1d:T7c9,# Train an instance of `lbbnn_net`. Function that for each epoch iterates through each mini-batch, computing the loss and using back-propagation to update network parameters. ```r train_lbbnn( epochs, LBBNN, lr, train_dl, device = "cpu", scheduler = NULL, sch_step_size = NULL ) ``` ## Arguments - `epochs`: integer, total number of epochs to train for, where one epoch is a pass through the entire training dataset (all mini batches). - `LBBNN`: An instance of `lbbnn_net`, to be trained. - `lr`: numeric, the learning rate to be used in the Adam optimizer. - `train_dl`: An instance of `torch::dataloader` consisting of a tensor dataset with features and targets. - `device`: the device to be trained on. Default is 'cpu', also accepts 'gpu' or 'mps'. - `scheduler`: A torch learning rate scheduler object. Can be used to decay learning rate for better convergence, currently only supports 'step'. - `sch_step_size`: Where to decay if using `torch::lr_step`. E.g. 1000 means learning rate is decayed every 1000 epochs. ## Returns a list containing the losses and accuracy (if classification) and density for each epoch during training. For comparisons sake we show the density with and without active paths. A list with elements (returned invisibly): - **accs**: Vector of accuracy per epoch (classification only). - **loss**: Vector of average loss per epoch. - **density**: Vector of network densities per epoch. ## Examples ```r x<-torch::torch_randn(3,2) b <- torch::torch_rand(2) y <- torch::torch_matmul(x,b) train_data <- torch::tensor_dataset(x,y) train_loader <- torch::dataloader(train_data,batch_size = 3,shuffle=FALSE) problem<-'regression' sizes <- c(2,1,1) inclusion_priors <-c(0.9,0.2) inclusion_inits <- matrix(rep(c(-10,10),2),nrow = 2,ncol = 2) stds <- c(1.0,1.0) model <- lbbnn_net(problem,sizes,inclusion_priors,stds,inclusion_inits, flow = FALSE) output <- train_lbbnn(epochs = 1,LBBNN = model, lr = 0.01, train_dl = train_loader) ```

train_lbbnn function