Python load_paths_from_files 예제들

예제 #1

image_dimensions = 3

##########################
# DATA LOADING
##########################

print("Loading paths...")

# download & untar or get local path
base_path = caltech101.download(dataset='img-gen-resized')

# path to image folder
base_path = os.path.join(base_path, caltech101.config.tar_inner_dirname)

# X_test contain only paths to images
(X_test, y_test) = util.load_paths_from_files(base_path, 'X_test.txt',
                                              'y_test.txt')

for cv_fold in [
        0
]:  # on which cross val folds to run; cant loop over several folds due to some bug
    print("fold {}".format(cv_fold))

    experiment_name = '_bn_triangular_cv{}_e{}'.format(cv_fold, nb_epoch)

    # load cross val split
    (X_train, y_train), (X_val,
                         y_val) = util.load_cv_split_paths(base_path, cv_fold)

    # compute class weights, since classes are highly imbalanced
    class_weight = compute_class_weight('auto', range(nb_classes), y_train)

예제 #2

파일: train_test_splitting.py 프로젝트: bentanust/ini_caltech101

def load_train_test_split_paths(base_path):
    return load_paths_from_files(base_path, 'X_train.txt', 'y_train.txt'), \
           load_paths_from_files(base_path, 'X_test.txt', 'y_test.txt')

예제 #3

파일: caltech101_cnn_training.py 프로젝트: bentanust/ini_caltech101

image_dimensions = 3

##########################
# DATA LOADING
##########################

print("Loading paths...")

# download & untar or get local path
base_path = caltech101.download(dataset='img-gen-resized')

# path to image folder
base_path = os.path.join(base_path, caltech101.config.tar_inner_dirname)

# X_test contain only paths to images
(X_test, y_test) = util.load_paths_from_files(base_path, 'X_test.txt', 'y_test.txt')

for cv_fold in [0]: # on which cross val folds to run; cant loop over several folds due to some bug
    print("fold {}".format(cv_fold))

    experiment_name = '_bn_triangular_cv{}_e{}'.format(cv_fold, nb_epoch)

    # load cross val split
    (X_train, y_train), (X_val, y_val) = util.load_cv_split_paths(base_path, cv_fold)

    # compute class weights, since classes are highly imbalanced
    class_weight = compute_class_weight('auto', range(nb_classes), y_train)

    if normalize_data:
        print("Load mean and std...")
        X_mean, X_std = util.load_cv_stats(base_path, cv_fold)

예제 #4

import os

from ini_caltech101 import util

path = os.path.abspath(os.path.join('datasets', 'img-gen-resized', '101_ObjectCategories'))
stratify = True
seed = 42

# X_train contain only paths to images
(X_train, y_train) = util.load_paths_from_files(path, 'X_train.txt', 'y_train.txt', full_path=False)

(X_train, y_train) = util.shuffle_data(X_train, y_train, seed=seed)

nb_folds = 10

for cv_fold, ((X_cv_train, y_cv_train), (X_cv_test, y_cv_test)) in \
        enumerate(util.make_cv_split(X_train, y_train, nb_folds=nb_folds, stratify=stratify, seed=seed)):

    split_config = {'fold': cv_fold,
                    'nb_folds': nb_folds,
                    'stratify': stratify,
                    'seed': seed,
                    'train_samples': len(X_cv_train),
                    'test_samples': len(X_cv_test)}

    print("Save split for fold {}".format(cv_fold))
    util.save_cv_split_paths(path, X_cv_train, y_cv_train, X_cv_test, y_cv_test, cv_fold, split_config)

    print("Calculating mean and std...")
    X_mean, X_std = util.calc_stats(X_cv_train, base_path=path)
    print("Save stats")

예제 #5

def load_train_test_split_paths(base_path):
    return load_paths_from_files(base_path, 'X_train.txt', 'y_train.txt'), \
           load_paths_from_files(base_path, 'X_test.txt', 'y_test.txt')