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knitr::opts_chunk$set(echo = TRUE, eval = F)
Recently, the reticulate library provided with one of the most anticipating functionality --- ability to write a python class in R.
We could use a HyperModel subclass instead of a model-building function. So, this makes it easy to share and reuse hypermodels.
A HyperModel subclass only needs to implement a build(self, hp)
method. And, again one should return a compiled model inside a build
function.
MyHyperModel <- reticulate::PyClass( "HyperModel", inherit = kerastuneR::HyperModel_class(), list( `__init__` = function(self, num_classes) { self$num_classes = num_classes NULL }, build = function(self,hp) { model = keras_model_sequential() model %>% layer_dense(units = hp$Int('units', min_value=32L, max_value=512L, step=32L), activation='relu') %>% layer_dense(as.integer(self$num_classes), activation='softmax') %>% compile( optimizer= tf$keras$optimizers$Adam( hp$Choice('learning_rate', values=c(1e-2, 1e-3, 1e-4))), loss='categorical_crossentropy', metrics='accuracy') } ) )
# generate some data x_data <- matrix(data = runif(500,0,1),nrow = 50,ncol = 5) y_data <- ifelse(runif(50,0,1) > 0.6, 1L,0L) %>% as.matrix() x_data2 <- matrix(data = runif(500,0,1),nrow = 50,ncol = 5) y_data2 <- ifelse(runif(50,0,1) > 0.6, 1L,0L) %>% as.matrix() # subclass MyHyperModel <- reticulate::PyClass( "HyperModel", inherit = kerastuneR::HyperModel_class(), list( `__init__` = function(self, num_classes) { self$num_classes = num_classes NULL }, build = function(self,hp) { model = keras_model_sequential() model %>% layer_dense(units = hp$Int('units', min_value=32L, max_value=512L, step=32L), activation='relu') %>% layer_dense(as.integer(self$num_classes), activation='softmax') %>% compile( optimizer= tf$keras$optimizers$Adam( hp$Choice('learning_rate', values=c(1e-2, 1e-3, 1e-4))), loss='categorical_crossentropy', metrics='accuracy') } ) ) # Random Search hypermodel = MyHyperModel(num_classes = 10) tuner = RandomSearch( hypermodel, objective = 'val_accuracy', max_trials = 10, directory = 'my_dir', project_name = 'helloworld') # Run tuner %>% fit_tuner(x_data,y_data, epochs = 5, validation_data = list(x_data2, y_data2))
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