def build_graph(self):
"""Builds the main part of the graph for the model, starting from the input embeddings to the final distributions for the answer span.
Defines:
self.logits_start, self.logits_end: Both tensors shape (batch_size, context_len).
These are the logits (i.e. values that are fed into the softmax function) for the start and end distribution.
Important: these are -large in the pad locations. Necessary for when we feed into the cross entropy function.
self.probdist_start, self.probdist_end: Both shape (batch_size, context_len). Each row sums to 1.
These are the result of taking (masked) softmax of logits_start and logits_end.
"""
# Use a RNN to get hidden states for the context and the question
# Note: here the RNNEncoder is shared (i.e. the weights are the same)
# between the context and the question.
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
context_hiddens = encoder.build_graph(self.context_embs, self.context_mask) # (batch_size, context_len, hidden_size*2)
question_hiddens = encoder.build_graph(self.qn_embs, self.qn_mask) # (batch_size, question_len, hidden_size*2)
# Use context hidden states to attend to question hidden states
attn_layer = BasicAttn(self.keep_prob, self.FLAGS.hidden_size*2, self.FLAGS.hidden_size*2)
_, attn_output = attn_layer.build_graph(question_hiddens, self.qn_mask, context_hiddens) # attn_output is shape (batch_size, context_len, hidden_size*2)
# Concat attn_output to context_hiddens to get blended_reps
blended_reps = tf.concat([context_hiddens, attn_output], axis=2) # (batch_size, context_len, hidden_size*4)
# Apply fully connected layer to each blended representation
# Note, blended_reps_final corresponds to b' in the handout
# Note, tf.contrib.layers.fully_connected applies a ReLU non-linarity here by default
blended_reps_final = tf.contrib.layers.fully_connected(blended_reps, num_outputs=self.FLAGS.hidden_size) # blended_reps_final is shape (batch_size, context_len, hidden_size)
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with vs.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(blended_reps_final, self.context_mask)
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with vs.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(blended_reps_final, self.context_mask)
def build_graph(self):
"""Builds the main part of the graph for the model, starting from the input embeddings to the final distributions for the answer span.
"""
# Use a RNN to get hidden states for the context and the question
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
_,context_hiddens = encoder.build_graph(self.context_embs, self.context_mask) # (batch_size, context_len, hidden_size*2)
_,question_hiddens = encoder.build_graph(self.qn_embs, self.qn_mask) # (batch_size, question_len, hidden_size*2)
# Use context hidden states to attend to question hidden states
attn_layer = BasicAttn(self.keep_prob, self.FLAGS.hidden_size*2, self.FLAGS.hidden_size*2)
_, attn_output = attn_layer.build_graph(question_hiddens, self.qn_mask, context_hiddens) # attn_output is shape (batch_size, context_len, hidden_size*2)
# Concat attn_output to context_hiddens to get blended_reps
blended_reps = tf.concat([context_hiddens, attn_output], axis=2) # (batch_size, context_len, hidden_size*4)
# Apply fully connected layer to each blended representation
blended_reps_final = tf.contrib.layers.fully_connected(blended_reps, num_outputs=self.FLAGS.hidden_size) # blended_reps_final is shape (batch_size, context_len, hidden_size)
# Use softmax layer to compute probability distribution for start location
with vs.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(blended_reps_final, self.context_mask, False)
# Use softmax layer to compute probability distribution for end location
with vs.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(blended_reps_final, self.context_mask, False)
def build_graph(self):
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
context_hiddens = encoder.build_graph(
self.context_embs,
self.context_mask) # (batch_size, context_len, hidden_size*2)
question_hiddens = encoder.build_graph(
self.qn_embs,
self.qn_mask) # (batch_size, question_len, hidden_size*2)
# Use context hidden states to attend to question hidden states
attn_layer = BasicAttn(self.keep_prob, self.FLAGS.hidden_size * 2)
_, attn_output = attn_layer.build_graph(
question_hiddens, self.qn_mask, context_hiddens
) # attn_output is shape (batch_size, context_len, hidden_size*2)
attn_layer = R_Net_Attn(self.keep_prob, self.FLAGS.hidden_size * 2,
self.FLAGS)
output = attn_layer.build_graph(
attn_output, self.context_mask
) # attn_output is shape (batch_size, context_len, hidden_size*2)
blended_reps_final = tf.contrib.layers.fully_connected(
tf.concat([attn_output, output], 2),
num_outputs=self.FLAGS.hidden_size
) # blended_reps_final is shape (batch_size, context_len, hidden_size)
with vs.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(
blended_reps_final, self.context_mask)
with vs.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(
blended_reps_final, self.context_mask)
def build_graph(self):
"""Builds the main part of the graph for the model, starting from the input embeddings to the final distributions for the answer span.
Defines:
self.logits_start, self.logits_end: Both tensors shape (batch_size, context_len).
These are the logits (i.e. values that are fed into the softmax function) for the start and end distribution.
Important: these are -large in the pad locations. Necessary for when we feed into the cross entropy function.
self.probdist_start, self.probdist_end: Both shape (batch_size, context_len). Each row sums to 1.
These are the result of taking (masked) softmax of logits_start and logits_end.
"""
# Use a RNN to get hidden states for the context and the question
# Note: here the RNNEncoder is shared (i.e. the weights are the same)
# between the context and the question.
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
print self.context_embs.shape
context_hiddens = encoder.build_graph(
self.context_embs,
self.context_mask) # (batch_size, context_len, hidden_size*2)
question_hiddens = encoder.build_graph(
self.qn_embs,
self.qn_mask) # (batch_size, question_len, hidden_size*2)
# Use context hidden states to attend to question hidden states
biattn_layer = AttentionFlowLayer(self.keep_prob, self.FLAGS.l2_lambda)
biattn_output = biattn_layer.build_graph(context_hiddens,
self.context_mask,
question_hiddens,
self.qn_mask,
scope="AttnFlow")
#RNNEncoder layer
model_layer = Model_Layer(self.FLAGS.hidden_size, self.keep_prob)
model_output = model_layer.build_graph(biattn_output,
self.context_mask)
#Fully connected
# Apply fully connected layer to each blended representation
# Note, blended_reps_final corresponds to b' in the handout
# Note, tf.contrib.layers.fully_connected applies a ReLU non-linarity here by default
blended_reps_final = tf.contrib.layers.fully_connected(
model_output, num_outputs=self.FLAGS.hidden_size
) # blended_reps_final is shape (batch_size, context_len, hidden_size)
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with vs.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(
blended_reps_final, self.context_mask)
#What are the masks used for?
