import tensorflow as tf
import numpy as np
class SiameseLSTMw2v(object):
"""
A LSTM based deep Siamese network for text similarity.
Uses an word embedding layer (looks up in pre-trained w2v), followed by a biLSTM and Energy Loss layer.
"""
def stackedRNN(self, x, dropout, scope, embedding_size, sequence_length, hidden_units):
n_hidden=hidden_units
n_layers=3
# Prepare data shape to match `static_rnn` function requirements
x = tf.unstack(tf.transpose(x, perm=[1, 0, 2]))
# print(x)
# Define lstm cells with tensorflow
# Forward direction cell
with tf.name_scope("fw"+scope),tf.variable_scope("fw"+scope):
stacked_rnn_fw = []
for _ in range(n_layers):
fw_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)
lstm_fw_cell = tf.contrib.rnn.DropoutWrapper(fw_cell,output_keep_prob=dropout)
stacked_rnn_fw.append(lstm_fw_cell)
lstm_fw_cell_m = tf.nn.rnn_cell.MultiRNNCell(cells=stacked_rnn_fw, state_is_tuple=True)
outputs, _ = tf.nn.static_rnn(lstm_fw_cell_m, x, dtype=tf.float32)
return outputs[-1]
def contrastive_loss(self, y,d,batch_size):
tmp= y *tf.square(d)
#tmp= tf.mul(y,tf.square(d))
tmp2 = (1-y) *tf.square(tf.maximum((1 - d),0))
return tf.reduce_sum(tmp +tmp2)/batch_size/2
def __init__(
self, sequence_length, vocab_size, embedding_size, hidden_units, l2_reg_lambda, batch_size, trainableEmbeddings):
# Placeholders for input, output and dropout
self.input_x1 = tf.placeholder(tf.int32, [None, sequence_length], name="input_x1")
self.input_x2 = tf.placeholder(tf.int32, [None, sequence_length], name="input_x2")
self.input_y = tf.placeholder(tf.float32, [None], name="input_y")
self.dropout_keep_prob = tf.placeholder(tf.float32, name="dropout_keep_prob")
# Keeping track of l2 regularization loss (optional)
l2_loss = tf.constant(0.0, name="l2_loss")
# Embedding layer
with tf.name_scope("embedding"):
self.W = tf.Variable(
tf.constant(0.0, shape=[vocab_size, embedding_size]),
trainable=trainableEmbeddings,name="W")
self.embedded_words1 = tf.nn.embedding_lookup(self.W, self.input_x1)
self.embedded_words2 = tf.nn.embedding_lookup(self.W, self.input_x2)
print self.embedded_words1
# Create a convolution + maxpool layer for each filter size
with tf.name_scope("output"):
self.out1=self.stackedRNN(self.embedded_words1, self.dropout_keep_prob, "side1", embedding_size, sequence_length, hidden_units)
self.out2=self.stackedRNN(self.embedded_words2, self.dropout_keep_prob, "side2", embedding_size, sequence_length, hidden_units)
self.distance = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(self.out1,self.out2)),1,keep_dims=True))
self.distance = tf.div(self.distance, tf.add(tf.sqrt(tf.reduce_sum(tf.square(self.out1),1,keep_dims=True)),tf.sqrt(tf.reduce_sum(tf.square(self.out2),1,keep_dims=True))))
self.distance = tf.reshape(self.distance, [-1], name="distance")
with tf.name_scope("loss"):
self.loss = self.contrastive_loss(self.input_y,self.distance, batch_size)
#### Accuracy computation is outside of this class.
with tf.name_scope("accuracy"):
self.temp_sim = tf.subtract(tf.ones_like(self.distance),tf.rint(self.distance), name="temp_sim") #auto threshold 0.5
correct_predictions = tf.equal(self.temp_sim, self.input_y)
self.accuracy=tf.reduce_mean(tf.cast(correct_predictions, "float"), name="accuracy")
