sits_resnet {sits}

Train ResNet classification models

Description

Use a ResNet architecture for classifying image time series. The ResNet (or deep residual network) was proposed by a team in Microsoft Research for 2D image classification. ResNet tries to address the degradation of accuracy in a deep network. The idea is to replace a deep network with a combination of shallow ones. In the paper by Fawaz et al. (2019), ResNet was considered the best method for time series classification, using the UCR dataset. Please refer to the paper for more details.

The R-torch version is based on the code made available by Zhiguang Wang, author of the original paper. The code was developed in python using keras.

https://github.com/cauchyturing (repo: UCR_Time_Series_Classification_Deep_Learning_Baseline)

The R-torch version also considered the code by Ignacio Oguiza, whose implementation is available at https://github.com/timeseriesAI/tsai/blob/main/tsai/models/ResNet.py.

There are differences between Wang's Keras code and Oguiza torch code. In this case, we have used Wang's keras code as the main reference.

Usage

sits_resnet(
  samples = NULL,
  samples_validation = NULL,
  blocks = c(64, 128, 128),
  kernels = c(7, 5, 3),
  epochs = 100,
  batch_size = 64,
  validation_split = 0.2,
  optimizer = torch::optim_adamw,
  opt_hparams = list(lr = 0.001, eps = 1e-08, weight_decay = 1e-06),
  lr_decay_epochs = 1,
  lr_decay_rate = 0.95,
  patience = 20,
  min_delta = 0.01,
  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.

blocks

Number of 1D convolutional filters for each block of three layers.

kernels

Size of the 1D convolutional kernels

epochs

Number of iterations to train the model. for each layer of each block.

batch_size

Number of samples per gradient update.

validation_split

Fraction of training data to be used as validation data.

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.

verbose

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

Value

A fitted model to be used for classification.

Note

Please refer to the sits documentation available in <https://e-sensing.github.io/sitsbook/> for detailed examples.

Author(s)

Gilberto Camara, [e-mail]

Rolf Simoes, [e-mail]

Felipe Souza, [e-mail]

Alber Sanchez, [e-mail]

Charlotte Pelletier, [e-mail]

Daniel Falbel, [e-mail]

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).

Examples

if (sits_run_examples()) {
    # create a ResNet model
    torch_model <- sits_train(samples_modis_ndvi, sits_resnet())
    # 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",
        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)
}