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with vs.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(
blended_reps_final, self.context_mask)
def build_graph(self):
"""Builds the main part of the graph for the model, starting from the input embeddings to the final distributions for the answer span.
Defines:
self.logits_start, self.logits_end: Both tensors shape (batch_size, context_len).
These are the logits (i.e. values that are fed into the softmax function) for the start and end distribution.
Important: these are -large in the pad locations. Necessary for when we feed into the cross entropy function.
self.probdist_start, self.probdist_end: Both shape (batch_size, context_len). Each row sums to 1.
These are the result of taking (masked) softmax of logits_start and logits_end.
self.c2q_dist : (batch_size, context_len, question_len) Context to Question attention probability. Each row should sum to 1
except if the context word is masked.
self.q2c_dist : (batch_size, context_len) Question to Context attention probability. Each row should sum to 1.
"""
print("Building BIDAF")
# Use a RNN to get hidden states for the context and the question
# Note: here the RNNEncoder is shared (i.e. the weights are the same)
# between the context and the question.
encoder = RNNEncoder(self.FLAGS.hidden_size,
self.keep_prob,
num_layers=self.FLAGS.num_layers,
mode=self.FLAGS.rnn_cell)
context_hiddens = encoder.build_graph(
self.context_embs,
self.context_mask) # (batch_size, context_len, hidden_size*2)
question_hiddens = encoder.build_graph(
self.qn_embs,
self.qn_mask) # (batch_size, question_len, hidden_size*2)
# Use context hidden states to attend to question hidden states
attn_layer = BidirectionAttn(self.keep_prob, self.FLAGS.hidden_size)
self.c2q_attn_dist, self.q2c_attn_dist, attn_output = \
attn_layer.build_graph(question_hiddens, self.qn_mask, context_hiddens, self.context_mask) # attn_output is shape (batch_size, context_len, hidden_size*4)
# Concat attn_output to context_hiddens to get blended_reps
blended_reps = tf.concat(
[context_hiddens, attn_output],
axis=2) # (batch_size, context_len, hidden_size*8)
# Apply fully connected layer to each blended representation
# Note, blended_reps_final corresponds to b' in the handout
# Note, tf.contrib.layers.fully_connected applies a ReLU non-linarity here by default
blended_reps_final = tf.contrib.layers.fully_connected(
blended_reps, num_outputs=self.FLAGS.hidden_size
) # blended_reps_final is shape (batch_size, context_len, hidden_size)
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with vs.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(
blended_reps_final, self.context_mask)
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with vs.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(
blended_reps_final, self.context_mask)
def build_graph(self):
"""Builds the main part of the graph for the model, starting from the input embeddings to the final distributions for the answer span.
Defines:
self.logits_start, self.logits_end: Both tensors shape (batch_size, context_len).
These are the logits (i.e. values that are fed into the softmax function) for the start and end distribution.
Important: these are -large in the pad locations. Necessary for when we feed into the cross entropy function.
self.probdist_start, self.probdist_end: Both shape (batch_size, context_len). Each row sums to 1.
These are the result of taking (masked) softmax of logits_start and logits_end.
"""
with vs.variable_scope("Encoder"):
# Use a RNN to get hidden states for the context and the question
# Note: here the RNNEncoder is shared (i.e. the weights are the same)
# between the context and the question.
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
context_hiddens = encoder.build_graph(self.context_embs, self.context_mask) # (batch_size, context_len, hidden_size*2)
question_hiddens = encoder.build_graph(self.qn_embs, self.qn_mask) # (batch_size, question_len, hidden_size*2)
# Use context hidden states to attend to question hidden states
attn_layer = CoAttn(self.keep_prob, self.FLAGS.hidden_size*2, self.FLAGS.hidden_size*2)
_, _, attn_output = attn_layer.build_graph(question_hiddens, self.qn_mask, context_hiddens, self.context_mask) # shapes are U_tilde: (batch_size, context_len, 2h), H_tilde: (batch_size, context_len, 1)
# Concat attn_output to context_hiddens to get blended_reps
blended_reps = tf.concat([context_hiddens, attn_output, context_hiddens * attn_output], axis=2) # (batch_size, context_len, hidden_size*8)
with vs.variable_scope("M1_init"):
# Bidirectional GRU M1
modeling_layer = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
blended_reps_1_init = modeling_layer.build_graph(blended_reps, self.context_mask) # (batch_size, N, 2h)
with vs.variable_scope("M1"):
# Bidrectional GRU M2
modeling_layer = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
blended_reps_1 = modeling_layer.build_graph(blended_reps_1_init, self.context_mask) # (batch_size, N, 2h)
with vs.variable_scope("M2"):
# Bidrectional GRU M2
modeling_layer = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
blended_reps_2 = modeling_layer.build_graph(blended_reps_1, self.context_mask) # (batch_size, N, 2h)
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with vs.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(tf.concat([blended_reps, blended_reps_1], axis=2), self.context_mask)
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with vs.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(tf.concat([blended_reps, blended_reps_2], axis=2), self.context_mask)
def build_graph(self):
context_embs_concat = tf.concat(
[self.elmo_context_input, self.context_embs],
2) #(batch_size, qn_len, 1024+self.FLAGS.embedding_size)
context_embs_concat.set_shape(
(None, None, 1024 + self.FLAGS.embedding_size))
#qn_embs_concat.set_shape((None, None, 1024+self.FLAGS.embedding_size))
self.qn_mask.set_shape((None, None))
self.context_mask.set_shape((None, None))
with tf.variable_scope("start"):
softmax_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_start.build_graph(
context_embs_concat, self.context_mask)
with tf.variable_scope("end"):
softmax_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_end.build_graph(
context_embs_concat, self.context_mask)
def build_graph(self):
"""Builds the main part of the graph for the model, starting from the input embeddings to the final distributions for the answer span.
Defines:
self.logits_start, self.logits_end: Both tensors shape (batch_size, context_len).
These are the logits (i.e. values that are fed into the softmax function) for the start and end distribution.
Important: these are -large in the pad locations. Necessary for when we feed into the cross entropy function.
self.probdist_start, self.probdist_end: Both shape (batch_size, context_len). Each row sums to 1.
These are the result of taking (masked) softmax of logits_start and logits_end.
