Hello!

I need to create a dataset encoded with autoenceders.

The data set has 500 records as follows:

I have installed and loaded the following libraries:

- library("rminer")
- library("keras")
- library("tensorflow")
- library("reticulate")

I have divided my data set into two parts, training and test, with the holdout method:

Division <- holdout(y=Datos$recid)

where **Datos** is my dataset

I get the training and test data, removing the column that is the data that you want to predict:

- x_train <- subset(Datos[Division$tr,], select = -recid)
- x_test <- subset(Datos[Division$ts,], select = -recid)

Then I convert the data set to matrix (I do not know if this is necessary)

- x_train_matrix = data.matrix(x_train)
- x_test_matrix = data.matrix(x_test)

At this moment, what I want to do is the following:

- Define a coding model, with an input layer and the coding layer.
- Extract the encoded data, for later take this reduction of characteristics to use them in other training models

original_dim <- 7L #334L

encoding_dim <- 4L #32L

input_img <- layer_input(shape = c(original_dim))

encoded<- layer_dense(input_img,encoding_dim , activation = "relu")

autoencoder <- keras_model(input_img, encoded)

autoencoder %>% compile(optimizer = 'adadelta', loss = 'binary_crossentropy')

Am I on the right path?

I need help to achieve the goal I have tried to explain, I hope you help me.

Thank you very much