Deep Learning with R Cookbook

How to do it...

Let's move on to create a neural network using symbolic programming:

1. We start with sampling data into training and validation datasets and creating respective iterators:

batch_size <- 3train_ids <- 1:4val_ids <- 5:6## create data iteratorstrain_data <- mx.io.arrayiter(data = x_data[,,train_ids, drop = F],label = y_data[, train_ids], batch.size = batch_size,shuffle = TRUE)val_data <- mx.io.arrayiter(data = x_data[,,val_ids, drop = F], label = y_data[, val_ids], batch.size = batch_size, shuffle = FALSE)

2. Now, let's create an RNN symbol with a one-to-one model configuration:

symbol <- rnn.graph(num_rnn_layer = 2, num_hidden = 30, input_size = NULL, num_embed = NULL, num_decode = 1, masking = F,  loss_output = "linear", ignore_label ...