sits_tuning: Tuning machine learning models hyper-parameters

In e-sensing/sits: Satellite Image Time Series Analysis for Earth Observation Data Cubes

Tuning machine learning models hyper-parameters

Description

Machine learning models use stochastic gradient descent (SGD) techniques to find optimal solutions. To perform SGD, models use optimization algorithms which have hyperparameters that have to be adjusted to achieve best performance for each application.

This function performs a random search on values of selected hyperparameters. Instead of performing an exhaustive test of all parameter combinations, it selecting them randomly. Validation is done using an independent set of samples or by a validation split. The function returns the best hyper-parameters in a list. Hyper-parameters passed to params parameter should be passed by calling sits_tuning_hparams().

Usage

sits_tuning(
  samples,
  samples_validation = NULL,
  validation_split = 0.2,
  ml_method = sits_tempcnn(),
  params = sits_tuning_hparams(optimizer = torch::optim_adamw, opt_hparams = list(lr =
    loguniform(10^-2, 10^-4))),
  trials = 30,
  multicores = 2,
  gpu_memory = 4,
  batch_size = 2^gpu_memory,
  progress = FALSE
)

Arguments

samples	Time series set to be validated.
samples_validation	Time series set used for validation.
validation_split	Percent of original time series set to be used for validation (if samples_validation is NULL)
ml_method	Machine learning method.
params	List with hyper parameters to be passed to ml_method. User can use uniform, choice, randint, normal, lognormal, loguniform, and beta distribution functions to randomize parameters.
trials	Number of random trials to perform the random search.
multicores	Number of cores to process in parallel.
gpu_memory	Memory available in GPU in GB (default = 4)
batch_size	Batch size for GPU classification.
progress	Show progress bar?

Value

A tibble containing all parameters used to train on each trial ordered by accuracy

Note

When using a GPU for deep learning, gpu_memory indicates the memory of the graphics card which is available for processing. The parameter batch_size defines the size of the matrix (measured in number of rows) which is sent to the GPU for classification. Users can test different values of batch_size to find out which one best fits their GPU architecture.

It is not possible to have an exact idea of the size of Deep Learning models in GPU memory, as the complexity of the model and factors such as CUDA Context increase the size of the model in memory. Therefore, we recommend that you leave at least 1GB free on the video card to store the Deep Learning model that will be used.

For users of Apple M3 chips or similar with a Neural Engine, be aware that these chips share memory between the GPU and the CPU. Tests indicate that the memsize should be set to half to the total memory and the batch_size parameter should be a small number (we suggest the value of 64). Be aware that increasing these parameters may lead to memory conflicts.

Author(s)

Rolf Simoes, [e-mail]

References

James Bergstra, Yoshua Bengio, "Random Search for Hyper-Parameter Optimization". Journal of Machine Learning Research. 13: 281–305, 2012.

Examples

if (sits_run_examples()) {
    # find best learning rate parameters for TempCNN
    tuned <- sits_tuning(
        samples_modis_ndvi,
        ml_method = sits_tempcnn(),
        params = sits_tuning_hparams(
            optimizer = choice(
                torch::optim_adamw
            ),
            opt_hparams = list(
                lr = loguniform(10^-2, 10^-4)
            )
        ),
        trials = 4,
        multicores = 2,
        progress = FALSE
    )
    # obtain best accuracy, kappa and best_lr
    accuracy <- tuned$accuracy[[1]]
    kappa <- tuned$kappa[[1]]
    best_lr <- tuned$opt_hparams[[1]]$lr
}

e-sensing/sits documentation built on Feb. 13, 2025, 2:22 a.m.