def senEncode_softmax(s_senEncode, w_varible, b_varible, n_feature, doc_len):
s = tf.reshape(s_senEncode, [-1, n_feature])
s = tf.nn.dropout(s, keep_prob=FLAGS.keep_prob2)
w = func.get_weight_varible(w_varible, [n_feature, FLAGS.n_class])
b = func.get_weight_varible(b_varible, [FLAGS.n_class])
pred = tf.matmul(s, w) + b
pred *= func.getmask(doc_len, FLAGS.max_doc_len, [-1, 1])
pred = tf.nn.softmax(pred)
pred = tf.reshape(pred, [-1, FLAGS.max_doc_len, FLAGS.n_class])
reg = tf.nn.l2_loss(w) + tf.nn.l2_loss(b)
return pred, reg
def build_model(x,
sen_len,
doc_len,
word_embedding,
clause_position,
embedding_pos,
keep_prob1,
keep_prob2,
RNN=func.biLSTM):
x = tf.nn.embedding_lookup(word_embedding, x)
inputs = tf.reshape(x, [-1, FLAGS.max_sen_len, FLAGS.embedding_dim])
n_hidden = 2 * FLAGS.n_hidden
inputs = tf.nn.dropout(inputs, keep_prob=keep_prob1)
sen_len = tf.reshape(sen_len, [-1])
with tf.name_scope('word_encode'):
wordEncode = RNN(inputs,
sen_len,
n_hidden=FLAGS.n_hidden,
scope=FLAGS.scope + 'word_layer')
wordEncode = tf.reshape(wordEncode, [-1, FLAGS.max_sen_len, n_hidden])
with tf.name_scope('attention'):
w1 = func.get_weight_varible('word_att_w1', [n_hidden, n_hidden])
b1 = func.get_weight_varible('word_att_b1', [n_hidden])
w2 = func.get_weight_varible('word_att_w2', [n_hidden, 1])
senEncode = func.att_var(wordEncode, sen_len, w1, b1, w2)
# (32*75,200)
senEncode = tf.reshape(senEncode,
[-1, FLAGS.max_doc_len, n_hidden]) #(32, 75, 200)
n_feature = 2 * FLAGS.n_hidden
out_units = 2 * FLAGS.n_hidden #200
batch = tf.shape(senEncode)[0] #32
pred_zeros = tf.zeros(([batch, FLAGS.max_doc_len,
FLAGS.max_doc_len])) #(32,75,75)
matrix = tf.reshape(
(1 - tf.eye(FLAGS.max_doc_len)),
[1, FLAGS.max_doc_len, FLAGS.max_doc_len]) + pred_zeros # 构造单位矩阵
pred_emotion_assist_list, reg_emotion_assist_list, pred_emotion_assist_label_list = [], [], []
pred_cause_assist_list, reg_cause_assist_list, pred_cause_assist_label_list = [], [], []
if FLAGS.assist_n_layers > 1:
'''******* emotion layer 1******'''
emotion_senEncode = trans_func(senEncode, senEncode, n_feature,
out_units,
'emotion_layer1') #(32,75,200)
pred_emotion_assist, reg_emotion_assist = senEncode_emotion_softmax(
emotion_senEncode, 'softmax_assist_w1', 'softmax_assist_b1',
out_units, doc_len)
#(32, 75,2)
pred_emotion_assist_label = tf.cast(
tf.reshape(tf.argmax(pred_emotion_assist, axis=2),
[-1, 1, FLAGS.max_doc_len]), tf.float32)
#(32, 75, 1)=>(32, 1, 75)
pred_emotion_assist_position = tf.cast(
tf.reshape(tf.argmax(pred_emotion_assist_label, axis=2), [-1, 1]) +
1, tf.int32) #emotion clause的所在位置，辅助clause的提取
pred_clause_relative_position = tf.cast(
tf.reshape(clause_position - pred_emotion_assist_position + 69,
[-1, FLAGS.max_doc_len]),
tf.float32) #基于emotion clause的相对位置 (32, 1, 75)
pred_clause_relative_position *= func.getmask(doc_len,
FLAGS.max_doc_len,
[-1, FLAGS.max_doc_len])
pred_clause_relative_position = tf.cast(pred_clause_relative_position,
tf.int32)
pred_clause_rep_embed = tf.nn.embedding_lookup(
embedding_pos, pred_clause_relative_position) #(32, 75, 50)
pred_emotion_assist_label = (pred_emotion_assist_label +
pred_zeros) * matrix # 屏蔽预测为1的标签
#matrix=>(32, 75, 75)
#pred_assist_label=>(32, 75, 75)
pred_emotion_assist_label_list.append(pred_emotion_assist_label)
pred_emotion_assist_list.append(pred_emotion_assist)
reg_emotion_assist_list.append(reg_emotion_assist)
'''******* cause layer 1******'''
cause_senEncode_assist = tf.concat([senEncode, pred_clause_rep_embed],
axis=2)
n_feature = out_units + FLAGS.embedding_dim_pos
cause_senEncode = trans_func(cause_senEncode_assist, senEncode,
n_feature, out_units, 'cause_layer')
pred_cause_assist, reg_cause_assist = senEncode_cause_softmax(
cause_senEncode, 'cause_softmax_assist_w1',
'cause_softmax_assist_b1', out_units, doc_len)
# (32, 75,2)
pred_cause_assist_label = tf.cast(
