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| def load_process(self, shuffle_size = 1000): | |
| self.loaded_dataset = self.dataset.map(self._load_labeled_data, num_parallel_calls=tf.data.experimental.AUTOTUNE) | |
| self.loaded_dataset = self.loaded_dataset.cache() | |
| # Shuffle data and create batches | |
| self.loaded_dataset = self.loaded_dataset.shuffle(buffer_size=shuffle_size) | |
| self.loaded_dataset = self.loaded_dataset.repeat() | |
| self.loaded_dataset = self.loaded_dataset.batch(self.batch_size) | |
| # Make dataset fetch batches in the background during the training of the model. | |
| self.loaded_dataset = self.loaded_dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE) |
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