def _inputEncodingBlock(self, scope):
with tf.device('/cpu:0'):
self.Embedding = tf.get_variable(
'Embedding', [self.n_vocab, self.embedding_size], tf.float32)
self.embeded_left = tf.nn.embedding_lookup(self.Embedding,
self.premise)
self.embeded_right = tf.nn.embedding_lookup(
self.Embedding, self.hypothesis)
print_shape('embeded_left', self.embeded_left)
print_shape('embeded_right', self.embeded_right)
with tf.variable_scope(scope):
outputsPremise, finalStatePremise = self._biLSTMBlock(
self.embeded_left, self.hidden_size, 'biLSTM',
self.premise_mask)
outputsHypothesis, finalStateHypothesis = self._biLSTMBlock(
self.embeded_right,
self.hidden_size,
'biLSTM',
self.hypothesis_mask,
isReuse=True)
a_bar = tf.concat(outputsPremise, axis=2)
b_bar = tf.concat(outputsHypothesis, axis=2)
print_shape('a_bar', a_bar)
print_shape('b_bar', b_bar)
return a_bar, b_bar
def _compositionBlock(self, m_a, m_b, hiddenSize, scope):
outputV_a, finalStateV_a = self._biLSTMBlock(m_a, hiddenSize, 'biLSTM')
outputV_b, finalStateV_b = self._biLSTMBlock(m_b,
hiddenSize,
'biLSTM',
isReuse=True)
v_a = tf.concat(outputV_a, axis=2)
v_b = tf.concat(outputV_b, axis=2)
print_shape('v_a', v_a)
print_shape('v_b', v_b)
v_a_avg = tf.reduce_mean(v_a, axis=1)
v_b_avg = tf.reduce_mean(v_b, axis=1)
v_a_max = tf.reduce_max(v_a, axis=1)
v_b_max = tf.reduce_max(v_b, axis=1)
print_shape('v_a_avg', v_a_avg)
print_shape('v_a_max', v_a_max)
print_shape('v_b_avg', v_b_avg)
print_shape('v_b_max', v_b_max)
v = tf.concat([v_a_avg, v_a_max, v_b_avg, v_b_max], axis=1)
print_shape('v', v)
y_hat = self._feedForwardBlock(v, self.hidden_size, self.n_classes,
'feed_forward')
return y_hat
def _localInferenceBlock(self, a_bar, b_bar, scope):
with tf.variable_scope(scope):
attentionWeights = tf.matmul(a_bar, tf.transpose(b_bar, [0, 2, 1]))
print_shape('att_wei', attentionWeights)
attentionSoft_a = tf.nn.softmax(attentionWeights)
attentionSoft_b = tf.nn.softmax(tf.transpose(attentionWeights))
attentionSoft_b = tf.transpose(attentionSoft_b)
print_shape('att_soft_a', attentionSoft_a)
print_shape('att_soft_b', attentionSoft_b)
a_hat = tf.matmul(attentionSoft_a, b_bar)
b_hat = tf.matmul(attentionSoft_b, a_bar)
print_shape('a_hat', a_hat)
print_shape('b_hat', b_hat)
a_diff = tf.subtract(a_bar, a_hat)
a_mul = tf.multiply(a_bar, a_hat)
print_shape('a_diff', a_diff)
print_shape('a_mul', a_mul)
b_diff = tf.subtract(b_bar, b_hat)
b_mul = tf.multiply(b_bar, b_hat)
print_shape('b_diff', b_diff)
print_shape('a_mul', b_mul)
m_a = tf.concat([a_bar, a_hat, a_diff, a_mul], axis=2)
m_b = tf.concat([b_bar, b_hat, b_diff, b_mul], axis=2)
print_shape('m_a', m_a)
print_shape('m_b', m_b)
return m_a, m_b