Image
Amit
7/21/2018
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#installing EBImage package
source("https://bioconductor.org/biocLite.R")## Bioconductor version 3.6 (BiocInstaller 1.28.0), ?biocLite for help## A new version of Bioconductor is available after installing the most
## recent version of R; see http://bioconductor.org/installbiocLite("EBImage")## BioC_mirror: https://bioconductor.org## Using Bioconductor 3.6 (BiocInstaller 1.28.0), R 3.4.1 (2017-06-30).## Installing package(s) 'EBImage'##
## The downloaded binary packages are in
## /var/folders/xg/p_v17x4s1wvc7jkj9m_f33dc0000gn/T//RtmpjRrgsx/downloaded_packages## Old packages: 'caTools', 'foreign', 'fpc', 'plotly', 'robustbase',
## 'stringi', 'trimcluster'library(EBImage)## Warning: package 'EBImage' was built under R version 3.4.3library(keras)## Warning: package 'keras' was built under R version 3.4.4##
## Attaching package: 'keras'## The following object is masked from 'package:EBImage':
##
## normalize#Read Images
setwd("~/Desktop/DATASET/data/amit/")
pics<-c('amit.0.jpg','amit.1.jpg','amit.2.jpg','amit.3.jpg','amit.4.jpg','amit.5.jpg',
'lucy.0.jpg','lucy.1.jpg', 'lucy.2.jpg','lucy.3.jpg','lucy.4.jpg','lucy.5.jpg')
ourpic<-list()
for ( i in 1:12) {ourpic[[i]]<-readImage(pics[i])}
print(ourpic[[1]])## Image
## colorMode : Color
## storage.mode : double
## dim : 385 327 3
## frames.total : 3
## frames.render: 1
##
## imageData(object)[1:5,1:6,1]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.22352941 0.27058824 0.14509804 0.04705882 0.01568627 0.04313725
## [2,] 0.17254902 0.16078431 0.03529412 0.03137255 0.08235294 0.25490196
## [3,] 0.05882353 0.04313725 0.01568627 0.04313725 0.21568627 0.28627451
## [4,] 0.01176471 0.01176471 0.08235294 0.23921569 0.29803922 0.24705882
## [5,] 0.01960784 0.09803922 0.37647059 0.45490196 0.48235294 0.37254902display(ourpic[[1]])
summary(ourpic[[2]])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.3098 0.5451 0.5042 0.6902 1.0000hist(ourpic[[6]])
