src.model.deeplearn.conv_autoenc_pwise_classif_model

Classes

ConvAutoencPwiseClassifModel(**kwargs)

class src.model.deeplearn.conv_autoenc_pwise_classif_model.ConvAutoencPwiseClassifModel(**kwargs)

Author:

Alberto M. Esmoris Pena

Convolutional autoencoder model for classification tasks. See ClassificationModel.

Variables:

model (DLModelHandler) – The deep learning model wrapped by the corresponding handler, i.e., the ConvAutoencPwiseClassif model wrapped by a SimpleDLModelHandler handler.

static extract_model_args(spec)

Extract the arguments to initialize/instantiate a ConvAutoencPwiseClassifModel from a key-word specification.

Parameters:

spec – The key-word specification containing the arguments.

Returns:

The arguments to initialize/instantiate a ConvAutoencPwiseClassifModel.

__init__(**kwargs)

Initialize an instance of ConvAutoencPwiseClassifModel.

Parameters:

kwargs – The attributes for the ConvAutoencPwiseClassifModel that will also be passed to the parent.

is_deep_learning_model()

See Model and model.Model.is_deep_learning_model().

prepare_model()

Prepare a convolutional autoencoder point-wise classifier with current model arguments.

Returns:

The prepared model itself. Note it is also assigned as the model attribute of the object/instance.

Return type:

ConvAutoencPwiseClassif

overwrite_pretrained_model(spec)

See model.Model.overwrite_pretrained_model().

update_paths()

Consider the current specification of model args (self.model_args) to update the paths.

predict(pcloud, X=None, F=None)

Use the model to compute predictions on the input point cloud.

The behavior of the base implementation (see model.Model.predict()) is extended to account for X and F matrix as different entities.

Parameters:

• X (np.ndarray) – The input matrix of coordinates where each row represents a point from the point cloud (OPTIONAL). If not given, it will be retrieved from the point cloud.

• F (np.ndarray) – The input matrix of features (OPTIONAL). If not given, it will be retrieved from the point cloud if there are feature names (fnames) available.

get_input_from_pcloud(pcloud)

See model.Model.get_input_from_pcloud().

training(X, y, F=None, info=True)

The fundamental training logic to train a convolutional autoencoder point-wise classifier.

See ClassificationModel and Model. Also see model.Model.training().

Parameters:

F (np.ndarray) – An optional (can be None) matrix of input features.

on_training_finished(X, y, yhat=None)

See model.Model.on_training_finished().

compute_pwise_activations(X, reducer=None)

Compute the point-wise activations of the last layer before the output softmax (or sigmoid for binary classification) layer in the convolutional autoencoder point-wise classification model.

Parameters:

• X (np.ndarray or list) – The matrix of coordinates representing the point cloud. Alternatively, it can be a list such that X[0] is the matrix of coordinates and X[1] the matrix of features.

• reducer (PredictionReducer or None) – The prediction reducer to reduce the point-wise activations (it should be the same used for typical predictions).

Returns:

The matrix of point-wise activations where points are rows and the columns are the components of the output activation function (activated vector or point-wise features).

Return type:

np.ndarray