def extract_tpu_data(self, tf_record, random_rotation=False): dataset = preprocessing.get_tpu_input_tensors( 1, 'nhwc', [tf_record], num_repeats=1, shuffle_records=False, shuffle_examples=False, filter_amount=1, random_rotation=random_rotation) pos_tensor, label_tensors = dataset.make_one_shot_iterator().get_next() return self.get_data_tensors(pos_tensor, label_tensors)
def _input_fn(params): return preprocessing.get_tpu_input_tensors( params['batch_size'], tf_records, filter_amount=FLAGS.filter_amount, shuffle_examples=FLAGS.shuffle_examples, shuffle_buffer_size=FLAGS.shuffle_buffer_size, random_rotation=True)
def input_fn(params): return preprocessing.get_tpu_input_tensors(params['batch_size'], tf_records, filter_amount=0.05)
def input_fn(params): return preprocessing.get_tpu_input_tensors(params['batch_size'], tf_records, random_rotation=True)
def input_fn(params): return preprocessing.get_tpu_input_tensors(params['batch_size'], tf_records)
def _input_fn(params): return preprocessing.get_tpu_input_tensors( params['batch_size'], tf_records, filter_amount=0.05, shuffle_examples=False)
def _input_fn(params): return preprocessing.get_tpu_input_tensors( params['train_batch_size'], params['input_layout'], tf_records, filter_amount=1.0)