evaluate {autokeras} | R Documentation |
Evaluate a Model
Description
Evaluate the best model for the given data.
Usage
## S3 method for class 'AutokerasModel'
evaluate(object, x_test, y_test = NULL, batch_size = 32, ...)
Arguments
object |
: A trained AutokerasModel instance. |
x_test |
: Any allowed types according to the input node. Testing data. Check corresponding AutokerasModel help to note how it should be provided. |
y_test |
: Any allowed types according to the input node. Testing data. Check corresponding AutokerasModel help to note how it should be provided. Defaults to 'NULL'. |
batch_size |
: numeric. Defaults to '32'. |
... |
: Unused. |
Value
numeric test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute model$metrics_names will give you the display labels for the scalar outputs.
Examples
## Not run:
library("keras")
# use the MNIST dataset as an example
mnist <- dataset_mnist()
c(x_train, y_train) %<-% mnist$train
c(x_test, y_test) %<-% mnist$test
library("autokeras")
# Initialize the image classifier
clf <- model_image_classifier(max_trials = 10) %>% # It tries 10 different models
fit(x_train, y_train) # Feed the image classifier with training data
# Predict with the best model
(predicted_y <- clf %>% predict(x_test))
# Evaluate the best model with testing data
clf %>% evaluate(x_test, y_test)
# Get the best trained Keras model, to work with the keras R library
export_model(clf)
## End(Not run)
[Package autokeras version 1.0.12 Index]