Create Deep Learning Multiclass Classification

Author: bkrai

Deep Learning Multiclass Classification

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+# Install packages
+library(---)
+install_keras()
+
+# Read data
+data <- read.csv(---, header = T)
+str(data)
+
+# Change to matrix
+data <- as.matrix(data)
+dimnames(data) <- NULL
+
+# Normalize
+data[, 1:21] <- normalize(---)
+data[,22] <- as.numeric(data[,22]) -1
+summary(data)
+
+# Data partition
+set.seed(1234)
+ind <- sample(2, nrow(data), replace = T, prob = c(---, ---))
+training <- data[---]
+test <- data[---]
+trainingtarget <- data[ind==1, 22]
+testtarget <- data[ind==2, 22]
+
+# One Hot Encoding
+trainLabels <- to_categorical(trainingtarget)
+testLabels <- ---(testtarget)
+print(testLabels)
+
+# Create sequential model
+model <- keras_model_sequential()
+model %>%
+         layer_dense(units=8, activation = ---, input_shape = ---) %>%
+         ---(units = 3, activation = ---)
+summary(model)
+
+# Compile
+model %>%
+         compile(loss = ---,
+                 optimizer = ---,
+                 metrics = ---)
+
+# Fit model
+history <- model %>%
+         fit(training,
+             trainLabels,
+             epoch = 200,
+             batch_size = 32,
+             validation_split = 0.2)
+plot(history)
+
+# Evaluate model with test data
+model1 <- model %>%
+         evaluate(test, testLabels)
+
+# Prediction & confusion matrix - test data
+prob <- model %>%
+         ---(test)
+
+pred <- model %>%
+         ---(test)
+table1 <- table(Predicted = pred, Actual = testtarget)
+
+cbind(prob, pred, testtarget)
+
+# Fine-tune model