oclustertotalnodes:1H2Oclustertotalmemory:19.17GBH2Oclustertotalcores:8H2Oclusterallowedcores:8H2Oclusterhealthy:TRUEH2OConnectionip:localhostH2OConnectionport:54321H2OConnectionproxy:NAH2OInternalSecurity:FALSEH2OAPIExtensions:XGBoost,Algos,AutoML,CoreV3,CoreV4RVersion:Rversion3.4.1(2017-06-30)

Next we will import data into H2O using h2o.importFile() function, in which we can specify column types and column names if needed. If you want to send data into H2O directly from R, you can use as.h2o() function

fmnist_train<-h2o.importFile(path="data/fashion-mnist_train.csv",destination_frame="fmnist_train",col.types=c("factor",rep("int",784)))fmnist_test<-h2o.importFile(path="data/fashion-mnist_test.csv",destination_frame="fmnist_test",col.types=c("factor",rep("int",784)))

If everything went fine, we can check if our datasets are in H2O:

h2o.ls()

key1fmnist_test2fmnist_train

Before we begin modeling, let’s take a quick look at the data:

xy_axis<-data.frame(x=expand.grid(1:28,28:1)[,1],y=expand.grid(1:28,28:1)[,2])plot_theme<-list(raster=geom_raster(hjust=0,vjust=0),gradient_fill=scale_fill_gradient(low="white",high="black",guide=FALSE),theme=theme(axis.line=element_blank(),axis.text=element_blank(),axis.ticks=element_blank(),axis.title=element_blank(),panel.background=element_blank(),panel.border=element_blank(),panel.grid.major=element_blank(),panel.grid.minor=element_blank(),plot.background=element_blank()))sample_plots<-sample(1:nrow(fmnist_train),100)%>%map(~{plot_data<-cbind(xy_axis,fill=as.data.frame(t(fmnist_train[.x,-1]))[,1])ggplot(plot_data,aes(x,y,fill=fill))+plot_theme})do.call("grid.arrange",c(sample_plots,ncol=10,nrow=10))

Now we will build a simple neural network, with one hidden layer of ten neurons:

fmnist_nn_1<-h2o.deeplearning(x=2:785,y="label",training_frame=fmnist_train,distribution="multinomial",model_id="fmnist_nn_1",l2=0.4,ignore_const_cols=FALSE,hidden=10,export_weights_and_biases=TRUE)

If we set export_weights_and_biases parameter to TRUE networks weights and biases will be saved and we can retrieve them using h2o.weights() and h2o.biases() functions. Thanks to this we can try to visualize neurons from the hidden layer (Note that we set ignore_const_cols to FALSE to get weights for every pixel).

weights_nn_1<-as.data.frame(h2o.weights(fmnist_nn_1,1))biases_nn_1<-as.vector(h2o.biases(fmnist_nn_1,1))neurons_plots<-1:10%>%map(~{plot_data<-cbind(xy_axis,fill=t(weights_nn_1[.x,])+biases_nn_1[.x])colnames(plot_data)[3]<-"fill"ggplot(plot_data,aes(x,y,fill=fill))+plot_theme})do.call("grid.arrange",c(neurons_plots,ncol=3,nrow=4))

We can definitely see some resemblance to shirts and sneakers. Let’s test our model:

h2o.confusionMatrix(fmnist_nn_1,fmnist_test)

ConfusionMatrix:Rowlabels:Actualclass;Columnlabels:Predictedclass0123456789ErrorRate08011214872362512200.1990=199/10001693823251340000.0620=62/100022446957188184901500.3050=305/100034323128652113220100.1350=135/10004161384477014250200.2300=230/10005001008650907370.1350=135/100062736224532624610702810.8930=893/100070000010708380550.1620=162/1000841132253610889740.1030=103/100090000040010408560.1440=144/1000Totals1152990112011031249117824210419729530.2368=2368/10000

Accuracy 0.7632 isn’t a great result, but we didn’t use full capabilities of H2O yet. We should do something more advanced!

In h2o.deeplearning() function there’s over 70 parameters responsible for structure and optimization of our model. Changing thme should give as much better results.

fmnist_nn_final<-h2o.deeplearning(x=2:785,y="label",training_frame=fmnist_train,distribution="multinomial",model_id="fmnist_nn_final",activation="RectifierWithDropout",hidden=c(1000,1000,2000),epochs=180,adaptive_rate=FALSE,rate=0.01,rate_annealing=1.0e-6,rate_decay=1.0,momentum_start=0.4,momentum_ramp=384000,momentum_stable=0.98,input_dropout_ratio=0.22,l1=1.0e-5,max_w2=15.0,initial_weight_distribution="Normal",initial_weight_scale=0.01,nesterov_accelerated_gradient=TRUE,loss="CrossEntropy",fast_mode=TRUE,diagnostics=TRUE,ignore_const_cols=TRUE,force_load_balance=TRUE,seed=3.656455e+18)h2o.confusionMatrix(fmnist_nn_final,fmnist_test)

ConfusionMatrix:Rowlabels:Actualclass;Columnlabels:Predictedclass0123456789ErrorRate08980141511660500.1020=102/100012990260000000.0100=10/1000212187513601350300.1250=125/1000316118925231140200.0750=75/100041061218850300200.1150=115/10005001009640241100.0360=36/10006131266225007220700.2780=278/10007000001009630270.0370=37/100084141123298110.0190=19/1000900000603709570.0430=43/1000Totals10641005103110031020985870102610019950.0840=840/10000

Accuracy 0.916 is a lot better result, but there’s sitll a lot of thing we can do to improve our model. In the future we can consider using a grid or random search to find best hyperparameters or use same ensemble methods to get better results.

var vglnk = { key: '949efb41171ac6ec1bf7f206d57e90b8' }; (function(d, t) {var s = d.createElement(t); s.type = 'text/javascript'; s.async = true;s.src = '//cdn.viglink.com/api/vglnk.js';var r = d.getElementsByTagName(t)[0]; r.parentNode.insertBefore(s, r); }(document, 'script'));