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CNN_Semeion_Keras(1).r
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library(data.table)
mydat <- fread('https://archive.ics.uci.edu/ml/machine-learning-databases/semeion/semeion.data')
head(mydat)
semeion<-mydat[,1:256]
dataVal<-mydat[,257:266]
semeion[,which(is.na(semeion))]
# Converting flatten data to Numeric numbers
semeion$digit <- NA
semeion$digit[which(mydat$V257 == 1)] <- 0
semeion$digit[which(mydat$V258 == 1)] <- 1
semeion$digit[which(mydat$V259 == 1)] <- 2
semeion$digit[which(mydat$V260 == 1)] <- 3
semeion$digit[which(mydat$V261 == 1)] <- 4
semeion$digit[which(mydat$V262 == 1)] <- 5
semeion$digit[which(mydat$V263 == 1)] <- 6
semeion$digit[which(mydat$V264 == 1)] <- 7
semeion$digit[which(mydat$V265 == 1)] <- 8
semeion$digit[which(mydat$V266 == 1)] <- 9
#semeion
#dataVal
#labels(semeion)
set.seed(101) # Set Seed so that same sample can be reproduced in future also
# Now Selecting 80% of data as sample from total 'n' rows of the data
sample <- sample.int(n = nrow(semeion), size = floor(.80*nrow(semeion)), replace = F)
CNN_Ttrain <- semeion[sample, ]
CNN_Ttest <- semeion[-sample, ]
X_Train<-CNN_Ttrain[, -257]
Y_Train<-CNN_Ttrain[, 257]
X_Test<-CNN_Ttest[, -257]
Y_Test<-CNN_Ttest[, 257]
library(reshape2)
sapply(Y_Train, function(x) sum(is.na(x)))
# reshape
head(Y_Train)
dim(Y_Train)
#dim(X_Train) <- c(nrow(X_Train),dim(X_Train)[2])
Y_Train <- data.matrix(Y_Train)
X_Train <- data.matrix(X_Train)
X_Test <- data.matrix(X_Test)
Y_Test <- data.matrix(Y_Test)
ncol(X_Train)
dim(X_Train)[2]
library(keras)
model <- keras_model_sequential()
model %>%
layer_dense(units = 128, activation = "relu", input_shape = dim(X_Train)[2]) %>%
layer_dropout(rate = 0.4) %>%
layer_dense(units = 64, activation = "relu") %>%
layer_dropout(rate = 0.3) %>%
layer_dense(units = 10, activation = "softmax")
summary(model)
model %>% compile(
loss = "sparse_categorical_crossentropy" , #"categorical_crossentropy"
optimizer = 'adam', # optimizer_rmsprop(),
metrics = c("accuracy")
)
history <- model %>% fit(
X_Train, Y_Train,
epochs = 1000, batch_size = 5,
validation_split = 0.2
)
plot(history)
#before running the model need to convert data to matrix form for keras model
X_Test <- data.matrix(X_Test)
Y_Test <- data.matrix(Y_Test)
model %>% evaluate(X_Test, Y_Test,verbose = 1)
predicted_Y_Test <- model %>% predict_classes(X_Test)
table(predicted_Y_Test, Y_Test)
#tunning by using epochs= 200 and batch_size = 30
history <- model %>% fit(
X_Train, Y_Train,
epochs = 200, batch_size = 30,
validation_split = 0.2
)
plot(history)
model %>% evaluate(X_Test, Y_Test,verbose = 1)
predicted_Y_Test <- model %>% predict_classes(X_Test)
table(predicted_Y_Test, Y_Test)
model <- keras_model_sequential()
model %>%
layer_dense(units = 256, activation = 'relu', input_shape = dim(X_Train)[2]) %>%
layer_dropout(rate = 0.2) %>%
layer_dense(units = 128, activation = 'relu') %>%
layer_dropout(rate = 0.2) %>%
layer_dense(units = 10, activation = 'softmax')
summary(model)
model %>% compile(
loss = "sparse_categorical_crossentropy" , #"categorical_crossentropy"
optimizer = 'adam', # optimizer_rmsprop(),
metrics = c("accuracy")
)
history <- model %>% fit(
X_Train, Y_Train,
epochs = 250, batch_size = 125,
validation_split = 0.01,
verbose= 2,
shuffle='True'
)
plot(history)
model %>% evaluate(X_Test, Y_Test,verbose = 1)
predicted_Y_Test <- model %>% predict_classes(X_Test)
table(predicted_Y_Test, Y_Test)
model <- keras_model_sequential()
model %>%
layer_dense(units = 256, activation = 'relu', input_shape = dim(X_Train)[2]) %>%
layer_dropout(rate = 0.1) %>%
layer_dense(units = 128, activation = 'relu') %>%
layer_dropout(rate = 0.1) %>%
layer_dense(units = 10, activation = 'softmax')
summary(model)
model %>% compile(
loss = "sparse_categorical_crossentropy" , #"categorical_crossentropy"
optimizer = 'adam', # optimizer_rmsprop(),
metrics = c("accuracy")
)
history <- model %>% fit(
X_Train, Y_Train,
epochs = 25, batch_size = 128,
validation_split = 0.011,
verbose = 2,
shuffle="True"
)
plot(history)
model %>% evaluate(X_Test, Y_Test,verbose = 1)
predicted_Y_Test <- model %>% predict_classes(X_Test)