str(ourpic)## List of 12
## $ :Formal class 'Image' [package "EBImage"] with 2 slots
## .. ..@ .Data : num [1:385, 1:327, 1:3] 0.2235 0.1725 0.0588 0.0118 0.0196 ...
## .. ..@ colormode: int 2
## $ :Formal class 'Image' [package "EBImage"] with 2 slots
## .. ..@ .Data : num [1:278, 1:400, 1:3] 0.671 0.627 0.584 0.541 0.588 ...
## .. ..@ colormode: int 2
## $ :Formal class 'Image' [package "EBImage"] with 2 slots
## .. ..@ .Data : num [1:374, 1:336, 1:3] 0.1412 0.0941 0.0471 0.102 0.0549 ...
## .. ..@ colormode: int 2
## $ :Formal class 'Image' [package "EBImage"] with 2 slots
## .. ..@ .Data : num [1:200, 1:200, 1:3] 0.604 0.604 0.604 0.608 0.616 ...
## .. ..@ colormode: int 2
## $ :Formal class 'Image' [package "EBImage"] with 2 slots
## .. ..@ .Data : num [1:200, 1:200, 1:3] 0.537 0.541 0.545 0.545 0.525 ...
## .. ..@ colormode: int 2
## $ :Formal class 'Image' [package "EBImage"] with 2 slots
## .. ..@ .Data : num [1:200, 1:200, 1:3] 0.835 0.835 0.835 0.835 0.835 ...
## .. ..@ colormode: int 2
## $ :Formal class 'Image' [package "EBImage"] with 2 slots
## .. ..@ .Data : num [1:349, 1:326, 1:3] 0.0627 0.0627 0.0627 0.0627 0.0627 ...
## .. ..@ colormode: int 2
## $ :Formal class 'Image' [package "EBImage"] with 2 slots
## .. ..@ .Data : num [1:4032, 1:3024, 1:3] 0.357 0.357 0.361 0.365 0.365 ...
## .. ..@ colormode: int 2
## $ :Formal class 'Image' [package "EBImage"] with 2 slots
## .. ..@ .Data : num [1:960, 1:1280, 1:3] 1 1 1 1 1 ...
## .. ..@ colormode: int 2
## $ :Formal class 'Image' [package "EBImage"] with 2 slots
## .. ..@ .Data : num [1:4032, 1:3024, 1:3] 0.706 0.706 0.71 0.71 0.706 ...
## .. ..@ colormode: int 2
## $ :Formal class 'Image' [package "EBImage"] with 2 slots
## .. ..@ .Data : num [1:3088, 1:2320, 1:3] 0.996 0.996 0.996 0.996 0.996 ...
## .. ..@ colormode: int 2
## $ :Formal class 'Image' [package "EBImage"] with 2 slots
## .. ..@ .Data : num [1:4032, 1:3024, 1:3] 0.863 0.855 0.851 0.855 0.863 ...
## .. ..@ colormode: int 2#resizing the image
for (i in 1:12) {ourpic[[i]]<-resize(ourpic[[i]],28,28)}
str(ourpic)## List of 12
## $ :Formal class 'Image' [package "EBImage"] with 2 slots
## .. ..@ .Data : num [1:28, 1:28, 1:3] 0.9949 0.0739 0.3946 0.0313 0.33 ...
## .. ..@ colormode: int 2
## $ :Formal class 'Image' [package "EBImage"] with 2 slots
## .. ..@ .Data : num [1:28, 1:28, 1:3] 0.608 0.919 1 0.959 0.607 ...
## .. ..@ colormode: int 2
## $ :Formal class 'Image' [package "EBImage"] with 2 slots
## .. ..@ .Data : num [1:28, 1:28, 1:3] 0.0633 0.0486 0.2331 0.5387 0.2372 ...
## .. ..@ colormode: int 2
## $ :Formal class 'Image' [package "EBImage"] with 2 slots
## .. ..@ .Data : num [1:28, 1:28, 1:3] 0.628 0.579 0.615 0.6 0.638 ...
## .. ..@ colormode: int 2
## $ :Formal class 'Image' [package "EBImage"] with 2 slots
## .. ..@ .Data : num [1:28, 1:28, 1:3] 0.531 0.505 0.372 0.343 0.337 ...
## .. ..@ colormode: int 2
## $ :Formal class 'Image' [package "EBImage"] with 2 slots
## .. ..@ .Data : num [1:28, 1:28, 1:3] 0.835 0.835 0.835 0.839 0.839 ...
## .. ..@ colormode: int 2
## $ :Formal class 'Image' [package "EBImage"] with 2 slots
## .. ..@ .Data : num [1:28, 1:28, 1:3] 0.0627 0.0627 0.0627 0.0627 0.0627 ...
## .. ..@ colormode: int 2
## $ :Formal class 'Image' [package "EBImage"] with 2 slots
## .. ..@ .Data : num [1:28, 1:28, 1:3] 0.586 0.998 0.88 0.786 0.757 ...
## .. ..@ colormode: int 2
## $ :Formal class 'Image' [package "EBImage"] with 2 slots
## .. ..@ .Data : num [1:28, 1:28, 1:3] 1 0.995 0.992 0.992 0.992 ...
## .. ..@ colormode: int 2
## $ :Formal class 'Image' [package "EBImage"] with 2 slots
## .. ..@ .Data : num [1:28, 1:28, 1:3] 0.703 0.256 0.275 0.848 0.756 ...
## .. ..@ colormode: int 2
## $ :Formal class 'Image' [package "EBImage"] with 2 slots
## .. ..@ .Data : num [1:28, 1:28, 1:3] 1 0.992 1 1 0.665 ...
## .. ..@ colormode: int 2
## $ :Formal class 'Image' [package "EBImage"] with 2 slots
## .. ..@ .Data : num [1:28, 1:28, 1:3] 0.837 0.82 0.804 0.814 0.78 ...
## .. ..@ colormode: int 2#reshape
for (i in 1:12) {ourpic[[i]]<-array_reshape(ourpic[[i]],c(28,28,3))}