"""
# Use a RNN to get hidden states for the context and the question
# Note: here the RNNEncoder is shared (i.e. the weights are the same)
# between the context and the question.
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
encoderQ = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
context_hiddens = encoder.build_graph(self.context_embs, self.context_mask,"rnnencoder1") # (batch_size, context_len, hidden_size*2)
question_hiddens = encoderQ.build_graph(self.qn_embs, self.qn_mask,"rnnencoderQ") # (batch_size, question_len, ,"rnnencoder1"hidden_size*2)
# Use context hidden states to attend to question hidden states
attn_layer = BasicAttn(self.keep_prob, self.FLAGS.hidden_size*2, self.FLAGS.hidden_size*2)
_, attn_output,new_attn = attn_layer.build_graph(question_hiddens, self.qn_mask, context_hiddens,2*self.FLAGS.hidden_size) # attn_output is shape (batch_size, context_len, hidden_size*2)
_,_,blended_reps_final=build_graph_middle(self,new_attn,attn_output,context_hiddens,question_hiddens)
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with vs.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(blended_reps_final, self.context_mask)
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with vs.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(blended_reps_final, self.context_mask)
'''
def build_graph_coattention(self):
"""Builds the main part of the graph for the model, starting from the input embeddings to the final distributions for the answer span.
"""
# Use a RNN to get hidden states for the context and the question
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
_,context_hiddens = encoder.build_graph(self.context_embs, self.context_mask) # (batch_size, context_len, hidden_size*2)
_,question_hiddens = encoder.build_graph(self.qn_embs, self.qn_mask) # (batch_size, question_len, hidden_size*2)
# Compute both sided attention
coatt= Coattention()
co_att= coatt.build_graph(self.FLAGS.batch_size,question_hiddens, context_hiddens, self.FLAGS.question_len, self.FLAGS.context_len, 2*self.FLAGS.hidden_size, self.keep_prob)
co_att_final = tf.contrib.layers.fully_connected(co_att, num_outputs=self.FLAGS.hidden_size)
# Use softmax layer to compute probability distribution for start location
with vs.variable_scope("StartDist") as scp:
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(co_att_final, self.context_mask, True)
scp.reuse_variables()
# Use softmax layer to compute probability distribution for end location
with vs.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(co_att_final, self.context_mask, True)
def build_graph(self):
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
if self.FLAGS.max_word_len:
context_hiddens = encoder.build_graph(
tf.concat([self.context_embs, self.context_char_hidden], 2),
self.context_mask) # (batch_size, context_len, hidden_size*2)
question_hiddens = encoder.build_graph(
tf.concat([self.qn_embs, self.qn_char_hidden], 2),
self.qn_mask) # (batch_size, question_len, hidden_size*2)
else:
context_hiddens = encoder.build_graph(
self.context_embs,
self.context_mask) # (batch_size, context_len, hidden_size*2)
question_hiddens = encoder.build_graph(
self.qn_embs,
self.qn_mask) # (batch_size, question_len, hidden_size*2)
attn_layer = BiDAF_Attn(self.keep_prob, self.FLAGS.hidden_size * 2, [
self.FLAGS.batch_size, self.FLAGS.context_len,
self.FLAGS.question_len
])
output = attn_layer.build_graph(
question_hiddens, self.qn_mask, context_hiddens, self.context_mask
) # attn_output is shape (batch_size, context_len, hidden_size*2)
blended_reps_final = tf.contrib.layers.fully_connected(
output, num_outputs=self.FLAGS.hidden_size)
with vs.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(
blended_reps_final, self.context_mask)
with vs.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(
blended_reps_final, self.context_mask)
def build_graph(self):
attn_layer = DynamicAttention_Attn(self.keep_prob, self.FLAGS)
output = attn_layer.build_graph(
self.qn_embs, self.qn_mask, self.context_embs, self.context_mask
) # attn_output is shape (batch_size, context_len, hidden_size*2)
encoder = RNNEncoder(self.FLAGS.embedding_size * 2, self.keep_prob)
context_hiddens = encoder.build_graph(
output,
self.context_mask) # (batch_size, context_len, embedding_size*4)
blended_reps_final = tf.contrib.layers.fully_connected(
context_hiddens, num_outputs=self.FLAGS.hidden_size
) # blended_reps_final is shape (batch_size, context_len, hidden_size)
with vs.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(
blended_reps_final, self.context_mask)
with vs.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(
blended_reps_final, self.context_mask)
def build_graph(self):
"""Builds the main part of the graph for the model, starting from the input embeddings to the final distributions for the answer span.
Defines:
self.logits_start, self.logits_end: Both tensors shape (batch_size, context_len).
These are the logits (i.e. values that are fed into the softmax function) for the start and end distribution.
Important: these are -large in the pad locations. Necessary for when we feed into the cross entropy function.
self.probdist_start, self.probdist_end: Both shape (batch_size, context_len). Each row sums to 1.
These are the result of taking (masked) softmax of logits_start and logits_end.
"""
