library(rtensorflow)
check_mnist <- function(model_path, csv_path) {
initializeSessionVariables()
loadSavedModel(model_path, c("train", "serve"))
training_iters <- 2000
batch_size <- 128
display_step <- 10
# Read MNIST data CSV file
data <- read.csv(file=csv_path, header=TRUE, sep=',')
print ("Data read successful")
# Extract label column
y_train <- data[,"label"]
# One hot Encoder for the labels
col <- 10
row <- length(y_train)
onehot <- array(data=rep(0, col * row),dim=c(row, col))
onehot[cbind(1:row, y_train + 1)] <- 1
y_train <- onehot
# Drop label for getting X training data
drops <- c("label")
X_train <- data[ , !(names(data) %in% drops)]
X_train <- X_train/255
step <- 0
for (i in 1:training_iters) {
samples <- sample(1:nrow(X_train), batch_size, replace=FALSE)
feedInput("x",X_train[samples,])
feedInput("y",y_train[samples,])
feedInput("keep_prob",c(0.75))
runSession(c("train"))
if (step%%display_step==0) {
feedInput("x",X_train[samples,])
feedInput("y",y_train[samples,])
feedInput("keep_prob",c(1.))
display <- runSession(c("cost","accuracy"))
cat("Iter ",i, ", ")
cat("Cost=", display[["cost"]])
cat(", Training Accuracy=", display[["accuracy"]],"\n")
}
step <- step+1
}
print ("Optimization Finished!")
deleteSessionVariables()
}
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