Classification

Classification#

class neurio.tasks.classification.MNISTClassification[source]#

Bases: Task

preprocess()[source]#: Preprocess the data :return:

evaluate(y_train, y_pred)[source]#: Evaluate the model on the given data, using the metrics defined in the task. :param y: true labels (could be self.y_test, self.y_train, self.y_validation) :param y_pred: predicted labels :return:

get_data() → list | tuple | array#: Returns the data used for the task. :return: if validation_data is None, returns train_data, test_data. Otherwise, returns train_data, validation_data, test_data.

abstract get_metrics()#: Returns a dictionary containing the metrics used for the task. The keys should be the name of the metrics, and the values :return:

abstract get_metrics_info() → dict#: Returns a dictionary containing the metrics used for the task. The keys should be the name of the metrics, and the values should be a description of the metrics. :return: Dictionary containing the metrics used for the task.

abstract get_test_data()#: Returns the test data used for the task. :return: X and Y, where X is the input(s) data and Y is the output(s) data.

abstract get_train_data()#

Returns the train data used for the task.

Returns:: X and Y, where X is the input(s) data and Y is the output(s) data.

abstract get_validation_data()#: Returns the validation data used for the task. :return: X and Y, where X is the input(s) data and Y is the output(s) data.

class neurio.tasks.classification.CIFAR10Classification[source]#

Bases: Task

preprocess()[source]#

evaluate(y_train, y_pred)[source]#: Evaluate the model on the given data, using the metrics defined in the task. :param y: true labels (could be self.y_test, self.y_train, self.y_validation) :param y_pred: predicted labels :return:

get_data() → list | tuple | array#: Returns the data used for the task. :return: if validation_data is None, returns train_data, test_data. Otherwise, returns train_data, validation_data, test_data.

abstract get_metrics()#: Returns a dictionary containing the metrics used for the task. The keys should be the name of the metrics, and the values :return:

abstract get_metrics_info() → dict#: Returns a dictionary containing the metrics used for the task. The keys should be the name of the metrics, and the values should be a description of the metrics. :return: Dictionary containing the metrics used for the task.

abstract get_test_data()#: Returns the test data used for the task. :return: X and Y, where X is the input(s) data and Y is the output(s) data.

abstract get_train_data()#

Returns the train data used for the task.

Returns:: X and Y, where X is the input(s) data and Y is the output(s) data.

abstract get_validation_data()#: Returns the validation data used for the task. :return: X and Y, where X is the input(s) data and Y is the output(s) data.

class neurio.tasks.classification.ImageNetClassification[source]#

Bases: Task

preprocess()[source]#: Preprocess the data :return:

evaluate(y_train, y_pred)[source]#: Evaluate the model on the given data, using the metrics defined in the task. :param y: true labels (could be self.y_test, self.y_train, self.y_validation) :param y_pred: predicted labels :return:

get_data() → list | tuple | array#: Returns the data used for the task. :return: if validation_data is None, returns train_data, test_data. Otherwise, returns train_data, validation_data, test_data.

abstract get_metrics()#: Returns a dictionary containing the metrics used for the task. The keys should be the name of the metrics, and the values :return:

abstract get_metrics_info() → dict#: Returns a dictionary containing the metrics used for the task. The keys should be the name of the metrics, and the values should be a description of the metrics. :return: Dictionary containing the metrics used for the task.

abstract get_test_data()#: Returns the test data used for the task. :return: X and Y, where X is the input(s) data and Y is the output(s) data.

abstract get_train_data()#

Returns the train data used for the task.

Returns:: X and Y, where X is the input(s) data and Y is the output(s) data.

abstract get_validation_data()#: Returns the validation data used for the task. :return: X and Y, where X is the input(s) data and Y is the output(s) data.

class neurio.tasks.classification.NMNISTClassification[source]#

Bases: Task

prepare_data()[source]#

evaluate(y_train, y_pred)[source]#: Evaluate the model on the given data, using the metrics defined in the task. :param y: true labels (could be self.y_test, self.y_train, self.y_validation) :param y_pred: predicted labels :return:

get_data() → list | tuple | array#: Returns the data used for the task. :return: if validation_data is None, returns train_data, test_data. Otherwise, returns train_data, validation_data, test_data.

abstract get_metrics()#: Returns a dictionary containing the metrics used for the task. The keys should be the name of the metrics, and the values :return:

abstract get_metrics_info() → dict#: Returns a dictionary containing the metrics used for the task. The keys should be the name of the metrics, and the values should be a description of the metrics. :return: Dictionary containing the metrics used for the task.

abstract get_test_data()#: Returns the test data used for the task. :return: X and Y, where X is the input(s) data and Y is the output(s) data.

abstract get_train_data()#

Returns the train data used for the task.

Returns:: X and Y, where X is the input(s) data and Y is the output(s) data.

abstract get_validation_data()#: Returns the validation data used for the task. :return: X and Y, where X is the input(s) data and Y is the output(s) data.

class neurio.tasks.classification.SHDClassification[source]#

Bases: Task

prepare_data()[source]#

evaluate(y_train, y_pred)[source]#: Evaluate the model on the given data, using the metrics defined in the task. :param y: true labels (could be self.y_test, self.y_train, self.y_validation) :param y_pred: predicted labels :return:

get_data() → list | tuple | array#: Returns the data used for the task. :return: if validation_data is None, returns train_data, test_data. Otherwise, returns train_data, validation_data, test_data.

abstract get_metrics()#: Returns a dictionary containing the metrics used for the task. The keys should be the name of the metrics, and the values :return:

abstract get_metrics_info() → dict#: Returns a dictionary containing the metrics used for the task. The keys should be the name of the metrics, and the values should be a description of the metrics. :return: Dictionary containing the metrics used for the task.

abstract get_test_data()#: Returns the test data used for the task. :return: X and Y, where X is the input(s) data and Y is the output(s) data.

abstract get_train_data()#

Returns the train data used for the task.

Returns:: X and Y, where X is the input(s) data and Y is the output(s) data.

abstract get_validation_data()#: Returns the validation data used for the task. :return: X and Y, where X is the input(s) data and Y is the output(s) data.

Classification

Contents

Classification#