#set training data:
x_train <-  as.matrix(Daten[1:5300,3:ncol(Daten)])


#set model:
model <- keras_model_sequential()
model %>%
  layer_dense(units = 5, activation = "tanh",
          input_shape = ncol(x_train), name = "bottleneck")%>%
    layer_dense(units = ncol(x_train))

#compile model:
model %>% compile(loss= "mean_squared_error", optimizer = "adam",
metrics = c("accuracy"))

#train model:
model %>% fit(x=x_train, y= x_train, epochs = 50, batch_size=32, verbose
= 1)


#show the weights of the model:
weights <- get_weights(model)

print(weights)