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model_char.py
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model_char.py
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import tensorflow as tf
from tensorflow.contrib import rnn
import numpy as np
from swish_activation import swish
from tensorflow.contrib.metrics import streaming_pearson_correlation
class model(object):
def __init__(
self, max_sequence_length,
total_classes,
embedding_size,
char_size,
char_embed_size,
id2Vecs,
batch_size,
max_word_len,
threshold=0.5,
lmd = 0
):
# placeholders
## sentence 1
self.x1 = tf.placeholder(tf.int32,[batch_size,max_sequence_length],name = "x")
self.chars_x1 = tf.placeholder(tf.int32,[batch_size,max_sequence_length,max_word_len])
## sentence 2
self.x2 = tf.placeholder(tf.int32, [batch_size, max_sequence_length], name="x")
self.chars_x2 = tf.placeholder(tf.int32, [batch_size, max_sequence_length, max_word_len])
## labels
self.labels = tf.placeholder(tf.float32,[batch_size,total_classes],name="labels")
## dropout
self.dropout = tf.placeholder(tf.float32,name="dropout")
## hidden units
self.hidden_Units = 100
self.char_embed_size = char_embed_size
self.batch_size = batch_size
self.id2Vecs = id2Vecs
# tf.split() might be useful
self.embedding_size = embedding_size
self.max_sequence_length = max_sequence_length
self.char_size = char_size
self.max_word_len = max_word_len
self.total_classes = total_classes
self.char_embed_size = char_embed_size
self.lmd = lmd
self.filter_sizes = [3, 4, 5]
self.num_filters = 50
self.total_filters = self.num_filters * len(self.filter_sizes)
self.threshold = threshold
self.l2_loss = tf.constant(value=0.0, dtype=tf.float32)
## change activation function here
# self.activation = swish
with tf.device('/cpu:0'):
# for i, filter_size in enumerate(self.filter_sizes):
# with tf.name_scope("conv-maxpool-%s" % filter_size) as scope:
# # Convolution Layer
# filter_shape = [filter_size, self.char_embed_size, 1, self.num_filters]
# self.W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name="W")
# self.b = tf.Variable(tf.constant(0.1, shape=[self.num_filters]), name="b")
#
with tf.variable_scope('this-scope') as scope:
self.right_lstm_cell = rnn.BasicLSTMCell(num_units=self.hidden_Units)
self.left_lstm_cell = rnn.BasicLSTMCell(num_units=self.hidden_Units)
self.right_lstm_cell = rnn.DropoutWrapper(self.right_lstm_cell, output_keep_prob=self.dropout)
self.left_lstm_cell = rnn.DropoutWrapper(self.left_lstm_cell, output_keep_prob=self.dropout)
sent_1 = self.get_text_emb(self.x1,self.chars_x1,name="sent_1")
scope.reuse_variables()
sent_2 = self.get_text_emb(self.x1, self.chars_x1,name="sent_2")
scope._reuse = None
out1 = self.get_out(sent_1)
out2 = self.get_out(sent_2)
# normalize_out1 = tf.nn.l2_normalize(out1, 1)
# normalize_out2 = tf.nn.l2_normalize(out2, 1)
out_matrix = []
for i in range(self.batch_size):
out_matrix.append(tf.matmul(out1[i,:,:],tf.transpose(out2[i,:,:])))
# out = tf.multiply(out1,out2)
out_matrix = tf.reshape(tf.stack(out_matrix),[-1,self.max_sequence_length*self.max_sequence_length])
with tf.name_scope("last-layer"):
self.W_f = tf.get_variable(
"W_f",
shape=[self.max_sequence_length * self.max_sequence_length, self.total_classes],
initializer=tf.contrib.layers.xavier_initializer()
)
bias = tf.Variable(tf.constant(value=0.01,shape=[self.total_classes],name="bias"))
self.l2_loss += tf.nn.l2_loss(self.W_f)
self.l2_loss += tf.nn.l2_loss(bias)
self.final_score = tf.nn.xw_plus_b(out_matrix,weights=self.W_f,biases=bias,name="scores")
with tf.name_scope("loss"):
self.loss = tf.losses.mean_squared_error(self.final_score, self.labels)
self.loss += lmd * self.l2_loss
## onjective function --> pearson correlation
with tf.name_scope("accuracy"):
