AutoKeras Text Classifier Model

AutoKeras text classification class.
To `fit`, `evaluate` or `predict`, format inputs as:

• x : array. The input data should be array. The data should be one dimensional. Each element in the data should be a string which is a full sentence.

• y : array. It can be raw labels, one-hot encoded if more than two classes, or binary encoded for binary classification.

model_text_classifier(
  num_classes = NULL,
  multi_label = FALSE,
  loss = NULL,
  metrics = NULL,
  name = "text_classifier",
  max_trials = 100,
  directory = tempdir(),
  objective = "val_loss",
  overwrite = TRUE,
  seed = runif(1, 0, 1e+07)
)

Arguments

num_classes	: numeric. Defaults to `NULL`. If `NULL`, it will infer from the data.
multi_label	: logical. Defaults to `FALSE`.
loss	: A Keras loss function. Defaults to use "binary_crossentropy" or "categorical_crossentropy" based on the number of classes.
metrics	: A list of Keras metrics. Defaults to use "accuracy".
name	: character. The name of the AutoModel. Defaults to "text_classifier".
max_trials	: numeric. The maximum number of different Keras Models to try. The search may finish before reaching the `max_trials`. Defaults to `100`.
directory	: character. The path to a directory for storing the search outputs. Defaults to `tempdir()`, which would create a folder with the name of the AutoModel in the current directory.
objective	: character. Name of model metric to minimize or maximize, e.g. "val_accuracy". Defaults to "val_loss".
overwrite	: logical. Defaults to `TRUE`. If `FALSE`, reloads an existing project of the same name if one is found. Otherwise, overwrites the project.
seed	: numeric. Random seed. Defaults to `runif(1, 0, 10e6)`.

Value

A non-trained text classifier AutokerasModel.

Details

Important: The object returned by this function behaves like an R6 object, i.e., within function calls with this object as parameter, it is most likely that the object will be modified. Therefore it is not necessary to assign the result of the functions to the same object.

Examples

if (FALSE) {
library("keras")

# Get IMDb dataset
imdb <- dataset_imdb(num_words = 1000)
c(x_train, y_train) %<-% imdb$train
c(x_test, y_test) %<-% imdb$test

# AutoKeras procceses each text data point as a character vector,
# i.e., x_train[[1]] "<START> this film was just brilliant casting..",
# so we need to transform the dataset.
word_index <- dataset_imdb_word_index()
word_index <- c(
  "<PAD>", "<START>", "<UNK>", "<UNUSED>",
  names(word_index)[order(unlist(word_index))]
)
x_train <- lapply(x_train, function(x) {
  paste(word_index[x + 1], collapse = " ")
})
x_test <- lapply(x_test, function(x) {
  paste(word_index[x + 1], collapse = " ")
})

x_train <- matrix(unlist(x_train), ncol = 1)
x_test <- matrix(unlist(x_test), ncol = 1)
y_train <- array(unlist(y_train))
y_test <- array(unlist(y_test))

library("autokeras")

# Initialize the text classifier
clf <- model_text_classifier(max_trials = 10) %>% # It tries 10 different models
  fit(x_train, y_train) # Feed the text classifier with training data

# If you want to use own valitadion data do:
clf <- model_text_classifier(max_trials = 10) %>%
  fit(
    x_train,
    y_train,
    validation_data = list(x_test, y_test)
  )

# 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)
}