downscalePredict.keras: Downscale climate data for a a previous defined deep learning...

In SantanderMetGroup/downscaleR.keras: A climate4R package for statistical downscaling <http://meteo.unican.es/climate4r>

Downscale climate data for a a previous defined deep learning keras model.

Description

Downscale data to local scales by deep learning models previously obtained by downscaleTrain.keras.

Usage

downscalePredict.keras(
  newdata,
  model,
  C4R.template,
  clear.session = FALSE,
  loss = NULL
)

Arguments

newdata	The grid data. It should be an object as returned by prepareNewData.keras.
model	An object containing the statistical model as returned from downscaleTrain.keras or a list of arguments passed to load_model_hdf5.
C4R.template	A climate4R grid that serves as template for the returned prediction object.
clear.session	A logical value. Indicates whether we want to destroy the current tensorflow graph and clear the model from memory. In particular, refers to whether we want to use the function k_clear_session after training. If FALSE, model is returned. If TRUE, then k_clear_session() is applied and no model is returned. Default to FALSE.
loss	Default to NULL. Otherwise a string indicating the loss function used to train the model. This is only relevant where we have used the 2 custom loss functions of this library: "gaussianLoss" or "bernouilliGammaLoss"

Details

This function relies on keras, which is a high-level neural networks API capable of running on top of tensorflow, CNTK or theano. There are official keras tutorials regarding how to build deep learning models. We suggest the user, especially the beginners, to consult these tutorials before using downscalePredict.keras.

Value

A regular/irregular grid object.

Author(s)

J. Bano-Medina

See Also

downscaleTrain.keras for training a downscaling deep model with keras downscalePredict.keras for predicting with a keras model prepareNewData.keras for predictor preparation with new (test) data downscaleR.keras Wiki

Other downscaling.functions: downscaleTrain.keras(), relevanceMaps()

Examples

 
# Loading data
require(climate4R.datasets)
require(transformeR)
require(magrittr)
require(keras)
data("VALUE_Iberia_tas")
y <- VALUE_Iberia_tas
data("NCEP_Iberia_hus850", "NCEP_Iberia_psl", "NCEP_Iberia_ta850")
x <- makeMultiGrid(NCEP_Iberia_hus850, NCEP_Iberia_psl, NCEP_Iberia_ta850)
# We divide in train and test data and standardize the predictors 
# using transformeR functions subsetGrid and scaleGrid, respectively.
xT <- subsetGrid(x,years = 1983:1995)
xt <- subsetGrid(x,years = 1996:2002) %>% scaleGrid(base = xT, type = "standardize")
xT <- scaleGrid(xT,type = "standardize")
yT <- subsetGrid(y,years = 1983:1995)
yt <- subsetGrid(y,years = 1996:2002)
# Preparing the predictors
xy.T <- prepareData.keras(x = xT, y = yT, 
                          first.connection = "conv",
                          last.connection = "dense",
                          channels = "last")
# Defining the keras model.... 
# We define 3 hidden layers that consists on
# 2 convolutional steps followed by a dense connection.
input_shape  <- dim(xy.T$x.global)[-1]
output_shape  <- dim(xy.T$y$Data)[2]
inputs <- layer_input(shape = input_shape)
hidden <- inputs %>% 
  layer_conv_2d(filters = 25, kernel_size = c(3,3), activation = 'relu') %>%  
  layer_conv_2d(filters = 10, kernel_size = c(3,3), activation = 'relu') %>% 
  layer_flatten() %>% 
  layer_dense(units = 10, activation = "relu")
outputs <- layer_dense(hidden,units = output_shape)
model <- keras_model(inputs = inputs, outputs = outputs)
# We can print model in console to observe its configuration
model
# Training the deep learning model
model <- downscaleTrain.keras(xy.T,
                              model = model,
                              compile.args = list("loss" = "mse", 
                              "optimizer" = optimizer_adam(lr = 0.01)),
                              fit.args = list("epochs" = 30, "batch_size" = 100))
# Predicting on the test set...
xy.t <- prepareNewData.keras(newdata = xt,data.structure = xy.T)
pred <- downscalePredict.keras(newdata = xy.t,
                               model = model,
                               clear.session = TRUE,
                               C4R.template = yT)
# We can now apply the visualizeR functions to the prediction
# as it preserves the climate4R template.
require(visualizeR)
temporalPlot(yt,pred)

SantanderMetGroup/downscaleR.keras documentation built on July 7, 2023, 1:22 p.m.