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")
X_mean, X_std = util.load_cv_stats(base_path, cv_fold)
normalize_data = (X_mean, X_std)
nb_train_sample = X_train.shape[0]
nb_val_sample = X_val.shape[0]
nb_test_sample = X_test.shape[0]
print('X_train shape:', X_train.shape)
print(nb_train_sample, 'train samples')
if X_val is not None:
print(nb_val_sample, 'validation samples')
print(nb_test_sample, 'test samples')
# shuffle/permutation
if shuffle_data:
(X_train, y_train) = util.shuffle_data(X_train, y_train, seed=None)
(X_val, y_val) = util.shuffle_data(X_val, y_val, seed=None)
(X_test, y_test) = util.shuffle_data(X_test, y_test, seed=None)
##########################
# MODEL BUILDING
##########################
print("Building model...")
if batch_normalization:
weight_reg = 5e-4 # weight regularization value for l2
dropout = False
dropout_fc_layer = False
lr = 0.005
lr_decay = 5e-4
X_mean, X_std = util.load_cv_stats(base_path, cv_fold)
normalize_data = (X_mean, X_std)
nb_train_sample = X_train.shape[0]
nb_val_sample = X_val.shape[0]
nb_test_sample = X_test.shape[0]
print('X_train shape:', X_train.shape)
print(nb_train_sample, 'train samples')
if X_val is not None:
print(nb_val_sample, 'validation samples')
print(nb_test_sample, 'test samples')
# shuffle/permutation
if shuffle_data:
(X_train, y_train) = util.shuffle_data(X_train, y_train, seed=None)
(X_val, y_val) = util.shuffle_data(X_val, y_val, seed=None)
(X_test, y_test) = util.shuffle_data(X_test, y_test, seed=None)
##########################
# MODEL BUILDING
##########################
print("Building model...")
if batch_normalization:
weight_reg = 5e-4 # weight regularization value for l2
dropout = False
dropout_fc_layer = False
lr = 0.005
lr_decay = 5e-4
