sits_lstm_fcn: Train a Long Short Term Memory Fully Convolutional Network

In sits: Satellite Image Time Series Analysis for Earth Observation Data Cubes

Train a Long Short Term Memory Fully Convolutional Network

Description

Uses a branched neural network consisting of a lstm (long short term memory) branch and a three-layer fully convolutional branch (FCN) followed by concatenation to classify time series data.

This function is based on the paper by Fazle Karim, Somshubra Majumdar, and Houshang Darabi. If you use this method, please cite the original LSTM with FCN paper.

The torch version is based on the code made available by the titu1994. The original python code is available at the website https://github.com/titu1994/LSTM-FCN. This code is licensed as GPL-3.

Usage

sits_lstm_fcn(
  samples = NULL,
  samples_validation = NULL,
  cnn_layers = c(128, 256, 128),
  cnn_kernels = c(8, 5, 3),
  lstm_width = 8,
  lstm_dropout = 0.8,
  epochs = 50,
  batch_size = 64,
  validation_split = 0.2,
  optimizer = torch::optim_adamw,
  opt_hparams = list(lr = 5e-04, eps = 1e-08, weight_decay = 1e-06),
  lr_decay_epochs = 1,
  lr_decay_rate = 0.95,
  patience = 20,
  min_delta = 0.01,
  seed = NULL,
  verbose = FALSE
)

Arguments

samples	Time series with the training samples.
samples_validation	Time series with the validation samples. if the samples_validation parameter is provided, the validation_split parameter is ignored.
cnn_layers	Number of 1D convolutional filters per layer
cnn_kernels	Size of the 1D convolutional kernels.
lstm_width	Number of neuros in the lstm's hidden layer.
lstm_dropout	Dropout rate of the lstm layer.
epochs	Number of iterations to train the model.
batch_size	Number of samples per gradient update.
validation_split	Fraction of training data to be used for validation.
optimizer	Optimizer function to be used.
opt_hparams	Hyperparameters for optimizer: lr : Learning rate of the optimizer eps: Term added to the denominator to improve numerical stability. weight_decay: L2 regularization
lr_decay_epochs	Number of epochs to reduce learning rate.
lr_decay_rate	Decay factor for reducing learning rate.
patience	Number of epochs without improvements until training stops.
min_delta	Minimum improvement in loss function to reset the patience counter.
seed	Seed for random values.
verbose	Verbosity mode (TRUE/FALSE). Default is FALSE.

Value

A fitted model to be used for classification.

Author(s)

Alexandre Assuncao, [e-mail]

References

F. Karim, S. Majumdar, H. Darabi and S. Chen, "LSTM Fully Convolutional Networks for Time Series Classification," in IEEE Access, vol. 6, pp. 1662-1669, 2018, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1109/ACCESS.2017.2779939")}.

sits documentation built on Sept. 9, 2025, 5:54 p.m.