# Use a RNN to get hidden states for the context and the question
# Note: here the RNNEncoder is shared (i.e. the weights are the same)
# between the context and the question.
with vs.variable_scope("e1c"):
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
context_hiddens = encoder.build_graph(
self.context_embs,
self.context_mask) # (batch_size, context_len, hidden_size*2)
with vs.variable_scope("e1q"):
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
question_hiddens = encoder.build_graph(
self.qn_embs,
self.qn_mask) # (batch_size, question_len, hidden_size*2)
# con_qn_hiddens = encoder.build_graph(self.con_qn_embs, self.con_qn_mask)
# context_hiddens = con_qn_hiddens[:, :self.FLAGS.context_len, :]
# question_hiddens = con_qn_hiddens[:, self.FLAGS.context_len:, :]
# with vs.variable_scope("e2"):
# encoder1 = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
# context_hiddens = encoder1.build_graph(context_hiddens, self.context_mask) # (batch_size, context_len, hidden_size*2)
# question_hiddens = encoder1.build_graph(question_hiddens, self.qn_mask) # (batch_size, question_len, hidden_size*2)
# Use context hidden states to attend to question hidden states
# attn_layer = BasicAttn(self.keep_prob, self.FLAGS.hidden_size*2, self.FLAGS.hidden_size*2)
# _, attn_output = attn_layer.build_graph(question_hiddens, self.qn_mask, context_hiddens) # attn_output is shape (batch_size, context_len, hidden_size*2)
with vs.variable_scope("a1"):
attn_layer = BidirectionalAttnNew(self.keep_prob,
self.FLAGS.hidden_size * 2,
self.FLAGS.hidden_size * 2)
_, attn_output = attn_layer.build_graph(
question_hiddens, self.qn_mask, context_hiddens,
self.context_mask
) # attn_output is shape (batch_size, context_len, hidden_size*2)
# Concat attn_output to context_hiddens to get blended_reps
# blended_reps_c = tf.concat([context_hiddens, attn_output_val], axis=2) # (batch_size, context_len, hidden_size*4)
# blended_reps_q = tf.concat([question_hiddens, attn_output_key], axis=2)
with vs.variable_scope("e2_1c"):
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
context_hiddens_f = encoder.build_graph(
attn_output,
self.context_mask) # (batch_size, context_len, hidden_size*2)
# with vs.variable_scope("a2"):
# attn_layer1 = BidirectionalAttn(self.keep_prob, self.FLAGS.hidden_size * 2, self.FLAGS.hidden_size * 2)
# _, _, attn_output_val, attn_output_key = attn_layer1.build_graph(question_hiddens,
# self.qn_mask,
# context_hiddens,
# self.context_mask)
blended_reps_st = tf.concat([context_hiddens_f, attn_output], axis=2)
with vs.variable_scope("e3c"):
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
context_hiddens_f_end = encoder.build_graph(
context_hiddens_f, self.context_mask)
blended_reps_end = tf.concat([context_hiddens_f_end, attn_output],
axis=2)
# with vs.variable_scope("AnsPoiStRNN"):
# encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
# start_hidden = encoder.build_graph(blended_reps, self.context_mask)
# print "OK1"
# with vs.variable_scope("AnsPoiStATT"):
# attn_layer = BasicAttn(self.keep_prob, self.FLAGS.hidden_size * 2, self.FLAGS.hidden_size * 2)
# start_att_dis, start_att_out = attn_layer.build_graph(question_hiddens, self.qn_mask, start_hidden)
# print start_att_dis.shape, start_att_out.shape
# print "OK2"
# with vs.variable_scope("AnsPoiEnRNN"):
# encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
# end_hidden = encoder.build_graph(start_att_out, self.context_mask)
# print "OK3"
# with vs.variable_scope("AnsPoiStATT"):
# attn_layer = BasicAttn(self.keep_prob, self.FLAGS.hidden_size * 2, self.FLAGS.hidden_size * 2)
# end_att_dis, _ = attn_layer.build_graph(end_hidden, self.context_mask, question_hiddens)
# print "OK4"
# Apply fully connected layer to each blended representation
# Note, blended_reps_final corresponds to b' in the handout
# Note, tf.contrib.layers.fully_connected applies a ReLU non-linarity here by default
blended_reps_final_st = tf.contrib.layers.fully_connected(
blended_reps_st, num_outputs=self.FLAGS.hidden_size
) # blended_reps_final is shape (batch_size, context_len, hidden_size)
blended_reps_final_end = tf.contrib.layers.fully_connected(
blended_reps_end, num_outputs=self.FLAGS.hidden_size)
# print "###", blended_reps_final.shape
# print start_att_dis.shape, end_att_dis.shape
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with vs.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(
blended_reps_final_st, self.context_mask)
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with vs.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(
blended_reps_final_end, self.context_mask)
def build_graph(self):
"""Builds the main part of the graph for the model, starting from the input embeddings to the final distributions for the answer span.
Defines:
self.logits_start, self.logits_end: Both tensors shape (batch_size, context_len).
These are the logits (i.e. values that are fed into the softmax function) for the start and end distribution.
Important: these are -large in the pad locations. Necessary for when we feed into the cross entropy function.
self.probdist_start, self.probdist_end: Both shape (batch_size, context_len). Each row sums to 1.
These are the result of taking (masked) softmax of logits_start and logits_end.
"""
# Use a RNN to get hidden states for the context and the question
# Note: here the RNNEncoder is shared (i.e. the weights are the same)
# between the context and the question.
encoder = RNNEncoder(self.FLAGS.h_hidden_size,
self.keep_prob,
num_layers=self.FLAGS.h_num_layers,
combiner=self.FLAGS.h_combiner,
cell_type=self.FLAGS.h_cell_type)
if self.FLAGS.share_encoder:
question_hiddens, question_states_fw, question_states_bw = encoder.build_graph(
self.qn_embs,
self.qn_mask) # (batch_size, question_len, hidden_size*2)
else:
question_encoder = RNNEncoder(self.FLAGS.h_hidden_size,
self.keep_prob,
num_layers=self.FLAGS.h_num_layers,
combiner=self.FLAGS.h_combiner,
cell_type=self.FLAGS.h_cell_type,
scope='question_encoder')
question_hiddens, question_states_fw, question_states_bw = question_encoder.build_graph(
self.qn_embs,
self.qn_mask) # (batch_size, question_len, hidden_size*2)
if not self.FLAGS.reuse_question_states:
question_states_fw, question_states_bw = None, None
context_hiddens, _, _ = encoder.build_graph(
self.context_embs,
self.context_mask,
initial_states_fw=question_states_fw,
initial_states_bw=question_states_bw
) # (batch_size, context_len, hidden_size*2)
if self.FLAGS.use_bidaf:
attn_layer = BiDAF(self.keep_prob)
context_att, question_att = attn_layer.build_graph(
question_hiddens, self.qn_mask, context_hiddens,
self.context_mask)
blended_reps = tf.concat([
context_hiddens, context_att, context_hiddens * context_att,
context_hiddens * question_att
],
axis=2)
else:
# Use context hidden states to attend to question hidden states
attn_layer = BasicAttn(self.keep_prob)
_, attn_output = attn_layer.build_graph(
question_hiddens, self.qn_mask, context_hiddens
) # attn_output is shape (batch_size, context_len, hidden_size*2)
# Concat attn_output to context_hiddens to get blended_reps
blended_reps = tf.concat(
[context_hiddens, attn_output, context_hiddens * attn_output],
axis=2) # (batch_size, context_len, hidden_size*4)
if self.FLAGS.modeling_layer_uses_rnn:
modelling_encoder = RNNEncoder(
self.FLAGS.h_model_size,
self.keep_prob,
num_layers=self.FLAGS.h_model_layers,
combiner=self.FLAGS.h_combiner,
cell_type=self.FLAGS.h_cell_type,
scope='blended_reps_scope')
blended_reps_final, model_states_fw, model_states_bw = modelling_encoder.build_graph(
blended_reps, self.context_mask)
else:
# Apply fully connected layer to each blended representation
# Note, blended_reps_final corresponds to b' in the handout
# Note, tf.contrib.layers.fully_connected applies a ReLU non-linarity here by default
blended_reps_final = tf.contrib.layers.fully_connected(
blended_reps, num_outputs=self.FLAGS.h_hidden_size
) # blended_reps_final is shape (batch_size, context_len, hidden_size)
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with vs.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(
blended_reps_final, self.context_mask)
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with vs.variable_scope("EndDist"):
if self.FLAGS.use_rnn_for_ends:
end_encoder = RNNEncoder(self.FLAGS.h_model_size,
self.keep_prob,
num_layers=self.FLAGS.h_model_layers,
combiner=self.FLAGS.h_combiner,
cell_type=self.FLAGS.h_cell_type,
scope='blended_reps_final')
blended_reps_combined = tf.concat([
blended_reps_final,
tf.expand_dims(self.probdist_start, 2)
], 2)
blended_reps_final, _, _ = end_encoder.build_graph(
blended_reps_combined,
self.context_mask,
initial_states_fw=model_states_fw,
initial_states_bw=model_states_bw)
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(
blended_reps_final, self.context_mask)
def build_graph(self):
"""Builds the main part of the graph for the model, starting from the input embeddings to the final distributions for the answer span.
