Create ImageRecognitionAndClassificationWithR

Author: bkrai

ImageRecognitionAndClassificationWithR

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+# Load Packages
+library(EBImage)
+library(keras)
+
+# Read images
+setwd('---')
+pics <- c('p1.jpg', 'p2.jpg', 'p3.jpg', 'p4.jpg', 'p5.jpg', 'p6.jpg',
+          'c1.jpg', 'c2.jpg', 'c3.jpg', 'c4.jpg', 'c5.jpg', 'c6.jpg')
+mypic <- list()
+for (i in 1:12) {mypic[[i]] <- readImage(pics[i])}
+
+# Explore
+print(mypic[[1]])
+display(mypic[[8]])
+summary(mypic[[1]])
+hist(mypic[[2]])
+str(mypic)
+
+# Resize
+for (i in 1:12) {mypic[[i]] <- resize(mypic[[i]], ---, ---)}
+
+# Reshape
+for (i in 1:12) {mypic[[i]] <- array_reshape(mypic[[i]], c(---, ---,---))}
+
+# Row Bind
+trainx <- NULL
+for (i in 7:11) {trainx <- rbind(trainx, mypic[[i]])}
+str(trainx)
+testx <- ---(mypic[[6]], mypic[[12]])
+trainy <- c(0,0,0,0,0,1,1,1,1,1 )
+testy <- c(---, ---)
+
+# One Hot Encoding
+trainLabels <- ---(trainy)
+testLabels <- ---(testy)
+
+# Model
+model <- keras_model_sequential()
+model %>%
+         layer_dense(units = 256, activation = ---, input_shape = c(2352)) %>%
+         layer_dense(units = 128, activation = 'relu') %>%
+         layer_dense(units = 2, activation = ---)
+summary(model)
+
+# Compile
+model %>%
+         compile(loss = ---,
+                 optimizer = optimizer_rmsprop(),
+                 metrics = c('accuracy'))
+
+# Fit Model
+history <- model %>%
+         fit(trainx,
+             ---,
+             epochs = 30,
+             batch_size = 32,
+             validation_split = 0.2)
+
+# Evaluation & Prediction - train data
+model %>% evaluate(---, ---)
+pred <- model %>% predict_classes(trainx)
+table(Predicted = pred, Actual = trainy)
+prob <- model %>% predict_proba(trainx)
+cbind(prob, Prected = pred, Actual= trainy)