/external/tensorflow/tensorflow/python/keras/preprocessing/ |
D | dataset_utils.py | 166 def get_training_or_validation_split(samples, labels, validation_split, subset): argument 180 if not validation_split: 183 num_val_samples = int(validation_split * len(samples)) 219 def check_validation_split_arg(validation_split, subset, shuffle, seed): argument 230 if validation_split and not 0 < validation_split < 1: 233 (validation_split,)) 234 if (validation_split or subset) and not (validation_split and subset): 240 if validation_split and shuffle and seed is None:
|
D | text_dataset.py | 37 validation_split=None, argument 138 validation_split, subset, shuffle, seed) 157 file_paths, labels, validation_split, subset)
|
D | text_dataset_test.py | 167 directory, batch_size=10, validation_split=0.2, subset='training', 173 directory, batch_size=10, validation_split=0.2, subset='validation', 237 directory, validation_split=2) 242 directory, validation_split=0.2, subset='other') 246 directory, validation_split=0, subset='training') 250 directory, validation_split=0.2, subset='training')
|
D | image_dataset.py | 44 validation_split=None, argument 188 validation_split, subset, shuffle, seed) 207 image_paths, labels, validation_split, subset)
|
D | image_dataset_test.py | 241 validation_split=0.2, subset='training', seed=1337) 247 validation_split=0.2, subset='validation', seed=1337) 336 directory, validation_split=2) 341 directory, validation_split=0.2, subset='other') 345 directory, validation_split=0, subset='training') 349 directory, validation_split=0.2, subset='training')
|
D | image_test.py | 144 preprocessing_image.ImageDataGenerator(validation_split=5) 286 self, validation_split): 324 validation_split=validation_split) 329 num_validation = int(count * validation_split)
|
D | image.py | 799 validation_split=0.0, argument 830 validation_split=validation_split,
|
/external/tensorflow/tensorflow/python/keras/engine/ |
D | training_generator_v1.py | 559 validation_split=0., argument 573 y, sample_weight, validation_split=validation_split) 649 validation_split=0., argument 662 validation_split) 729 validation_split=0., argument 750 validation_split=validation_split, 757 elif validation_split and 0. < validation_split < 1.: 761 x, y, sample_weights, validation_split))
|
D | training_distributed_v1.py | 585 validation_split=0., argument 606 validation_split=validation_split) 614 validation_split=validation_split, 624 validation_split=validation_split, 641 validation_split=validation_split, 644 elif validation_split:
|
D | training_arrays_v1.py | 606 validation_split=0., argument 628 validation_split=validation_split, 634 elif validation_split and 0. < validation_split < 1.: 637 x, y, sample_weights, validation_split)
|
D | training_utils_v1.py | 1235 def validate_dataset_input(x, y, sample_weight, validation_split=None): argument 1266 if validation_split is not None and validation_split != 0.0: 1270 'Received: x=%s, validation_split=%f' % (x, validation_split)) 1292 validation_split=None): argument 1302 if validation_split: 1838 def split_training_and_validation_data(x, y, sample_weights, validation_split): argument 1844 split_at = int(x[0].shape[0] * (1. - validation_split)) 1846 split_at = int(len(x[0]) * (1. - validation_split)) 1927 validation_split=0., argument
|
D | training_v1.py | 622 validation_split=0., argument 801 validation_split=validation_split, 2108 validation_split=0, argument 2210 validation_split) 2222 validation_split=0, argument 2288 validation_split) 2299 validation_split)
|
D | data_adapter_test.py | 1040 data_adapter.train_validation_split((x, y, sw), validation_split=0.2)) 1061 lambda: np.ones((10, 1)), validation_split=0.2) 1066 np.ones((1, 10)), validation_split=0.2) 1070 None, validation_split=0.2) 1075 (np.ones((10, 1)), None), validation_split=0.2)
|
D | data_adapter.py | 1466 def train_validation_split(arrays, validation_split): argument 1503 split_at = int(math.floor(batch_dim * (1. - validation_split))) 1511 batch_dim=batch_dim, validation_split=validation_split))
|
D | training.py | 889 validation_split=0., argument 1121 if validation_split: 1126 (x, y, sample_weight), validation_split=validation_split))
|
D | training_dataset_test.py | 124 validation_split=0.5,
|
D | training_test.py | 344 validation_split=0.2) 691 epochs=1, batch_size=2, validation_split=0.5) 1804 validation_split=0.1) 1890 validation_split=0.1)
|
/external/tensorflow/tensorflow/python/keras/distribute/ |
D | distributed_training_utils_v1.py | 433 validation_split=0.): argument 438 if validation_split and 0. < validation_split < 1.: 439 num_samples = int(num_samples * (1 - validation_split))
|
D | keras_utils_test.py | 257 validation_split=0.5,
|
/external/rnnoise/training/ |
D | rnn_train.py | 115 validation_split=0.1)
|
/external/tensorflow/tensorflow/lite/python/ |
D | util_test.py | 264 validation_split=0.1,
|
/external/tensorflow/tensorflow/python/keras/ |
D | callbacks_test.py | 440 model.fit(x, y, batch_size=10, epochs=2, validation_split=0.2)
|