Defines:
self.logits_start, self.logits_end: Both tensors shape (batch_size, context_len).
These are the logits (i.e. values that are fed into the softmax function) for the start and end distribution.
Important: these are -large in the pad locations. Necessary for when we feed into the cross entropy function.
self.probdist_start, self.probdist_end: Both shape (batch_size, context_len). Each row sums to 1.
These are the result of taking (masked) softmax of logits_start and logits_end.
"""
# Use a RNN to get hidden states for the context and the question
# Note: here the RNNEncoder is shared (i.e. the weights are the same)
# between the context and the question.
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
context_embs = self.context_embs
qn_embs = self.qn_embs
if self.FLAGS.enable_cnn:
context_embs = tf.concat(
[self.context_embs, self.context_char_embs], axis=2)
qn_embs = tf.concat([self.qn_embs, self.qn_char_embs], axis=2)
context_hiddens = encoder.build_graph(
context_embs,
self.context_mask) # (batch_size, context_len, hidden_size*2)
question_hiddens = encoder.build_graph(
qn_embs, self.qn_mask) # (batch_size, question_len, hidden_size*2)
# Encode query-aware representations of the context words
bidaf_attn_layer = BidafAttn(self.keep_prob, self.FLAGS.context_len,
self.FLAGS.hidden_size * 2)
bidaf_out = bidaf_attn_layer.build_graph(
question_hiddens, self.qn_mask, context_hiddens,
self.context_mask) # (batch_size, context_len, hidden_size*8)
# Condense the information: hidden_size*8 --> hidden_size*2
bidaf_out = tf.contrib.layers.fully_connected(
bidaf_out,
num_outputs=self.FLAGS.hidden_size * 2,
normalizer_fn=tf.contrib.layers.batch_norm
) # (batch_size, context_len, hidden_size*2)
# Co-attention
co_attn_layer = CoAttnLite(self.keep_prob, self.FLAGS.hidden_size,
self.FLAGS.hidden_size * 2)
co_out = co_attn_layer.build_graph(
question_hiddens, self.qn_mask, context_hiddens,
self.context_mask) # (batch_size, context_len, hidden_size*2)
bico_out = tf.concat([bidaf_out, co_out],
2) # (batch_size, context_len, hidden_size*4)
# Capture interactions among context words conditioned on the query.
gru_layer1 = RNNEncoder(
self.FLAGS.hidden_size, self.keep_prob
) # params: (hidden_size*4 + hidden_size) * hidden_size * 2 * 3
model_reps1 = gru_layer1.build_graph(
bico_out, self.context_mask, variable_scope='ModelGRU1'
) # (batch_size, context_len, hidden_size*2)
gru_layer2 = RNNEncoder(
self.FLAGS.hidden_size, self.keep_prob
) # params: (2*hidden_size + hidden_size) * hidden_size * 2 * 3
model_reps2 = gru_layer2.build_graph(
model_reps1, self.context_mask, variable_scope='ModelGRU2'
) # (batch_size, context_len, hidden_size*2)
# Self Attention & GRU layer parallel to GRU layer2.
with tf.variable_scope('SelfAttnGRU'):
self_attn_layer = MulAttn(self.keep_prob,
self.FLAGS.hidden_size * 2,
self.FLAGS.hidden_size * 2)
se_attn = self_attn_layer.build_graph(
model_reps1, self.context_mask, model_reps1,
self.context_mask) # (batch_size, context_len, hidden_size*2)
se_gru_layer = RNNEncoder(
self.FLAGS.hidden_size, self.keep_prob
) # params: (2*hidden_size + hidden_size) * hidden_size * 2 * 3
se_out = se_gru_layer.build_graph(
se_attn, self.context_mask, variable_scope='SelfGRU'
) # (batch_size, context_len, hidden_size*2)
model_reps = tf.concat([model_reps2, se_out],
2) # (batch_size, context_len, hidden_size*4)
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with vs.variable_scope("StartDist"):
start_reps = tf.concat(
[bico_out, model_reps],
2) # (batch_size, context_len, hidden_size*10)
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(
start_reps, self.context_mask)
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with vs.variable_scope("EndDist"):
gru_end_layer = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
model_end_reps = gru_end_layer.build_graph(
model_reps, self.context_mask, variable_scope='EndGRU'
) # (batch_size, context_len, hidden_size*2)
end_reps = tf.concat(
[bico_out, model_end_reps],
2) # (batch_size, context_len, hidden_size*10)
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(
end_reps, self.context_mask)
for variable in tf.trainable_variables():
tf.summary.histogram(variable.name.replace(':', '/'), variable)
def build_graph(self):
"""Builds the main part of the graph for the model, starting from the input embeddings to the final distributions for the answer span.
Defines:
self.logits_start, self.logits_end: Both tensors shape (batch_size, context_len).
These are the logits (i.e. values that are fed into the softmax function) for the start and end distribution.
Important: these are -large in the pad locations. Necessary for when we feed into the cross entropy function.
self.probdist_start, self.probdist_end: Both shape (batch_size, context_len). Each row sums to 1.
These are the result of taking (masked) softmax of logits_start and logits_end.
