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ParallelR/PP_for_COS/deepnet_mnist.R
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# Examples for the R and Parallel Computing blog in COS website (cos.name) | |
# Author: Peng Zhao, 9/18/2016 | |
# Example 1: Using Parallel Libraries | |
# using 'deepnet' and 'mnist' dataset for example | |
# see the performance w/ and w/o parallel BLAS backend | |
#install.packages("data.table") | |
#install.packages("deepnet") | |
library(data.table) | |
library(deepnet) | |
# download MNIST dataset in below links | |
# https://h2o-public-test-data.s3.amazonaws.com/bigdata/laptop/mnist/train.csv.gz | |
# https://h2o-public-test-data.s3.amazonaws.com/bigdata/laptop/mnist/test.csv.gz | |
# OR: http://www.parallelr.com/materials/3_ParDNN/ | |
mnist.train <- as.matrix(fread("./train.csv", header=F)) | |
mnist.test <- as.matrix(fread("./test.csv", header=F)) | |
# V785 is the label | |
x <- mnist.train[, 1:784]/255 | |
y <- model.matrix(~as.factor(mnist.train[, 785])-1) | |
system.time( | |
nn <- dbn.dnn.train(x,y, | |
hidden=c(64), | |
output="softmax", | |
batchsize=128, numepochs=100, learningrate = 0.1) | |
) | |
# OpenBLAS | |
# env LD_PRELOAD=/.../tools/OpenBLAS/lib/libopenblas.so R CMD BATCH deepnet_mnist.R | |
#begin to train dbn ...... | |
#training layer 1 rbm ... | |
#dbn has been trained. | |
#begin to train deep nn ...... | |
####loss on step 10000 is : 0.193343 | |
####loss on step 20000 is : 0.121218 | |
####loss on step 30000 is : 0.127029 | |
####loss on step 40000 is : 0.159519 | |
#deep nn has been trained. | |
# user system elapsed | |
# 2197.394 10496.190 867.748 | |
# native R | |
# R CMD BATCH deepnet_mnist.R | |
#begin to train dbn ...... | |
#training layer 1 rbm ... | |
#dbn has been trained. | |
#begin to train deep nn ...... | |
####loss on step 10000 is : 0.179346 | |
####loss on step 20000 is : 0.123266 | |
####loss on step 30000 is : 0.136734 | |
####loss on step 40000 is : 0.085222 | |
#deep nn has been trained. | |
# user system elapsed | |
#2110.710 2.311 2115.042 | |