def constrcut_predictor(path): # warm_start_from=tf.estimator.WarmStartSettings( # ckpt_to_initialize_from=str(path), # vars_to_warm_start=".*" # everything in TRAINABLE_VARIABLES - excluding optimiser params # # vars_to_warm_start=[".*"], # everything in GLOBAL_VARIABLES - including optimiser params # ) model = LISAModel(hparams, model_config, layer_task_config, layer_attention_config, feature_idx_map, label_idx_map, vocab) # ws = WarmStartSettings(ckpt_to_initialize_from=path, # vars_to_warm_start=".*") # print("debug <loading from>:", path) estimator = tf.estimator.Estimator(model_fn=model.model_fn, model_dir=path) return estimator
# ('feature' in data_config[d] and data_config[d]['feature']) or # ('label' in data_config[d] and data_config[d]['label'])]): # if 'feature' in data_config[f] and data_config[f]['feature']: # feature_idx_map[f] = i # if 'label' in data_config[f] and data_config[f]['label']: # if 'type' in data_config[f] and data_config[f]['type'] == 'range': # idx = data_config[f]['conll_idx'] # j = i + idx[1] if idx[1] != -1 else -1 # label_idx_map[f] = (i, j) # else: # label_idx_map[f] = (i, i+1) feature_idx_map, label_idx_map = util.load_feat_label_idx_maps(data_config) # Initialize the model model = LISAModel(hparams, model_config, layer_task_config, layer_attention_config, feature_idx_map, label_idx_map, vocab) tf.logging.log( tf.logging.INFO, "Created model with %d trainable parameters" % tf_utils.get_num_trainable_parameters()) # Set up the Estimator estimator = tf.estimator.Estimator(model_fn=model.model_fn, model_dir=args.save_dir) def dev_input_fn(): return train_utils.get_input_fn(vocab, data_config, dev_filenames, hparams.batch_size,