str(ourpic)## List of 12
## $ : num [1:28, 1:28, 1:3] 0.9949 0.0739 0.3946 0.0313 0.33 ...
## $ : num [1:28, 1:28, 1:3] 0.608 0.919 1 0.959 0.607 ...
## $ : num [1:28, 1:28, 1:3] 0.0633 0.0486 0.2331 0.5387 0.2372 ...
## $ : num [1:28, 1:28, 1:3] 0.628 0.579 0.615 0.6 0.638 ...
## $ : num [1:28, 1:28, 1:3] 0.531 0.505 0.372 0.343 0.337 ...
## $ : num [1:28, 1:28, 1:3] 0.835 0.835 0.835 0.839 0.839 ...
## $ : num [1:28, 1:28, 1:3] 0.0627 0.0627 0.0627 0.0627 0.0627 ...
## $ : num [1:28, 1:28, 1:3] 0.586 0.998 0.88 0.786 0.757 ...
## $ : num [1:28, 1:28, 1:3] 1 0.995 0.992 0.992 0.992 ...
## $ : num [1:28, 1:28, 1:3] 0.703 0.256 0.275 0.848 0.756 ...
## $ : num [1:28, 1:28, 1:3] 1 0.992 1 1 0.665 ...
## $ : num [1:28, 1:28, 1:3] 0.837 0.82 0.804 0.814 0.78 ...#Row Bind
trainx<-NULL
for (i in 1:5) {trainx<-rbind(trainx,ourpic[[i]])}
str(trainx)## num [1:5, 1:2352] 0.9949 0.6081 0.0633 0.6283 0.5308 ...for (i in 7:11) {trainx<-rbind(trainx,ourpic[[i]])}
str(trainx)## num [1:10, 1:2352] 0.9949 0.6081 0.0633 0.6283 0.5308 ...testx<-rbind(ourpic[[6]],ourpic[[12]])
trainy<-c(0,0,0,0,0,1,1,1,1,1)
testy<-c(0,1)#one hot encoding
trainlabel<-to_categorical(trainy)
testlabel<-to_categorical(testy)
trainlabel## [,1] [,2]
## [1,] 1 0
## [2,] 1 0
## [3,] 1 0
## [4,] 1 0
## [5,] 1 0
## [6,] 0 1
## [7,] 0 1
## [8,] 0 1
## [9,] 0 1
## [10,] 0 1testlabel## [,1] [,2]
## [1,] 1 0
## [2,] 0 1#model
model<-keras_model_sequential()
model %>%
layer_dense(units=256,activation='relu',input_shape=c(2352)) %>%
layer_dense(units=128,activation='relu')%>%
layer_dense(units=2,activation='softmax')
summary(model)## ___________________________________________________________________________
## Layer (type) Output Shape Param #
## ===========================================================================
## dense_1 (Dense) (None, 256) 602368
## ___________________________________________________________________________
## dense_2 (Dense) (None, 128) 32896
## ___________________________________________________________________________
## dense_3 (Dense) (None, 2) 258
## ===========================================================================
## Total params: 635,522
## Trainable params: 635,522
## Non-trainable params: 0
## ___________________________________________________________________________model %>%
compile(loss='binary_crossentropy',
optimizer=optimizer_rmsprop(),
metrics=c('accuracy'))
history<-model %>%
fit(trainx,
trainlabel,
epochs=30,
batch_size=32,
validation_split=0.2)
plot(history)
model %>%
evaluate(trainx,trainlabel)## $loss
## [1] 0.1731277
##
## $acc
## [1] 0.9pred<-model %>%
predict_classes(trainx)
table(Predicated=pred,Actual=trainy)## Actual
## Predicated 0 1
## 0 5 1
## 1 0 4prob<-model%>%
predict_proba(trainx)
cbind(prob,Prected=pred,Actual=trainy)## Prected Actual
## [1,] 0.97065282 0.029347217 0 0
## [2,] 0.95203716 0.047962870 0 0
## [3,] 0.97327948 0.026720496 0 0
## [4,] 0.96288842 0.037111521 0 0
## [5,] 0.99630320 0.003696774 0 0
## [6,] 0.05559082 0.944409192 1 1
## [7,] 0.02322231 0.976777732 1 1
## [8,] 0.02457794 0.975422025 1 1
## [9,] 0.37761804 0.622381985 1 1
## [10,] 0.63357282 0.366427183 0 1display(ourpic[[11]])## Only the first frame of the image stack is displayed.
## To display all frames use 'all = TRUE'.
model%>%
evaluate(testx,testlabel)## $loss
## [1] 0.2212991
##
## $acc
## [1] 1pred<-model%>%
predict_classes(testx)
pred## [1] 0 1table(Predicted=pred,Actual=testy)## Actual
## Predicted 0 1
## 0 1 0
## 1 0 1Including Plots
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