"""
# Use a RNN to get hidden states for the context and the question
# Note: here the RNNEncoder is shared (i.e. the weights are the same)
# between the context and the question.
if self.FLAGS.model == "baseline" :
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
elif self.FLAGS.model == "bidaf" or self.FLAGS.model == "bidaf_dynamic" or self.FLAGS.model=="bidaf_self_attn" or self.FLAGS.model=="bidaf_dynamic_self_attn":
print("INSIDE the BIDAF model")
encoder = RNNEncoder_LSTM(self.FLAGS.hidden_size, self.keep_prob)
elif self.FLAGS.model == "coatt" or self.FLAGS.model == "coatt_dynamic" or self.FLAGS.model=="coatt_dynamic_self_attn":
encoder = LSTMEncoder(self.FLAGS.hidden_size, self.keep_prob)
if self.FLAGS.model != "coatt" and self.FLAGS.model != "coatt_dynamic" and self.FLAGS.model!="coatt_dynamic_self_attn":
context_hiddens = encoder.build_graph(self.context_embs, self.context_mask) # (batch_size, context_len, hidden_size*2)
question_hiddens = encoder.build_graph(self.qn_embs, self.qn_mask) # (batch_size, question_len, hidden_size*2)
# Attention model
# Use context hidden states to attend to question hidden states
if self.FLAGS.model == "baseline" :
attn_layer = BasicAttn(self.keep_prob, self.FLAGS.hidden_size * 2, self.FLAGS.hidden_size * 2)
_,attn_output = attn_layer.build_graph(question_hiddens, self.qn_mask, context_hiddens) # attn_output is shape (batch_size, context_len, hidden_size*2)
# Concat attn_output to context_hiddens to get blended_reps
blended_reps = tf.concat([context_hiddens, attn_output], axis=2) # (batch_size, context_len, hidden_size*4)
# Apply fully connected layer to each blended representation
# Note, blended_reps_final corresponds to b' in the handout
# Note, tf.contrib.layers.fully_connected applies a ReLU non-linarity here by default
blended_reps_final = tf.contrib.layers.fully_connected(blended_reps, num_outputs=self.FLAGS.hidden_size) # blended_reps_final is shape (batch_size, context_len, hidden_size)
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with vs.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(blended_reps_final,self.context_mask)
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with vs.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(blended_reps_final,self.context_mask)
# Attention model
# Use context hidden states to attend to question hidden states
if self.FLAGS.model == "coatt" :
#context_hiddens = encoder.build_graph(self.context_embs, self.context_mask, "context") # (batch_size, context_len, hidden_size*2)
#question_hiddens = encoder.build_graph(self.qn_embs, self.qn_mask, "question") # (batch_size, question_len, hidden_size*2)
context_hiddens, question_hiddens = encoder.build_graph1(self.context_embs, self.qn_embs, self.context_mask, self.qn_mask)
attn_layer = CoAttention(self.keep_prob, self.FLAGS.hidden_size*2, self.FLAGS.hidden_size*2)
attn_output = attn_layer.build_graph(question_hiddens, self.qn_mask, context_hiddens, self.context_mask)
blended_reps_final = attn_output
#blended_reps = tf.concat([context_hiddens, attn_output], axis=2)
# Apply fully connected layer to each blended representation
# Note, blended_reps_final corresponds to b' in the handout
# Note, tf.contrib.layers.fully_connected applies a ReLU non-linarity here by default
#blended_reps_final = tf.contrib.layers.fully_connected(blended_reps, num_outputs=self.FLAGS.hidden_size) # blended_reps_final is shape (batch_size, context_len, hidden_size)
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with vs.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(blended_reps_final,self.context_mask)
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with vs.variable_scope("EndDist"):
contextLen = tf.reduce_sum(self.context_mask, axis=1)
cell = tf.contrib.rnn.LSTMBlockCell(2 * self.FLAGS.hidden_size)
(fw_out, bw_out), _ = tf.nn.bidirectional_dynamic_rnn(cell, cell, attn_output, contextLen, dtype = tf.float32)
U_1 = tf.concat([fw_out, bw_out], axis=2)
out = tf.nn.dropout(U_1, self.keep_prob)
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(out,self.context_mask)
elif self.FLAGS.model =="bidaf" or self.FLAGS.model=="bidaf_self_attn":
attn_layer = BiDafAttn(self.keep_prob, self.FLAGS.hidden_size*2, self.FLAGS.hidden_size*2)
attn_output_tmp = attn_layer.build_graph(question_hiddens, self.qn_mask, context_hiddens, self.context_mask) # attn_output is shape (batch_size, context_len, hidden_size*8)
# Set of vectors which produces a set of query aware feature vectors for each word in the context
#blended_reps = attn_output #(batch_size, num_keys, 4*value_vec_size)
if self.FLAGS.model == "bidaf_self_attn":
self_attn_layer = SelfAttn(self.keep_prob, self.FLAGS.hidden_size * 8, self.FLAGS.hidden_size * 8)
_,self_attn_output = self_attn_layer.build_graph(attn_output_tmp, self.context_mask) #(batch_size, conetx_len, 8*hidden_size)
attn_output = tf.concat([attn_output_tmp, self_attn_output], axis=2) #(batch_size, context_len, 16*hidden_size)
else:
attn_output = attn_output_tmp
# In BIDAF the attention output is feed to a modeling layer
# The Modeling layer is a 2 layer lstm
mod_layer = MODEL_LAYER_BIDAF(self.FLAGS.hidden_size, self.keep_prob)
mod_layer_out = mod_layer.build_graph(attn_output, self.context_mask) # (batch_size, context_len, hidden_size*2)
blended_reps_start = tf.concat([attn_output,mod_layer_out], axis=2) # (batch_size, context_len, hidden_size*10)
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with vs.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(blended_reps_start, self.context_mask)
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with vs.variable_scope("EndDist"):
# Concatenate the start logits with the modelling layer output to get the input to the
# end word lstm
#self.logits_start has a shape of #(batch_size, context_len)
logits_start_expand = tf.expand_dims(self.logits_start, axis=2) #(batch_size, context_len, 1)
end_lstm_input = tf.concat([logits_start_expand, mod_layer_out], axis=2) #(batch_size, context_len, 1 + hidden_size*2)
