knitr::opts_chunk$set(eval = FALSE)
Keras Tuner is a hypertuning framework made for humans. It aims at making the life of AI practitioners, hypertuner algorithm creators and model designers as simple as possible by providing them with a clean and easy to use API for hypertuning. Keras Tuner makes moving from a base model to a hypertuned one quick and easy by only requiring you to change a few lines of code.
A hyperparameter tuner for Keras, specifically for tf$keras
with TensorFlow 2.0.
Full documentation and tutorials available on the Keras Tuner website.
Currently, the package is available on github:
devtools::install_github('EagerAI/kerastuneR')
Later, you need to install the python module kerastuner:
kerastuneR::install_kerastuner(python_path = 'paste python path')
Here's how to perform hyperparameter tuning for a single-layer dense neural network using random search.
First, we define a model-building function. It takes an argument hp
from which you can sample hyperparameters, such as hp$Int('units', min_value=32L, max_value=512L, step=32L)
(an integer from a certain range).
Sample data:
x_data <- matrix(data = runif(500,0,1),nrow = 50,ncol = 5) y_data <- ifelse(runif(50,0,1) > 0.6, 1L,0L) %>% as.matrix() x_data2 <- matrix(data = runif(500,0,1),nrow = 50,ncol = 5) y_data2 <- ifelse(runif(50,0,1) > 0.6, 1L,0L) %>% as.matrix()
This function returns a compiled model.
library(keras) library(kerastuneR) library(dplyr) build_model = function(hp) { model = keras_model_sequential() model %>% layer_dense(units=hp$Int('units', min_value=32, max_value=512, step=32), input_shape = ncol(x_data) activation='relu') %>% layer_dense(units=1, activation='sigmoid') %>% compile( optimizer= tf$keras$optimizers$Adam( hp$Choice('learning_rate', values=c(1e-2, 1e-3, 1e-4))), loss='binary_crossentropy', metrics='accuracy') return(model) }
Next, instantiate a tuner. You should specify the model-building function, the name of the objective to optimize (whether to minimize or maximize is automatically inferred for built-in metrics), the total number of trials (max_trials)
to test, and the number of models that should be built and fit for each trial (executions_per_trial)
.
Available tuners are RandomSearch
and Hyperband
.
Note: the purpose of having multiple executions per trial is to reduce results variance and therefore be able to more accurately assess the performance of a model. If you want to get results faster, you could set executions_per_trial=1 (single round of training for each model configuration).
tuner = RandomSearch( build_model, objective = 'val_accuracy', max_trials = 5, executions_per_trial = 3, directory = 'my_dir', project_name = 'helloworld')
You can print a summary of the search space:
tuner %>% search_summary()
Then, start the search for the best hyperparameter configuration. The call to search has the same signature as model %>% fit()
. But here instead of fit()
we call fit_tuner()
.
tuner %>% fit_tuner(x_data,y_data, epochs=5, validation_data = list(x_data2,y_data2))
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