tf.reshape(tf.argmax(pred_cause_assist, axis=2),
[-1, 1, FLAGS.max_doc_len]), tf.float32)
# (32, 75, 1)=>(32, 1, 75)
pred_cause_assist_label = (pred_cause_assist_label +
pred_zeros) * matrix # 屏蔽预测为1的标签
# matrix=>(32, 75, 75)
# pred_assist_label=>(32, 75, 75)
pred_cause_assist_label_list.append(pred_cause_assist_label)
pred_cause_assist_list.append(pred_cause_assist)
reg_cause_assist_list.append(reg_cause_assist)
for i in range(2, FLAGS.assist_n_layers):
emotion_senEncode_assist = tf.concat([
emotion_senEncode, pred_emotion_assist_label,
pred_cause_assist_label
],
axis=2) # (32, 75, 275)
n_feature = out_units + 2 * FLAGS.max_doc_len # 275
emotion_senEncode = trans_func(emotion_senEncode_assist,
emotion_senEncode, n_feature, out_units,
'emotion_layer' + str(i)) # (32,75,200)
pred_emotion_assist, reg_emotion_assist = senEncode_emotion_softmax(
emotion_senEncode, 'emotion_softmax_assist_w' + str(i),
'emotion_softmax_assist_b' + str(i), out_units, doc_len)
pred_emotion_assist_label = tf.cast(
tf.reshape(tf.argmax(pred_emotion_assist, axis=2),
[-1, 1, FLAGS.max_doc_len]), tf.float32)
# pred_emotion_assist_position = tf.cast(tf.reshape(tf.argmax(pred_emotion_assist_label, axis=2), [-1, 1]),
# tf.float32) + 1 # emotion clause的所在位置，辅助clause的提取
# pred_clause_relative_position = tf.reshape(clause_position - pred_emotion_assist_position,
# [-1, FLAGS.max_doc_len]) # 基于emotion clause的相对位置 (32, 1, 75)
# pred_clause_relative_position *= func.getmask(doc_len, FLAGS.max_doc_len, [-1, FLAGS.max_doc_len])
# pred_clause_rep_embed = tf.nn.embedding_lookup(embedding_pos, pred_clause_relative_position) # (32, 75, 50)
pred_emotion_assist_label = (pred_emotion_assist_label +
pred_zeros) * matrix
pred_emotion_assist_label_list.append(pred_emotion_assist_label)
pred_emotion_assist_label = tf.reduce_sum(
pred_emotion_assist_label_list, axis=0)
# 不同layer加和 pred_assist_label=>(32,75,75)
pred_emotion_assist_list.append(pred_emotion_assist)
reg_emotion_assist_list.append(reg_emotion_assist)
cause_senEncode_assist = tf.concat([
cause_senEncode, pred_cause_assist_label, pred_emotion_assist_label
],
axis=2) #(32, 75, 275)
n_feature = out_units + 2 * FLAGS.max_doc_len #275
cause_senEncode = trans_func(cause_senEncode_assist, cause_senEncode,
n_feature, out_units,
'cause_layer' + str(i)) #(32,75,200)
pred_cause_assist, reg_cause_assist = senEncode_cause_softmax(
cause_senEncode, 'cause_softmax_assist_w' + str(i),
'cause_softmax_assist_b' + str(i), out_units, doc_len)
pred_cause_assist_label = tf.cast(
tf.reshape(tf.argmax(pred_cause_assist, axis=2),
[-1, 1, FLAGS.max_doc_len]), tf.float32)
pred_cause_assist_label = (pred_cause_assist_label +
pred_zeros) * matrix
pred_cause_assist_label_list.append(pred_cause_assist_label)
pred_cause_assist_label = tf.reduce_sum(pred_cause_assist_label_list,
axis=0)
#不同layer加和 pred_assist_label=>(32,75,75)
pred_cause_assist_list.append(pred_cause_assist)
reg_cause_assist_list.append(reg_cause_assist)
'''*******Main******'''
if FLAGS.main_n_layers > 1:
senEncode_main = tf.concat([emotion_senEncode, cause_senEncode],
axis=2)
n_feature = 2 * out_units
senEncode_main = trans_func(senEncode_main, senEncode_main, n_feature,
out_units, 'main_layer1')
senEncode_main = tf.concat([
senEncode_main, pred_emotion_assist_label, pred_cause_assist_label
],
axis=2)
n_feature = out_units + 2 * FLAGS.max_doc_len
senEncode_main = trans_func(senEncode_main, senEncode_main, n_feature,
out_units, 'main_layer2')
else:
senEncode_main = tf.concat([emotion_senEncode, cause_senEncode],
axis=2)
n_feature = 2 * out_units
senEncode_main = trans_func(senEncode_main, senEncode_main, n_feature,
out_units, 'main_layer1')
pred, reg = senEncode_main_softmax(senEncode_main, 'softmax_w',
'softmax_b', out_units, doc_len)
return pred, reg, pred_emotion_assist_list, reg_emotion_assist_list, pred_cause_assist_list, reg_cause_assist_list