def test_build_graph(self, dataset_name, regression):
"""Test whether build_graph works as expected."""
data_x, data_y, _ = data_utils.load_dataset(dataset_name)
data_gen = data_utils.split_training_dataset(
data_x, data_y, n_splits=5, stratified=not regression)
(x_train, y_train), (x_validation, y_validation) = next(data_gen)
sess = tf.InteractiveSession()
graph_tensors_and_ops, metric_scores = graph_builder.build_graph(
x_train=x_train,
y_train=y_train,
x_test=x_validation,
y_test=y_validation,
activation='exu',
learning_rate=1e-3,
batch_size=256,
shallow=True,
regression=regression,
output_regularization=0.1,
dropout=0.1,
decay_rate=0.999,
name_scope='model',
l2_regularization=0.1)
# Run initializer ops
sess.run(tf.global_variables_initializer())
sess.run([
graph_tensors_and_ops['iterator_initializer'],
graph_tensors_and_ops['running_vars_initializer']
])
for _ in range(2):
sess.run(graph_tensors_and_ops['train_op'])
self.assertIsInstance(metric_scores['train'](sess), float)
sess.close()
def create_test_train_fold(fold_num):
"""Splits the dataset into training and held-out test set."""
data_x, data_y, _ = data_utils.load_dataset(FLAGS.dataset_name)
tf.logging.info('Dataset: %s, Size: %d', FLAGS.dataset_name, data_x.shape[0])
tf.logging.info('Cross-val fold: %d/%d', FLAGS.fold_num, _N_FOLDS)
# Get the training and test set based on the StratifiedKFold split
(x_train_all, y_train_all), test_dataset = data_utils.get_train_test_fold(data_x,
data_y,
fold_num=fold_num,
num_folds=_N_FOLDS,
stratified=not FLAGS.regression)
data_gen = data_utils.split_training_dataset(x_train_all,
y_train_all,
FLAGS.num_splits,
stratified=not FLAGS.regression)
return data_gen, test_dataset
def test_data(self, dataset_name, dataset_size):
"""Test whether a dataset is loaded properly with specified size."""
x, y, _ = data_utils.load_dataset(dataset_name)
self.assertIsInstance(x, np.ndarray)
self.assertIsInstance(y, np.ndarray)
self.assertLen(x, dataset_size)