/external/tensorflow/tensorflow/python/keras/preprocessing/ |
D | dataset_utils.py | 169 def get_training_or_validation_split(samples, labels, validation_split, subset): argument 183 if not validation_split: 186 num_val_samples = int(validation_split * len(samples)) 221 def check_validation_split_arg(validation_split, subset, shuffle, seed): argument 232 if validation_split and not 0 < validation_split < 1: 235 (validation_split,)) 236 if (validation_split or subset) and not (validation_split and subset): 242 if validation_split and shuffle and seed is None:
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D | text_dataset.py | 38 validation_split=None, argument 139 validation_split, subset, shuffle, seed) 158 file_paths, labels, validation_split, subset)
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D | text_dataset_test.py | 171 directory, batch_size=10, validation_split=0.2, subset='training', 177 directory, batch_size=10, validation_split=0.2, subset='validation', 241 directory, validation_split=2) 246 directory, validation_split=0.2, subset='other') 250 directory, validation_split=0, subset='training') 254 directory, validation_split=0.2, subset='training')
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D | image_dataset.py | 45 validation_split=None, argument 182 validation_split, subset, shuffle, seed) 201 image_paths, labels, validation_split, subset)
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D | image_dataset_test.py | 245 validation_split=0.2, subset='training', seed=1337) 251 validation_split=0.2, subset='validation', seed=1337) 340 directory, validation_split=2) 345 directory, validation_split=0.2, subset='other') 349 directory, validation_split=0, subset='training') 353 directory, validation_split=0.2, subset='training')
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D | image_test.py | 141 preprocessing_image.ImageDataGenerator(validation_split=5) 283 self, validation_split): 321 validation_split=validation_split) 326 num_validation = int(count * validation_split)
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D | image.py | 783 validation_split=0.0, argument 814 validation_split=validation_split,
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/external/tensorflow/tensorflow/python/keras/engine/ |
D | training_generator_v1.py | 562 validation_split=0., argument 576 y, sample_weight, validation_split=validation_split) 652 validation_split=0., argument 665 validation_split) 732 validation_split=0., argument 753 validation_split=validation_split, 760 elif validation_split and 0. < validation_split < 1.: 764 x, y, sample_weights, validation_split))
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D | training_distributed_v1.py | 590 validation_split=0., argument 611 validation_split=validation_split) 619 validation_split=validation_split, 629 validation_split=validation_split, 646 validation_split=validation_split, 649 elif validation_split:
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D | training_arrays_v1.py | 609 validation_split=0., argument 631 validation_split=validation_split, 637 elif validation_split and 0. < validation_split < 1.: 640 x, y, sample_weights, validation_split)
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D | training_utils_v1.py | 1232 def validate_dataset_input(x, y, sample_weight, validation_split=None): argument 1263 if validation_split is not None and validation_split != 0.0: 1267 'Received: x=%s, validation_split=%f' % (x, validation_split)) 1289 validation_split=None): argument 1299 if validation_split: 1835 def split_training_and_validation_data(x, y, sample_weights, validation_split): argument 1841 split_at = int(x[0].shape[0] * (1. - validation_split)) 1843 split_at = int(len(x[0]) * (1. - validation_split)) 1924 validation_split=0., argument
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D | training_v1.py | 624 validation_split=0., argument 804 validation_split=validation_split, 2100 validation_split=0, argument 2202 validation_split) 2214 validation_split=0, argument 2280 validation_split) 2291 validation_split)
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D | data_adapter_test.py | 1044 data_adapter.train_validation_split((x, y, sw), validation_split=0.2)) 1065 lambda: np.ones((10, 1)), validation_split=0.2) 1070 np.ones((1, 10)), validation_split=0.2) 1074 None, validation_split=0.2) 1079 (np.ones((10, 1)), None), validation_split=0.2)
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D | data_adapter.py | 1449 def train_validation_split(arrays, validation_split): argument 1488 split_at = int(math.floor(batch_dim * (1. - validation_split))) 1496 batch_dim=batch_dim, validation_split=validation_split))
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D | training.py | 876 validation_split=0., argument 1084 if validation_split: 1089 (x, y, sample_weight), validation_split=validation_split))
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/external/tensorflow/tensorflow/tools/api/golden/v2/ |
D | tensorflow.keras.preprocessing.pbtxt | 17 …or_mode\', \'batch_size\', \'image_size\', \'shuffle\', \'seed\', \'validation_split\', \'subset\'… 21 …s_names\', \'batch_size\', \'max_length\', \'shuffle\', \'seed\', \'validation_split\', \'subset\'…
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D | tensorflow.keras.preprocessing.image.-image-data-generator.pbtxt | 7 …flip\', \'rescale\', \'preprocessing_function\', \'data_format\', \'validation_split\', \'dtype\']…
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D | tensorflow.keras.experimental.-wide-deep-model.pbtxt | 218 …', \'y\', \'batch_size\', \'epochs\', \'verbose\', \'callbacks\', \'validation_split\', \'validati…
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D | tensorflow.keras.models.-model.pbtxt | 217 …', \'y\', \'batch_size\', \'epochs\', \'verbose\', \'callbacks\', \'validation_split\', \'validati…
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D | tensorflow.keras.-sequential.pbtxt | 223 …', \'y\', \'batch_size\', \'epochs\', \'verbose\', \'callbacks\', \'validation_split\', \'validati…
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/external/tensorflow/tensorflow/python/keras/distribute/ |
D | distributed_training_utils_v1.py | 443 validation_split=0.): argument 448 if validation_split and 0. < validation_split < 1.: 449 num_samples = int(num_samples * (1 - validation_split))
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/external/tensorflow/tensorflow/tools/api/golden/v1/ |
D | tensorflow.keras.preprocessing.image.-image-data-generator.pbtxt | 7 …flip\', \'rescale\', \'preprocessing_function\', \'data_format\', \'validation_split\', \'dtype\']…
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D | tensorflow.keras.-sequential.pbtxt | 223 …', \'y\', \'batch_size\', \'epochs\', \'verbose\', \'callbacks\', \'validation_split\', \'validati…
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/external/rnnoise/training/ |
D | rnn_train.py | 115 validation_split=0.1)
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/external/tensorflow/tensorflow/lite/python/ |
D | util_test.py | 293 validation_split=0.1,
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