def default_params(): params = ModelBase.default_params() params.update({ "source.max_seq_len": 50, "source.reverse": True, "target.max_seq_len": 50, "embedding.dim": 100, "embedding.share": False, "inference.beam_search.beam_width": 0, "inference.beam_search.length_penalty_weight": 0.0, "inference.beam_search.choose_successors_fn": "choose_top_k", "vocab_source": "", "vocab_target": "", }) return params
def default_params(): params = ModelBase.default_params() params.update({ "source.max_seq_len": 50, "source.reverse": True, "target.max_seq_len": 50, "embedding.dim": 100, "embedding.init_scale": 0.04, "embedding.share": False, "inference.beam_search.beam_width": 0, "inference.beam_search.length_penalty_weight": 0.0, "inference.beam_search.choose_successors_fn": "choose_top_k", "optimizer.clip_embed_gradients": 0.1, "vocab_source": "", "vocab_target": "", }) return params
def default_params(): params = ModelBase.default_params() params.update({ "source.max_seq_len": 50, "source.reverse": True, "target.max_seq_len": 50, "embedding.dim": 100, "embedding.init_scale": 0.04, "embedding.share": False, "embedding.source_embedding": None, "embedding.target_embedding": None, "inference.beam_search.beam_width": 0, "inference.beam_search.length_penalty_weight": 0.0, "inference.beam_search.choose_successors_fn": "choose_top_k", "optimizer.clip_embed_gradients": 0.1, "vocab_source": "", "vocab_target": "", }) return params
def default_params(): params = ModelBase.default_params() params.update({ "attention.class": "AttentionLayerBahdanau", "attention.params": { "num_units": 128 }, "bridge.class": "seq2seq.models.bridges.ZeroBridge", "bridge.params": {}, "encoder.class": "seq2seq.encoders.InceptionV3Encoder", "encoder.params": {}, # Arbitrary parameters for the encoder "decoder.class": "seq2seq.decoders.AttentionDecoder", "decoder.params": {}, # Arbitrary parameters for the decoder "target.max_seq_len": 50, "embedding.dim": 100, "inference.beam_search.beam_width": 0, "inference.beam_search.length_penalty_weight": 0.0, "inference.beam_search.choose_successors_fn": "choose_top_k", "vocab_target": "", }) return params