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
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