table(predicted_Y_Test, Y_Test)
#calculating time to measure the model run time using KERAS
start_time <- Sys.time()
model <- keras_model_sequential()
model %>%
layer_dense(units = 1024, activation = 'relu', input_shape = dim(X_Train)[2]) %>%
layer_dropout(rate = 0.1) %>%
layer_dense(units = 512, activation = 'relu') %>%
layer_dropout(rate = 0.1) %>%
layer_dense(units = 256, activation = 'relu') %>%
layer_dropout(rate = 0.1) %>%
layer_dense(units = 128, activation = 'relu') %>%
layer_dropout(rate = 0.1) %>%
layer_dense(units = 10, activation = 'softmax')
summary(model)
model %>% compile(
loss = "sparse_categorical_crossentropy" , #"categorical_crossentropy"
optimizer = 'adam', # optimizer_rmsprop(),
metrics = c("accuracy")
)
history <- model %>% fit(
X_Train, Y_Train,
epochs = 200, batch_size = 128,
validation_split = 0.02,
verbose = 2,
shuffle="True"
)
plot(history)
model %>% evaluate(X_Test, Y_Test,verbose = 1)
predicted_Y_Test <- model %>% predict_classes(X_Test)
table(predicted_Y_Test, Y_Test)
end_time <- Sys.time()
run_time <- end_time - start_time
print("--------------------------------------------------------------------------------------")
paste("Total run time for Keras model", run_time, " seconds")
#calculating time to measure the model run time using KERAS
start_time <- Sys.time()
model <- keras_model_sequential()
model %>%
layer_dense(units = 1024, activation = 'relu', input_shape = dim(X_Train)[2]) %>%
layer_dropout(rate = 0.1) %>%
layer_dense(units = 512, activation = 'relu') %>%
layer_dropout(rate = 0.1) %>%
layer_dense(units = 256, activation = 'relu') %>%
layer_dropout(rate = 0.1) %>%
layer_dense(units = 128, activation = 'relu') %>%
layer_dropout(rate = 0.1) %>%
layer_dense(units = 10, activation = 'softmax')
summary(model)
model %>% compile(
loss = "sparse_categorical_crossentropy" , #"categorical_crossentropy"
optimizer = 'adam', # optimizer_rmsprop(),
metrics = c("accuracy")
)
history <- model %>% fit(
X_Train, Y_Train,
epochs = 200, batch_size = 129,
validation_split = 0.02,
verbose = 2,
shuffle="True"
)
plot(history)
model %>% evaluate(X_Test, Y_Test,verbose = 1)
predicted_Y_Test <- model %>% predict_classes(X_Test)
table(predicted_Y_Test, Y_Test)
end_time <- Sys.time()
run_time <- end_time - start_time
print("--------------------------------------------------------------------------------------")
paste("Total run time for Keras model", run_time, " seconds")
library('forecast')
predicted <- data.frame(predicted_Y_Test)
actual <- data.frame(Y_Test)
error <- predicted - actual
rmse <- sqrt(mean(error^2))
mae <- mean(abs(error))
rmse
ls()
model <- keras_model_sequential()
model %>%
layer_dense(units = 128, activation = "relu", input_shape = dim(X_Train)[2]) %>%
layer_dropout(rate = 0.4) %>%
layer_dense(units = 64, activation = "relu") %>%
layer_dropout(rate = 0.3) %>%
layer_dense(units = 10, activation = "softmax")
summary(model)
model %>% compile(
loss = "sparse_categorical_crossentropy" , #"categorical_crossentropy"
optimizer = 'adam', # optimizer_rmsprop(),
metrics = c("accuracy")
)
history <- model %>% fit(
X_Train, Y_Train,
epochs = 200, batch_size = 100,
validation_split = 0.2
)
plot(history)
model %>% evaluate(X_Test, Y_Test,verbose = 1)
predicted_Y_Test <- model %>% predict_classes(X_Test)
table(predicted_Y_Test, Y_Test)
end_time <- Sys.time()
run_time <- end_time - start_time
print("--------------------------------------------------------------------------------------")
paste("Total run time for Keras model", run_time, " seconds")
model <- keras_model_sequential()
model %>%
layer_dense(units = 128, activation = "relu", input_shape = dim(X_Train)[2]) %>%
layer_dropout(rate = 0.2) %>%
layer_dense(units = 64, activation = "relu") %>%
layer_dropout(rate = 0.2) %>%
layer_dense(units = 10, activation = "softmax")
summary(model)
model %>% compile(
loss = "sparse_categorical_crossentropy" , #"categorical_crossentropy"
optimizer = 'adam', # optimizer_rmsprop(),
metrics = c("accuracy")
)
history <- model %>% fit(
X_Train, Y_Train,
epochs = 200, batch_size = 120,
validation_split = 0.2
)
plot(history)
model %>% evaluate(X_Test, Y_Test,verbose = 1)
predicted_Y_Test <- model %>% predict_classes(X_Test)
