sits (version 1.5.2)

sits_mlp: Train multi-layer perceptron models using torch

Description

Use a multi-layer perceptron algorithm to classify data. This function uses the R "torch" and "luz" packages. Please refer to the documentation of those package for more details.

Usage

sits_mlp(
  samples = NULL,
  samples_validation = NULL,
  layers = c(512, 512, 512),
  dropout_rates = c(0.2, 0.3, 0.4),
  optimizer = torch::optim_adamw,
  opt_hparams = list(lr = 0.001, eps = 1e-08, weight_decay = 1e-06),
  epochs = 100,
  batch_size = 64,
  validation_split = 0.2,
  patience = 20,
  min_delta = 0.01,
  verbose = FALSE
)

Value

A torch mlp model to be used for classification.

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.

layers

Vector with number of hidden nodes in each layer.

dropout_rates

Vector with the dropout rates (0,1) for each layer.

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

epochs

Number of iterations to train the model.

batch_size

Number of samples per gradient update.

validation_split

Number between 0 and 1. Fraction of the training data for validation. The model will set apart this fraction and will evaluate the loss and any model metrics on this data at the end of each epoch.

patience

Number of epochs without improvements until training stops.

min_delta

Minimum improvement in loss function to reset the patience counter.

verbose

Verbosity mode (TRUE/FALSE). Default is FALSE.

Author

Gilberto Camara,

References

Zhiguang Wang, Weizhong Yan, and Tim Oates, "Time series classification from scratch with deep neural networks: A strong baseline", 2017 international joint conference on neural networks (IJCNN).

Examples

if (sits_run_examples()) {
    # create an MLP model
    torch_model <- sits_train(samples_modis_ndvi,
           sits_mlp(epochs = 20, verbose = TRUE))
    # plot the model
    plot(torch_model)
    # create a data cube from local files
    data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
    cube <- sits_cube(
        source = "BDC",
        collection = "MOD13Q1-6.1",
        data_dir = data_dir
    )
    # classify a data cube
    probs_cube <- sits_classify(
        data = cube, ml_model = torch_model, output_dir = tempdir()
    )
    # plot the probability cube
    plot(probs_cube)
    # smooth the probability cube using Bayesian statistics
    bayes_cube <- sits_smooth(probs_cube, output_dir = tempdir())
    # plot the smoothed cube
    plot(bayes_cube)
    # label the probability cube
    label_cube <- sits_label_classification(
        bayes_cube,
        output_dir = tempdir()
    )
    # plot the labelled cube
    plot(label_cube)
}