Semantic training scf folder

operators.ml3d.semantic_training_scf_folder(client, data_in_folder='/data_in_folder', out_model_parameters_path='trained_model/model_1', class_names='1,2,3,4,5,6,7,8', feature_names='red,green,blue', point_names='x,y,z', label_name='classification', max_epochs=500, learning_rate=0.01, learning_rate_decay=0.1, feature_dimensions='12,48,96,192,384', batch_size=2, worker_instance_type='x2large', manager_instance_type='small', extension_data_in_path='.laz', skip_existing_files=False)

semantic_training_scf_folder(client,

data_in_folder=’/data_in_folder’,

out_model_parameters_path=’trained_model/model_1’,

class_names=’1,2,3,4,5,6,7,8’,

feature_names=’red,green,blue’,

point_names=’x,y,z’,

label_name=’classification’,

max_epochs=500,

learning_rate=0.01,

learning_rate_decay=0.1,

feature_dimensions=’12,48,96,192,384’,

batch_size=2,

worker_instance_type=’x2large’,

manager_instance_type=”small”,

extension_data_in_folder=”./data/files/”,

skip_existing_files = False )

Parameters:

• out_model_parameters_path – path to model

• class_names – comma separated list of class names. Class 0 is always given and is used to denote unlabeled points.

• feature_names – comma separated list of features that are provided

• point_names – comma separated list of point identifiers in (las/laz)

• label_name – label name for (las/laz)

• max_epochs – maximum number of epochs

• learning_rate – learning rate

• learning_rate_decay – learning rate decay

• feature_dimensions – feature dimensions

• batch_size – batch_size

• data_in_folder – folder to folder that contains the training data

• worker_instance_type – cloud instance type of worker nodes

• manager_instance_type – cloud instance type of manager node

• extension_data_in_folder – File extension of files in folder for data_in_folder

• skip_existing_files – skip files that already exist in the output folder