3d95882c14d31f3d1e358c39dcf34dfecdcc75a3,tgen/tfclassif.py,RerankingClassifier,__init__,#RerankingClassifier#Any#,94

Before Change

        // Train Summaries

        self.loss_summary_reranker = None

        self.train_summary_op = None
        self.train_summary_dir = None
        self.train_summary_writer = None


        timestamp = str(int(time.time()))
        self.out_dir = os.path.abspath(os.path.join(os.path.curdir, "seq_runs", timestamp))


    def save_to_file(self, model_fname):
        Save the classifier  to a file (actually two files, one for configuration and one

After Change

    A classifier for trees that decides which DAIs are currently represented
    (to be used in limiting candidate generator and/or re-scoring the trees).

    def __init__(self, cfg):

        super(RerankingClassifier, self).__init__(scope_name="rerank-" +
                                                  cfg.get("scope_suffix", ""))
        self.cfg = cfg
        self.language = cfg.get("language", "en")
        self.selector = cfg.get("selector", "")
        self.tree_embs = cfg.get("nn", "").startswith("emb")
        if self.tree_embs:
            self.tree_embs = TreeEmbeddingClassifExtract(cfg)
            self.emb_size = cfg.get("emb_size", 50)
        self.mode = cfg.get("mode", "tokens" if cfg.get("use_tokens") else "trees")

        self.nn_shape = cfg.get("nn_shape", "ff")
        self.num_hidden_units = cfg.get("num_hidden_units", 512)

        self.passes = cfg.get("passes", 200)
        self.min_passes = cfg.get("min_passes", 0)
        self.alpha = cfg.get("alpha", 0.1)
        self.randomize = cfg.get("randomize", True)
        self.batch_size = cfg.get("batch_size", 1)

        self.validation_freq = cfg.get("validation_freq", 10)
        self.max_cores = cfg.get("max_cores")
        self.cur_da = None
        self.cur_da_bin = None
        self.checkpoint_path = None

        self.delex_slots = cfg.get("delex_slots", None)
        if self.delex_slots:
            self.delex_slots = set(self.delex_slots.split(","))

        // Train Summaries
        self.train_summary_dir = cfg.get("tb_summary_dir", None)
        if self.train_summary_dir:
            self.loss_summary_reranker = None
            self.train_summary_op = None
            self.train_summary_dir = None
            self.train_summary_writer = None

Instances