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.
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
)A fitted model to be used for classification.
Time series with the training samples.
Time series with the validation samples. if the
samples_validation parameter is provided,
the validation_split parameter is ignored.
Number of 1D convolutional filters per layer
Size of the 1D convolutional kernels.
Number of neuros in the lstm's hidden layer.
Dropout rate of the lstm layer.
Number of iterations to train the model.
Number of samples per gradient update.
Fraction of training data to be used for validation.
Optimizer function to be used.
Hyperparameters for optimizer: lr : Learning rate of the optimizer eps: Term added to the denominator to improve numerical stability. weight_decay: L2 regularization
Number of epochs to reduce learning rate.
Decay factor for reducing learning rate.
Number of epochs without improvements until training stops.
Minimum improvement in loss function to reset the patience counter.
Seed for random values.
Verbosity mode (TRUE/FALSE). Default is FALSE.
Alexandre Assuncao, alexcarssuncao@gmail.com
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, tools:::Rd_expr_doi("10.1109/ACCESS.2017.2779939").