def __init__(self, data_hparams, hparams=None):
ModelBase.__init__(self, hparams)
self._data_hparams = HParams(data_hparams,
PairedTextData.default_hparams())
self._src_vocab = None
self._tgt_vocab = None
self._src_embedder = None
self._tgt_embedder = None
self._connector = None
self._encoder = None
self._decoder = None
def default_hparams():
"""Returns a dictionary of hyperparameters with default values.
"""
hparams = ModelBase.default_hparams()
hparams.update({
"name": "seq2seq",
"source_embedder": "WordEmbedder",
"source_embedder_hparams": {},
"target_embedder": "WordEmbedder",
"target_embedder_hparams": {},
"embedder_share": True,
"embedder_hparams_share": True,
"encoder": "UnidirectionalRNNEncoder",
"encoder_hparams": {},
"decoder": "BasicRNNDecoder",
"decoder_hparams": {},
"decoding_strategy_train": "train_greedy",
"decoding_strategy_infer": "infer_greedy",
"beam_search_width": 0,
"connector": "MLPTransformConnector",
"connector_hparams": {},
"optimization": {}
})
return hparams
def default_hparams():
"""Returns a dictionary of hyperparameters with default values.
.. code-block:: python
{
"source_embedder": "WordEmbedder",
"source_embedder_hparams": {},
"target_embedder": "WordEmbedder",
"target_embedder_hparams": {},
"embedder_share": True,
"embedder_hparams_share": True,
"encoder": "UnidirectionalRNNEncoder",
"encoder_hparams": {},
"decoder": "BasicRNNDecoder",
"decoder_hparams": {},
"decoding_strategy_train": "train_greedy",
"decoding_strategy_infer": "infer_greedy",
"beam_search_width": 0,
"connector": "MLPTransformConnector",
"connector_hparams": {},
"optimization": {},
"name": "seq2seq",
}
Here:
"source_embedder" : str or class or instance
Word embedder for source text. Can be a class, its name or module
path, or a class instance.
"source_embedder_hparams" : dict
Hyperparameters for constructing the source embedder. E.g.,
See :meth:`~texar.modules.WordEmbedder.default_hparams` for
hyperparameters of :class:`~texar.modules.WordEmbedder`. Ignored
if "source_embedder" is an instance.
"target_embedder", "target_embedder_hparams" :
Same as "source_embedder" and "source_embedder_hparams" but for
target text embedder.
"embedder_share" : bool
Whether to share the source and target embedder. If `True`,
source embedder will be used to embed target text.
"embedder_hparams_share" : bool
Whether to share the embedder configurations. If `True`,
target embedder will be created with "source_embedder_hparams".
But the two embedders have different set of trainable variables.
"encoder", "encoder_hparams" :
Same as "source_embedder" and "source_embedder_hparams" but for
encoder.
"decoder", "decoder_hparams" :
Same as "source_embedder" and "source_embedder_hparams" but for
decoder.
"decoding_strategy_train" : str
The decoding strategy in training mode. See
:meth:`~texar.modules.RNNDecoderBase._build` for details.
"decoding_strategy_infer" : str
The decoding strategy in eval/inference mode.
"beam_search_width" : int
Beam width. If > 1, beam search is used in eval/inference mode.
"connector", "connector_hparams" :
The connector class and hyperparameters. A connector transforms
an encoder final state to a decoder initial state.
"optimization" : dict
Hyperparameters of optimizating the model. See
:func:`~texar.core.default_optimization_hparams` for details.
"name" : str
Name of the model.
"""
hparams = ModelBase.default_hparams()
hparams.update({
"name": "seq2seq",
"source_embedder": "WordEmbedder",
"source_embedder_hparams": {},
"target_embedder": "WordEmbedder",
"target_embedder_hparams": {},
"embedder_share": True,
"embedder_hparams_share": True,
"encoder": "UnidirectionalRNNEncoder",
"encoder_hparams": {},
"decoder": "BasicRNNDecoder",
"decoder_hparams": {},
"decoding_strategy_train": "train_greedy",
"decoding_strategy_infer": "infer_greedy",
"beam_search_width": 0,
"connector": "MLPTransformConnector",
"connector_hparams": {},
"optimization": {}
})
return hparams