# LSTM
end_layer = END_WORD_LAYER(self.FLAGS.hidden_size, self.keep_prob)
blended_reps_end = end_layer.build_graph(end_lstm_input, self.context_mask)
blended_reps_end_final = tf.concat([attn_output, blended_reps_end], axis=2)
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(blended_reps_end_final, self.context_mask)
elif self.FLAGS.model =="bidaf_dynamic" or self.FLAGS.model =="bidaf_dynamic_self_attn":
attn_layer = BiDafAttn(self.keep_prob, self.FLAGS.hidden_size*2, self.FLAGS.hidden_size*2)
attn_output_tmp = attn_layer.build_graph(question_hiddens, self.qn_mask, context_hiddens, self.context_mask) # attn_output is shape (batch_size, context_len, hidden_size*8)
if self.FLAGS.model == "bidaf_dynamic_self_attn":
self_attn_layer = SelfAttn(self.keep_prob, self.FLAGS.hidden_size * 8, self.FLAGS.hidden_size * 8)
_,self_attn_output = self_attn_layer.build_graph(attn_output_tmp,self.context_mask) # (batch_size, conetx_len, 8*hidden_size)
attn_output = tf.concat([attn_output_tmp, self_attn_output], axis=2) #(batch_size, context_len, 16*hidden_size)
else:
attn_output = attn_output_tmp
# Set of vectors which produces a set of query aware feature vectors for each word in the context
#blended_reps = attn_output #(batch_size, num_keys, 4*value_vec_size)
# In BIDAF the attention output is feed to a modeling layer
# The Modeling layer is a 2 layer lstm
mod_layer = MODEL_LAYER_BIDAF(self.FLAGS.hidden_size, self.keep_prob)
mod_layer_out = mod_layer.build_graph(attn_output, self.context_mask) # (batch_size, context_len, hidden_size*2)
blended_reps_start = tf.concat([attn_output,mod_layer_out], axis=2) # (batch_size, context_len, hidden_size*10)
# We now feed this to dynamic decoder module coded in Answer decoder
# the output of the decoder are start, end, alpha_logits and beta_logits
# start and end have a shape of (batch_size, num_iterations)
#alpha_logits and beta_logits have a shape of (batch_size, num_iterations, inpit_dim)
decoder = ANSWER_DECODER(self.FLAGS.hidden_size, self.keep_prob, self.FLAGS.num_iterations, self.FLAGS.max_pool, self.FLAGS.batch_size)
u_s_init = mod_layer_out[:,0,:]
u_e_init = mod_layer_out[:,0,:]
start_location, end_location, alpha_logits, beta_logits = decoder.build_graph(mod_layer_out, self.context_mask, u_s_init, u_e_init)
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with vs.variable_scope("StartDist"):
#softmax_layer_start = SimpleSoftmaxLayer()
logits_start_tmp = [masked_softmax(logits, self.context_mask,1) for logits in alpha_logits]
self.alpha_logits , alpha_logits_probs = zip(*logits_start_tmp)
self.logits_start, self.probdist_start = self.alpha_logits[self.FLAGS.num_iterations -1], alpha_logits_probs[self.FLAGS.num_iterations -1]
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with vs.variable_scope("EndDist"):
logits_end_tmp = [masked_softmax(logits, self.context_mask,1) for logits in beta_logits]
self.beta_logits , beta_logits_probs = zip(*logits_end_tmp)
self.logits_end, self.probdist_end = self.beta_logits[self.FLAGS.num_iterations -1], beta_logits_probs[self.FLAGS.num_iterations -1]
elif self.FLAGS.model =="coatt_dynamic" or self.FLAGS.model == "coatt_dynamic_self_attn":
context_hiddens, question_hiddens = encoder.build_graph1(self.context_embs, self.qn_embs, self.context_mask, self.qn_mask)
attn_layer = CoAttention(self.keep_prob, self.FLAGS.hidden_size*2, self.FLAGS.hidden_size*2)
if self.FLAGS.model == "coatt_dynamic_self_attn":
CoATT = attn_layer.build_graph1(question_hiddens, self.qn_mask, context_hiddens, self.context_mask)
self_attn_layer = SelfAttn(self.keep_prob, self.FLAGS.hidden_size * 8, self.FLAGS.hidden_size * 8)
_, self_attn_output = self_attn_layer.build_graph(CoATT, self.context_mask) # (batch_size, conetx_len, 8*hidden_size)
attn_output = tf.concat([CoATT, self_attn_output], axis=2) #(batch_size, context_len, 16*hidden_size)
else:
U = attn_layer.build_graph(question_hiddens, self.qn_mask, context_hiddens, self.context_mask)
attn_output = U
#blended_reps = tf.concat([context_hiddens, attn_output], axis=2)
# Apply fully connected layer to each blended representation
# Note, blended_reps_final corresponds to b' in the handout
# Note, tf.contrib.layers.fully_connected applies a ReLU non-linarity here by default
decoder = ANSWER_DECODER(self.FLAGS.hidden_size, self.keep_prob, self.FLAGS.num_iterations, self.FLAGS.max_pool, self.FLAGS.batch_size)
u_s_init = attn_output[:,0,:]
u_e_init = attn_output[:,0,:]
start_location, end_location, alpha_logits, beta_logits = decoder.build_graph(attn_output, self.context_mask, u_s_init, u_e_init)
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with vs.variable_scope("StartDist"):
#softmax_layer_start = SimpleSoftmaxLayer()
logits_start_tmp = [masked_softmax(logits, self.context_mask,1) for logits in alpha_logits]
self.alpha_logits , alpha_logits_probs = zip(*logits_start_tmp)
self.logits_start, self.probdist_start = self.alpha_logits[self.FLAGS.num_iterations -1], alpha_logits_probs[self.FLAGS.num_iterations -1]
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with vs.variable_scope("EndDist"):
logits_end_tmp = [masked_softmax(logits, self.context_mask,1) for logits in beta_logits]
self.beta_logits , beta_logits_probs = zip(*logits_end_tmp)
self.logits_end, self.probdist_end = self.beta_logits[self.FLAGS.num_iterations -1], beta_logits_probs[self.FLAGS.num_iterations -1]
def build_graph(self):
"""Builds the main part of the graph for the model, starting from the input embeddings to the final distributions for the answer span.
Defines:
self.logits_start, self.logits_end: Both tensors shape (batch_size, context_len).
These are the logits (i.e. values that are fed into the softmax function) for the start and end distribution.
Important: these are -large in the pad locations. Necessary for when we feed into the cross entropy function.
self.probdist_start, self.probdist_end: Both shape (batch_size, context_len). Each row sums to 1.
These are the result of taking (masked) softmax of logits_start and logits_end.
