Python get_n_classes_from_file_multilabelの例

コード例 #1

ファイル: train.py プロジェクト: LibraryOfCongress/Exploring-ML-with-Project-Aida

def default_config():
    train_data = None  # Directory with training data
    eval_data = None  # Directory with validation data
    model_output_dir = None  # Directory to output tf model
    restore_model = False  # Set to true to continue training
    classes_file = None  # txt file with classes values (unused for REGRESSION)
    gpu = ''  # GPU to be used for training
    prediction_type = utils.PredictionType.CLASSIFICATION  # One of CLASSIFICATION, REGRESSION or MULTILABEL
    pretrained_model_name = 'resnet50'
    model_params = utils.ModelParams(pretrained_model_name=pretrained_model_name).to_dict()  # Model parameters
    training_params = utils.TrainingParams().to_dict()  # Training parameters
    if prediction_type == utils.PredictionType.CLASSIFICATION:
        assert classes_file is not None
        model_params['n_classes'] = utils.get_n_classes_from_file(classes_file)
    elif prediction_type == utils.PredictionType.REGRESSION:
        model_params['n_classes'] = 1
    elif prediction_type == utils.PredictionType.MULTILABEL:
        assert classes_file is not None
        model_params['n_classes'] = utils.get_n_classes_from_file_multilabel(classes_file)

コード例 #2

def default_config():
    #train_data = "Saumweg_training_combined.csv"  # Directory with training data
    train_data = "/scratch/users/ycan/dhSegment/zistovi/train/"
    eval_data = "/scratch/users/ycan/dhSegment/zistovi/train/"  # Directory with validation data
    model_output_dir = "model_zistovi_unet_all_100/"  # Directory to output tf model
    restore_model = False  # Set to true to continue training
    classes_file = "classes.txt" # txt file with classes values (unused for REGRESSION)
    gpu = '0'  # GPU to be used for training
    prediction_type = utils.PredictionType.CLASSIFICATION  # One of CLASSIFICATION, REGRESSION or MULTILABEL
    pretrained_model_name = 'unet'
    model_params = utils.ModelParams(pretrained_model_name=pretrained_model_name).to_dict()  # Model parameters
    training_params = utils.TrainingParams().to_dict()  # Training parameters
    if prediction_type == utils.PredictionType.CLASSIFICATION:
        assert classes_file is not None
        model_params['n_classes'] = utils.get_n_classes_from_file(classes_file)
    elif prediction_type == utils.PredictionType.REGRESSION:
        model_params['n_classes'] = 1
    elif prediction_type == utils.PredictionType.MULTILABEL:
        assert classes_file is not None
        model_params['n_classes'] = utils.get_n_classes_from_file_multilabel(classes_file)

コード例 #3

import os