def __init__(self, vocab_size, emb_size, region_sizes=[3,4,5], \
region_merge_fn=tf.reduce_max, \
name="multi_region_embedding", \
initializer=None, \
**kwargs):
BaseLayer.__init__(self, name, **kwargs)
self._emb_size = emb_size
self._region_sizes = region_sizes[:]
self._region_sizes.sort()
self._region_merge_fn = region_merge_fn
region_num = len(region_sizes)
self._K = [None] * region_num
self._K[-1] = tf.get_variable(name + '_K_%d' % (region_num - 1), \
shape=[vocab_size, self._region_sizes[-1], emb_size], \
initializer=initializer)
for i in range(region_num - 1):
st = int(self._region_sizes[-1] / 2 - self._region_sizes[i] / 2)
ed = st + self._region_sizes[i]
self._K[i] = self._K[-1][:, st:ed, :]
super(MultiRegionEmbedding, self).__init__(vocab_size, emb_size, name,
initializer, **kwargs)
def __init__(self, vocab_size, emb_size, region_size=3, \
region_merge_fn=tf.reduce_max, \
name="win_pool_embedding", \
initializer=None, \
**kwargs):
BaseLayer.__init__(self, name, **kwargs)
self._region_size = region_size
self._region_merge_fn = region_merge_fn
super(WinPoolEmbedding, self).__init__(vocab_size, emb_size, name,
initializer, **kwargs)
def __init__(self,
vocab_size,
emb_size,
name="embedding",
initializer=None,
**kwargs):
BaseLayer.__init__(self, name, **kwargs)
self._emb_size = emb_size
if not initializer:
initializer = tf.contrib.layers.variance_scaling_initializer()
self._W = self.get_variable(name + '_W',
shape=[vocab_size, emb_size],
initializer=initializer)
def __init__(self, vocab_size, emb_size, region_size=3, \
region_merge_fn=tf.reduce_max, \
name="scalar_region_embedding", \
initializer=None, \
**kwargs):
BaseLayer.__init__(self, name, **kwargs)
self._emb_size = emb_size
self._region_size = region_size
self._region_merge_fn = region_merge_fn
if not initializer:
initializer = tf.contrib.layers.variance_scaling_initializer()
self._K = self.get_variable(name + '_K',
shape=[vocab_size, region_size, 1],
initializer=initializer)
super(ScalarRegionEmbedding, self).__init__(vocab_size, emb_size, name,
initializer, **kwargs)
def __init__(self,
vocab_size,
emb_size,
name="character_embedding",
initializer=None,
**kwargs):
"""
Params: emb_size: embedding dim
vocab_size: character vocab size
hiddden_size: hidden_layer size
"""
BaseLayer.__init__(self, name, **kwargs)
self._emb_size = emb_size
self.hidden_size = kwargs[
"hidden_size"] if "hidden_size" in kwargs else 64
if not initializer:
initializer = tf.contrib.layers.variance_scaling_initializer()
self._W = self.get_variable(name + '_W',
shape=[vocab_size, emb_size],
initializer=initializer)
def __init__(self, region_size, name="RegionAlig", **args):
BaseLayer.__init__(self, name, **args)
self._region_size = region_size