in ocr/utils/encoder_decoder.py [0:0]
def _get_attention_cell(attention_cell, units=None,
scaled=True, num_heads=None,
use_bias=False, dropout=0.0):
"""
Parameters
----------
attention_cell : AttentionCell or str
units : int or None
Returns
-------
attention_cell : AttentionCell
"""
if isinstance(attention_cell, str):
if attention_cell == 'scaled_luong':
return DotProductAttentionCell(units=units, scaled=True, normalized=False,
use_bias=use_bias, dropout=dropout, luong_style=True)
elif attention_cell == 'scaled_dot':
return DotProductAttentionCell(units=units, scaled=True, normalized=False,
use_bias=use_bias, dropout=dropout, luong_style=False)
elif attention_cell == 'dot':
return DotProductAttentionCell(units=units, scaled=False, normalized=False,
use_bias=use_bias, dropout=dropout, luong_style=False)
elif attention_cell == 'cosine':
return DotProductAttentionCell(units=units, scaled=False, use_bias=use_bias,
dropout=dropout, normalized=True)
elif attention_cell == 'mlp':
return MLPAttentionCell(units=units, normalized=False)
elif attention_cell == 'normed_mlp':
return MLPAttentionCell(units=units, normalized=True)
elif attention_cell == 'multi_head':
base_cell = DotProductAttentionCell(scaled=scaled, dropout=dropout)
return MultiHeadAttentionCell(base_cell=base_cell, query_units=units, use_bias=use_bias,
key_units=units, value_units=units, num_heads=num_heads)
else:
raise NotImplementedError
else:
assert isinstance(attention_cell, AttentionCell),\
'attention_cell must be either string or AttentionCell. Received attention_cell={}'\
.format(attention_cell)
return attention_cell