def __init__( self, memory, memory_sequence_length=None, cell=None, cell_dropout_mode=None, vocab_size=None, output_layer=None, #attention_layer=None, # TODO(zhiting): only valid for tf>=1.0 cell_input_fn=None, hparams=None): RNNDecoderBase.__init__(self, cell, vocab_size, output_layer, cell_dropout_mode, hparams) attn_hparams = self._hparams['attention'] attn_kwargs = attn_hparams['kwargs'].todict() # Parse the 'probability_fn' argument if 'probability_fn' in attn_kwargs: prob_fn = attn_kwargs['probability_fn'] if prob_fn is not None and not callable(prob_fn): prob_fn = utils.get_function(prob_fn, [ 'tensorflow.nn', 'tensorflow.contrib.sparsemax', 'tensorflow.contrib.seq2seq' ]) attn_kwargs['probability_fn'] = prob_fn attn_kwargs.update({ "memory_sequence_length": memory_sequence_length, "memory": memory }) self._attn_kwargs = attn_kwargs attn_modules = ['tensorflow.contrib.seq2seq', 'texar.custom'] # Use variable_scope to ensure all trainable variables created in # the attention mechanism are collected with tf.variable_scope(self.variable_scope): attention_mechanism = utils.check_or_get_instance( attn_hparams["type"], attn_kwargs, attn_modules, classtype=tf.contrib.seq2seq.AttentionMechanism) self._attn_cell_kwargs = { "attention_layer_size": attn_hparams["attention_layer_size"], "alignment_history": attn_hparams["alignment_history"], "output_attention": attn_hparams["output_attention"], } self._cell_input_fn = cell_input_fn # Use variable_scope to ensure all trainable variables created in # AttentionWrapper are collected with tf.variable_scope(self.variable_scope): #if attention_layer is not None: # self._attn_cell_kwargs["attention_layer_size"] = None attn_cell = AttentionWrapper( self._cell, attention_mechanism, cell_input_fn=self._cell_input_fn, #attention_layer=attention_layer, **self._attn_cell_kwargs) self._cell = attn_cell
def __init__(self, cell=None, cell_dropout_mode=None, vocab_size=None, output_layer=None, hparams=None): RNNDecoderBase.__init__(self, cell, vocab_size, output_layer, cell_dropout_mode, hparams)
def __init__(self, cell=None, cell_dropout_mode=None, vocab_size=None, output_layer=None, position_embedder=None, hparams=None): RNNDecoderBase.__init__(self, cell, vocab_size, output_layer, cell_dropout_mode, hparams) self.position_embedder = position_embedder self.current_segment_id = -1