| import tensorflow as tf | |
| import tf_utils as tfu | |
| from tqdm import tqdm | |
| from graph.base import GraphPartBase | |
| class Lemm(GraphPartBase): | |
| def __init__(self, for_usage, global_settings, current_settings, optimiser, reset_optimiser): | |
| super().__init__(for_usage, global_settings, current_settings, optimiser, reset_optimiser, 'lemm', ["Loss", "AccuracyByChar", "Accuracy"]) | |
| self.chars_count = self.chars_count + 1 | |
| self.start_char_index = global_settings['start_token'] | |
| self.end_char_index = global_settings['end_token'] | |
| self.results = [] | |
| self.ys = [] | |
| self.y_seq_lens = [] | |
| self.cls = [] | |
| self.keep_drops = [] | |
| self.decoder_keep_drops = [] | |
| def __build_graph_for_device__(self, x, x_seq_len, batch_size, x_cls=None): | |
| self.xs.append(x) | |
| self.x_seq_lens.append(x_seq_len) | |
| if x_cls is None: | |
| x_cls = tf.placeholder(dtype=tf.int32, shape=(None,), name='XClass') | |
| self.cls.append(x_cls) | |
| if batch_size is None: | |
| batch_size = self.settings['batch_size'] | |
| y = tf.placeholder(dtype=tf.int32, shape=(None, None), name='Y') | |
| self.ys.append(y) | |
| y_seq_len = tf.placeholder(dtype=tf.int32, shape=(None,), name='YSeqLen') | |
| self.y_seq_lens.append(y_seq_len) | |
| tfu.seq2seq(self, | |
| batch_size, | |
| x, | |
| x_cls, | |
| x_seq_len, | |
| y, | |
| x_cls, | |
| y_seq_len) | |
| def __update_feed_dict__(self, op_name, feed_dict, batch, dev_num): | |
| feed_dict[self.cls[dev_num]] = batch['x_cls'] | |
| feed_dict[self.ys[dev_num]] = batch['y'] | |
| feed_dict[self.y_seq_lens[dev_num]] = batch['y_seq_len'] | |
| feed_dict[self.keep_drops[dev_num]] = self.settings['keep_drop'] | |
| feed_dict[self.decoder_keep_drops[dev_num]] = self.settings['decoder']['keep_drop'] | |
| def __load_dataset__(self, operation_name): | |
| items = list(tfu.load_lemma_dataset( | |
| self.dataset_path, | |
| self.devices_count, | |
| operation_name, | |
| self.settings['batch_size'] | |
| )) | |
| return items | |