import?os
import?pickle
import?random
import?jieba
import?numpy?as?np

from?keras?import?backend?as?K
from?keras.optimizers?import?SGD
from?keras.regularizers?import?l2
from?gensim.models?import?Word2Vec
from?keras.engine.topology?import?layer
from?keras.layers?import?Bidirectional
from?keras.engine.topology?import?layer
from?keras.utils?import?CustomobjectScope
from?keras.models?import?ModelSequentialload_model
from?keras.preprocessing.sequence?import?pad_sequences
from?keras.layers.normalization?import?BatchNormalization
from?keras.callbacks?import?EarlyStopping?ModelCheckpoint
from?keras?import?initializers?regularizers?constraints
from?keras.layers.core?import?Dense?Dropout?Activation?Flatten
from?keras.layers?import?Input?LSTM?Dense?merge?Flatten?embedding?DropoutBidirectionalGRU

embedDING_DIM?=?100

class?Attention（layer）:
????def?__init__（self?step_dim=130
?????????????????W_regularizer=None?b_regularizer=None
?????????????????W_constraint=None?b_constraint=None
?????????????????bias=True?**kwargs）:
????????self.supports_masking?=?True
????????#self.init?=?initializations.get（‘glorot_uniform‘）
????????self.init?=?initializers.get（‘glorot_uniform‘）

????????self.W_regularizer?=?regularizers.get（W_regularizer）
????????self.b_regularizer?=?regularizers.get（b_regularizer）

????????self.W_constraint?=?constraints.get（W_constraint）
????????self.b_constraint?=?constraints.get（b_constraint）

????????self.bias?=?bias
????????self.step_dim?=?step_dim
????????self.features_dim?=?0
????????super（Attention?self）.__init__（**kwargs）
????def?build（self?input_shape）:
????????assert?len（input_shape）?==?3
????????self.W?=?self.add_weight（（input_shape[-1]）
?????????????????????????????????initializer=self.init
?????????????????????????????????name=‘{}_W‘.format（self.name）
?????????????????????????????????regularizer=self.W_regularizer
?????????????????????????????????constraint=self.W_constraint）
????????self.features_dim?=?input_shape[-1]
????????if?self.bias:
????????????self.b?=?self.add_weight（（input_shape[1]）
?????????????????????????????????????initializer=‘zero‘
?????????????????????????????????????name=‘{}_b‘.format（self.name）
?????????????????????????????????????regularizer=self.b_regularizer
?????????????????????????????????????constraint=self.b_constraint）
????????else:
????????????self.b?=?None
????????self.built?=?True
????def?compute_mask（self?input?input_mask=None）:
????????return?None

????def?call（self?x?mask=None）:
????????features_dim?=?self.features_dim
????????step_dim?=?self.step_dim
????????eij?=?K.reshape（K.dot（K.reshape（x?（-1?features_dim））?K.reshape（self.W?（features_dim?1）））?（-1?step_dim））
????????if?self.bias:
????????????eij?+=?self.b
????????eij?=?K.tanh（eij）
????????a?=?K.exp（eij）
????????if?mask?is?not?None:
????????????a?*=?K.cast（mask?K.floatx（））
????????a?/=?K.cast（K.sum（a?axis=1?keepdims=True）?+?K.epsilo