sits (version 0.13.0)

sits_mlp: Train a deep learning model using multi-layer perceptron

Description

Use a multi-layer perceptron algorithm to classify data. This function is a front-end to the "keras" method R package. Please refer to the documentation in that package for more details.

Usage

sits_mlp(
  samples = NULL,
  layers = c(512, 512, 512, 512),
  activation = "elu",
  dropout_rates = c(0.1, 0.2, 0.2, 0.3),
  optimizer = keras::optimizer_adam(lr = 0.001),
  epochs = 200,
  batch_size = 64,
  validation_split = 0.2,
  verbose = 0
)

Arguments

samples

Time series with the training samples.

layers

Vector with number of hidden nodes in each layer.

activation

Vector with the names of activation functions. Valid values are 'relu', 'elu', 'selu', 'sigmoid'.

dropout_rates

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

optimizer

Function with a pointer to the optimizer function (default is optimization_adam()). Options are optimizer_adadelta(), optimizer_adagrad(), optimizer_adam(), optimizer_adamax(), optimizer_nadam(), optimizer_rmsprop(), optimizer_sgd()

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.

verbose

Verbosity mode (0 = silent, 1 = progress bar, 2 = one line per epoch).

Value

Either a model to be passed in sits_predict or a function prepared to be called further.

References

Hassan Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, and Pierre-Alain Muller, "Deep learning for time series classification: a review", Data Mining and Knowledge Discovery, 33(4): 917--963, 2019.

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).

Implementation based on the python keras implementation provided in https://github.com/hfawaz/dl-4-tsc.

Examples

# NOT RUN {
# Retrieve the set of samples for the Mato Grosso region
data(samples_modis_4bands)
samples_mt_ndvi <- sits_select(samples_modis_4bands, bands = "NDVI")
# Build a machine learning model based on deep learning
dl_model <- sits_train(
    samples_mt_ndvi,
    sits_mlp(
        layers = c(64, 64),
        dropout_rates = c(0.50, 0.40),
        epochs = 50
    )
)
# get a point with a 16 year time series
point_ndvi <- sits_select(point_mt_6bands, bands = "NDVI")
# classify the point
point_class <- sits_classify(point_ndvi, dl_model)
# plot the classified point
plot(point_class)
# }