table(predicted_Y_Test, Y_Test)
end_time <- Sys.time()
run_time <- end_time - start_time
print("--------------------------------------------------------------------------------------")
paste("Total run time for Keras model", run_time, " seconds")
get_config(model)
get_layer(model, index = 2)
model$layers
model$inputs
model$outputs
# Plot the accuracy of the training data
plot(history$metrics$acc, main="Model Accuracy", xlab = "epoch", ylab="accuracy", col="blue", type="l")
# Plot the accuracy of the validation data
lines(history$metrics$val_acc, col="green")
# Add Legend
legend("bottomright", c("train","test"), col=c("blue", "green"), lty=c(1,1))
# Plot the model loss of the training data
plot(history$metrics$loss, main="Model Loss", xlab = "epoch", ylab="loss", col="blue", type="l")
# Plot the model loss of the test data
lines(history$metrics$val_loss, col="green")
# Add legend
legend("topright", c("train","test"), col=c("blue", "green"), lty=c(1,1))
model <- keras_model_sequential()
model %>%
layer_dense(units = 128, activation = "relu", input_shape = dim(X_Train)[2]) %>%
layer_dropout(rate = 0.2) %>%
layer_dense(units = 64, activation = "relu") %>%
layer_dropout(rate = 0.2) %>%
layer_dense(units = 10, activation = "softmax")
summary(model)
model %>% compile(
loss = "sparse_categorical_crossentropy" , #"categorical_crossentropy"
optimizer = 'sgd', # optimizer_rmsprop(),
metrics = c("accuracy")
)
history <- model %>% fit(
X_Train, Y_Train,
epochs = 200, batch_size = 128,
validation_split = 0.2
)
plot(history)
model %>% evaluate(X_Test, Y_Test,verbose = 1)
predicted_Y_Test <- model %>% predict_classes(X_Test)
table(predicted_Y_Test, Y_Test)
end_time <- Sys.time()
run_time <- end_time - start_time
print("--------------------------------------------------------------------------------------")
paste("Total run time for Keras model", run_time, " seconds")
# Plot the model loss
plot(history$metrics$loss, main="Model Loss", xlab = "epoch", ylab="loss", col="blue", type="l")
lines(history$metrics$val_loss, col="green")
legend("topright", c("train","test"), col=c("blue", "green"), lty=c(1,1))
# Plot the model accuracy
plot(history$metrics$acc, main="Model Accuracy", xlab = "epoch", ylab="accuracy", col="blue", type="l")
lines(history$metrics$val_acc, col="green")
legend("bottomright", c("train","test"), col=c("blue", "green"), lty=c(1,1))
#calculating time to measure the model run time using KERAS
start_time <- Sys.time()
model <- keras_model_sequential()
model %>%
layer_dense(units = 1024, activation = 'relu', input_shape = dim(X_Train)[2]) %>%
layer_dropout(rate = 0.1) %>%
layer_dense(units = 512, activation = 'relu') %>%
layer_dropout(rate = 0.1) %>%
layer_dense(units = 256, activation = 'relu') %>%
layer_dropout(rate = 0.1) %>%
layer_dense(units = 128, activation = 'relu') %>%
layer_dropout(rate = 0.1) %>%
layer_dense(units = 10, activation = 'softmax')
summary(model)
model %>% compile(
loss = "sparse_categorical_crossentropy" , #"categorical_crossentropy"
optimizer = 'adam', # optimizer_rmsprop(),
metrics = c("accuracy")
)
history <- model %>% fit(
X_Train, Y_Train,
epochs = 23, batch_size = 128,
validation_split = 0.02,
verbose = 2,
shuffle="True"
)
plot(history)
model %>% evaluate(X_Test, Y_Test,verbose = 1)
predicted_Y_Test <- model %>% predict_classes(X_Test)
table(predicted_Y_Test, Y_Test)
end_time <- Sys.time()
run_time <- end_time - start_time
print("--------------------------------------------------------------------------------------")
paste("Total run time for Keras model", run_time, " seconds")
paste("RMSE : ", sqrt(mean((data.frame(predicted_Y_Test) - data.frame(Y_Test))^2)))
# Plot the model loss
plot(history$metrics$loss, main="Model Loss", xlab = "epoch", ylab="loss", col="blue", type="l")
lines(history$metrics$val_loss, col="green")
legend("topright", c("train","test"), col=c("blue", "green"), lty=c(1,1))
# Plot the model accuracy
plot(history$metrics$acc, main="Model Accuracy", xlab = "epoch", ylab="accuracy", col="blue", type="l")
lines(history$metrics$val_acc, col="green")
legend("bottomright", c("train","test"), col=c("blue", "green"), lty=c(1,1))