# self.acc = tf.contrib.metrics.streaming_pearson_correlation(self.final_score, self.labels)[0]
self.acc = self.pearson_correlation(self.final_score,self.labels)
# self.acc = tf.reduce_mean(l)
def pearson_correlation(self,x,y):
"""
need to test this function
:param x: tensor 1
:param y: tensor 2
:return: pearson coefficient
"""
numerator = ( self.batch_size*tf.reduce_sum(x*y) - tf.reduce_sum(x)*tf.reduce_sum(y) )
denominator = tf.sqrt( (self.batch_size*tf.reduce_sum(x*x) - tf.reduce_sum(x)*tf.reduce_sum(x) )*
(self.batch_size*tf.reduce_sum(y*y) - tf.reduce_sum(y)*tf.reduce_sum(y) ) )
return numerator/denominator
def get_text_emb(self,x,chars_x,name):
sent_chars_1 = self.get_char_emb(chars_x)
word_emb_1 = self.get_word_emb(x,name+"_char_1")
return self.combine_char_word_emb(sent_chars_1,word_emb_1)
def get_char_emb(self,chars_x):
# layer contains trainable weights
with tf.name_scope("char-embeddings-layer"):
self.char_embeddings = tf.get_variable(
"char_embeddings",
shape=[self.char_size,self.char_embed_size],
initializer=tf.contrib.layers.xavier_initializer()
)
self.embedded_chars = tf.nn.embedding_lookup(self.char_embeddings, chars_x)
self.embedded_chars = tf.expand_dims(self.embedded_chars, -1)
self.embedded_chars = tf.reshape(self.embedded_chars, [-1, self.max_word_len, self.char_embed_size, 1])
pooled_outputs = []
for i, filter_size in enumerate(self.filter_sizes):
with tf.name_scope("conv-maxpool-%s" % filter_size):
# Convolution Layer
filter_shape = [filter_size, self.char_embed_size, 1, self.num_filters]
W = tf.get_variable(name="W_"+str(filter_size),shape=filter_shape)
b = tf.get_variable(name="b_"+str(filter_size),shape=[self.num_filters])
conv = tf.nn.conv2d(
self.embedded_chars,
W,
strides=[1, 1, 1, 1],
padding="VALID",
name="conv")
# Apply nonlinearity
h = tf.nn.relu(tf.nn.bias_add(conv,b), name="relu")
# Maxpooling over the outputs
pooled = tf.nn.max_pool(
h,
ksize=[1, self.max_word_len - filter_size + 1, 1, 1],
strides=[1, 1, 1, 1],
padding='VALID',
name="pool")
pooled_outputs.append(pooled)
char2word_embeddings = tf.reshape(tf.squeeze(tf.concat(pooled_outputs, axis=3)),[self.batch_size,self.max_sequence_length,self.total_filters])
return char2word_embeddings
def get_word_emb(self,x,name):
"""
:param x:
:return:
"""
with tf.device('/cpu:0'):
with tf.name_scope("word-embedding-layer"):
self.embeddings = tf.Variable(initial_value=self.id2Vecs,dtype=tf.float32,name='embedding_lookup')
word_embeddings = tf.nn.embedding_lookup(self.embeddings,x,name=name)
return word_embeddings
def get_out(self,sentences):
self.outputs,self.state = tf.nn.bidirectional_dynamic_rnn(self.left_lstm_cell,self.right_lstm_cell,sentences,dtype=tf.float32)
combined_output = tf.concat(self.outputs, axis=2)
out = tf.reshape(combined_output,shape=[self.batch_size,self.max_sequence_length,self.hidden_Units*2])
# out = combined_output[:,-1,:]
return out
def combine_char_word_emb(self,chars_emb,word_emb):
"""
:param chars_emb:
:param word_emb:
:return: combine char and word embeddings
"""""
sentences =tf.concat(
[
tf.reshape(word_emb,[-1,self.batch_size,self.max_sequence_length,self.embedding_size])
,
tf.reshape(chars_emb,[-1,self.batch_size,self.max_sequence_length,self.total_filters])
],
axis=3
)
sentences = tf.squeeze(sentences)
sentences = tf.layers.dropout(sentences,rate=self.dropout)
return sentences
# convert data to slices
#Initial state of the LSTM memory.
# hidden_state = tf.zeros([self.batch_size,])
#
# self.sequence = tf.split(self.sentences,num_or_size_splits=max_sequence_length,axis=1)
# m = model(max_sequence_length=11,total_classes=5,embedding_size=300,char_size = 357,max_word_len =12 ,char_embed_size = 30,id2Vecs=np.zeros([7,300]),batch_size=3)