model_rnn: A configurable recurrent neural network model

In krzjoa/torchts: Time series Models with torch

A configurable recurrent neural network model

Description

New features will be added in near future, e.g. categorical feature handling and so on.

Usage

model_rnn(
  rnn_layer = nn_gru,
  input_size,
  output_size,
  hidden_size,
  horizon = 1,
  embedding = NULL,
  initial_layer = nn_nonlinear,
  last_timesteps = 1,
  final_layer = nn_linear,
  dropout = 0,
  batch_first = TRUE
)

Arguments

rnn_layer	(nn_rnn_base) A recurrent torch layer.
input_size	(integer) Input size.
output_size	(integer) Output size (number of target variables).
hidden_size	(integer) A size of recurrent hidden layer.
horizon	(integer) Horizon size. How many steps ahead produce from the last n steps?
embedding	(embedding_spec) List with two values: num_embeddings and embedding_dim.
initial_layer	(nn_module) A torch module to preprocess numeric features before the recurrent layer.
final_layer	(nn_module) If not null, applied instead of default linear layer.
dropout	(logical) Use dropout.
batch_first	(logical) Channel order.

Examples

library(dplyr, warn.conflicts = FALSE)
library(torch)
library(torchts)

# Preparing data
weather_data <-
  weather_pl %>%
  filter(station == "TRN") %>%
  select(date, tmax_daily, rr_type) %>%
  mutate(rr_type = ifelse(is.na(rr_type), "NA", rr_type))

weather_dl <-
  weather_data %>%
  as_ts_dataloader(
    tmax_daily ~ date + tmax_daily + rr_type,
    timesteps = 30,
    categorical = "rr_type",
    batch_size = 32
  )

unique(weather_data$rr_type)
n_unique_values <- n_distinct(weather_data$rr_type)

.embedding_spec <-
   embedding_spec(
     num_embeddings = n_unique_values,
     embedding_dim  = embedding_size_google(n_unique_values)
   )

input_size <- 1 + embedding_size_google(n_unique_values) # tmax_daily + rr_type embedding

# Creating a model
rnn_net <-
  model_rnn(
    input_size  = input_size,
    output_size = 2,
    hidden_size = 10,
    horizon     = 10,
    embedding   = .embedding_spec
  )

print(rnn_net)

# Prediction example on non-trained neural network
batch <-
  dataloader_next(dataloader_make_iter(weather_dl))

# debugonce(rnn_net$forward)
rnn_net(batch$x_num, batch$x_cat)

krzjoa/torchts documentation built on June 24, 2022, 5:30 a.m.