Task API

AutoKeras support the following task APIs.

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ImageClassifier

autokeras.task.ImageClassifier(num_classes=None, multi_label=False, loss=None, metrics=None, name='image_classifier', max_trials=100, directory=None, seed=None)

AutoKeras image classification class.

Arguments

  • num_classes: Int. Defaults to None. If None, it will infer from the data.
  • multi_label: Boolean. Defaults to False.
  • loss: A Keras loss function. Defaults to None. If None, the loss will be inferred from the AutoModel.
  • metrics: A list of Keras metrics. Defaults to None. If None, the metrics will be inferred from the AutoModel.
  • name: String. The name of the AutoModel. Defaults to 'image_classifier'.
  • max_trials: Int. The maximum number of different Keras Models to try. The search may finish before reaching the max_trials. Defaults to 100.
  • directory: String. The path to a directory for storing the search outputs. Defaults to None, which would create a folder with the name of the AutoModel in the current directory.
  • seed: Int. Random seed.

[source]

ImageRegressor

autokeras.task.ImageRegressor(output_dim=None, loss=None, metrics=None, name='image_regressor', max_trials=100, directory=None, seed=None)

AutoKeras image regression class.

Arguments

  • output_dim: Int. The number of output dimensions. Defaults to None. If None, it will infer from the data.
  • multi_label: Boolean. Defaults to False.
  • loss: A Keras loss function. Defaults to None. If None, the loss will be inferred from the AutoModel.
  • metrics: A list of Keras metrics. Defaults to None. If None, the metrics will be inferred from the AutoModel.
  • name: String. The name of the AutoModel. Defaults to 'image_regressor'.
  • max_trials: Int. The maximum number of different Keras Models to try. The search may finish before reaching the max_trials. Defaults to 100.
  • directory: String. The path to a directory for storing the search outputs. Defaults to None, which would create a folder with the name of the AutoModel in the current directory.
  • seed: Int. Random seed.

[source]

TextClassifier

autokeras.task.TextClassifier(num_classes=None, multi_label=False, loss=None, metrics=None, name='text_classifier', max_trials=100, directory=None, seed=None)

AutoKeras text classification class.

Arguments

  • num_classes: Int. Defaults to None. If None, it will infer from the data.
  • multi_label: Boolean. Defaults to False.
  • loss: A Keras loss function. Defaults to None. If None, the loss will be inferred from the AutoModel.
  • metrics: A list of Keras metrics. Defaults to None. If None, the metrics will be inferred from the AutoModel.
  • name: String. The name of the AutoModel. Defaults to 'text_classifier'.
  • max_trials: Int. The maximum number of different Keras Models to try. The search may finish before reaching the max_trials. Defaults to 100.
  • directory: String. The path to a directory for storing the search outputs. Defaults to None, which would create a folder with the name of the AutoModel in the current directory.
  • seed: Int. Random seed.

[source]

TextRegressor

autokeras.task.TextRegressor(output_dim=None, loss=None, metrics=None, name='text_regressor', max_trials=100, directory=None, seed=None)

AutoKeras text regression class.

Arguments

  • output_dim: Int. The number of output dimensions. Defaults to None. If None, it will infer from the data.
  • multi_label: Boolean. Defaults to False.
  • loss: A Keras loss function. Defaults to None. If None, the loss will be inferred from the AutoModel.
  • metrics: A list of Keras metrics. Defaults to None. If None, the metrics will be inferred from the AutoModel.
  • name: String. The name of the AutoModel. Defaults to 'text_regressor'.
  • max_trials: Int. The maximum number of different Keras Models to try. The search may finish before reaching the max_trials. Defaults to 100.
  • directory: String. The path to a directory for storing the search outputs. Defaults to None, which would create a folder with the name of the AutoModel in the current directory.
  • seed: Int. Random seed.

Coming Soon:

StructuredDataClassifier

StructuredDataRegressor

TimeSeriesForecaster