"""
print "Running Attention Model with... %s" % self.FLAGS.attention
if self.FLAGS.attention == "BiDAF":
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
context_hiddens = encoder.build_graph(
self.context_embs,
self.context_mask) # (batch_size, context_len, hidden_size*2)
question_hiddens = encoder.build_graph(
self.qn_embs,
self.qn_mask) # (batch_size, question_len, hidden_size*2)
bidaf_attn_layer = BiDirectionalAttn(self.keep_prob,
self.FLAGS.hidden_size * 2,
self.FLAGS.hidden_size * 2,
self.FLAGS.question_len,
self.FLAGS.context_len)
_, context_to_question, _, question_to_context = bidaf_attn_layer.build_graph(
question_hiddens, self.qn_mask, context_hiddens,
self.context_mask)
# Combine attention vectors and hidden context vector
context_c2q = tf.multiply(context_hiddens, context_to_question)
context_q2c = tf.multiply(context_hiddens, question_to_context)
blended_reps = tf.concat(
[
context_hiddens, context_to_question, context_c2q,
context_q2c
],
axis=2) # (batch_size, context_len, hidden_size*8)
# Modeling Layers (2 layers of bidirectional LSTM) encodes the query-aware representations of context words.
modeling_layer = BiRNN(self.FLAGS.hidden_size, self.keep_prob)
blended_reps_1 = modeling_layer.build_graph(
blended_reps,
self.context_mask) # (batch_size, context_len, hidden_size*2).
modeling_layer_2 = BiRNN2(self.FLAGS.hidden_size, self.keep_prob)
blended_reps_final = modeling_layer_2.build_graph(
blended_reps_1,
self.context_mask) # (batch_size, context_len, hidden_size*2).
else: # Default: self.FLAGS.attention == "BasicAttn"
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
context_hiddens = encoder.build_graph(
self.context_embs,
self.context_mask) # (batch_size, context_len, hidden_size*2)
question_hiddens = encoder.build_graph(
self.qn_embs,
self.qn_mask) # (batch_size, question_len, hidden_size*2)
# Use context hidden states to attend to question hidden states
attn_layer = BasicAttn(self.keep_prob, self.FLAGS.hidden_size * 2,
self.FLAGS.hidden_size * 2)
_, attn_output = attn_layer.build_graph(
question_hiddens, self.qn_mask, context_hiddens
) # attn_output is shape (batch_size, context_len, hidden_size*2)
# Concat attn_output to context_hiddens to get blended_reps
blended_reps = tf.concat(
[context_hiddens, attn_output],
axis=2) # (batch_size, context_len, hidden_size*4)
# Apply fully connected layer to each blended representation
# Note, blended_reps_final corresponds to b' in the handout
# Note, tf.contrib.layers.fully_connected applies a ReLU non-linarity here by default
blended_reps_final = tf.contrib.layers.fully_connected(
blended_reps, num_outputs=self.FLAGS.hidden_size
) # blended_reps_final is shape (batch_size, context_len, hidden_size)
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with vs.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(
blended_reps_final, self.context_mask)
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with vs.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(
blended_reps_final, self.context_mask)
def build_graph(self):
"""Builds the main part of the graph for the model, starting from the input embeddings to the final distributions for the answer span.
Defines:
self.logits_start, self.logits_end: Both tensors shape (batch_size, context_len).
These are the logits (i.e. values that are fed into the softmax function) for the start and end distribution.
Important: these are -large in the pad locations. Necessary for when we feed into the cross entropy function.
self.probdist_start, self.probdist_end: Both shape (batch_size, context_len). Each row sums to 1.
These are the result of taking (masked) softmax of logits_start and logits_end.
"""
# character-level CNN to get hybrid word embeddings
charCnn = CharCNN(self.FLAGS.word_len, self.FLAGS.char_embedding_size,
self.FLAGS.num_filters, self.FLAGS.kernel_size)
# (batch_size, context_len, num_filters)
char_context_hiddens = charCnn.build_graph(self.char_context_embs,
self.char_context_mask,
self.FLAGS.context_len)
# (batch_size, question_len, num_filters)
char_qn_hiddens = charCnn.build_graph(self.char_qn_embs,
self.char_qn_mask,
self.FLAGS.question_len)
# hybrid word embeddings
hybrid_context_embs = tf.concat(
[self.context_embs, char_context_hiddens],
axis=-1) # (batch_size, context_len, emb_size+char_emb_size)
hybrid_qn_embs = tf.concat(
[self.qn_embs, char_qn_hiddens],
axis=-1) # (batch_size, question_len, emb_size+char_emb_size)
# Use a RNN to get hidden states for the context and the question
# Note: here the RNNEncoder is shared (i.e. the weights are the same)
# between the context and the question.
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob, "GRU")
context_hiddens = encoder.build_graph(
hybrid_context_embs,
self.context_mask) # (batch_size, context_len, hidden_size*2)
question_hiddens = encoder.build_graph(
hybrid_qn_embs,
self.qn_mask) # (batch_size, question_len, hidden_size*2)
# coattention has been the best attention model I've found
attn_layer = CoAttn(self.keep_prob, self.FLAGS.hidden_size * 2,
self.FLAGS.hidden_size * 2)
u = attn_layer.build_graph(
question_hiddens, self.qn_mask,
context_hiddens) # shape (batch_size, context_len, 8*hidden_size)
# Apply fully connected layer to each blended representation
# Note, blended_reps_final corresponds to b' in the handout
# Note, tf.contrib.layers.fully_connected applies a ReLU non-linarity here by default
blended_reps_final = tf.contrib.layers.fully_connected(
u, num_outputs=self.FLAGS.hidden_size
) # blended_reps_final is shape (batch_size, context_len, hidden_size)
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with vs.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(
blended_reps_final, self.context_mask)
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with vs.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(
blended_reps_final, self.context_mask)
def build_graph(self):
"""Builds the main part of the graph for the model, starting from the input embeddings to the final distributions for the answer span.
Defines:
self.logits_start, self.logits_end: Both tensors shape (batch_size, context_len).
These are the logits (i.e. values that are fed into the softmax function) for the start and end distribution.
Important: these are -large in the pad locations. Necessary for when we feed into the cross entropy function.
self.probdist_start, self.probdist_end: Both shape (batch_size, context_len). Each row sums to 1.
These are the result of taking (masked) softmax of logits_start and logits_end.
"""
# Use a RNN to get hidden states for the context and the question
# Note: here the RNNEncoder is shared (i.e. the weights are the same)
# between the context and the question.
if self.FLAGS.cell_type in ['rnn_gru', 'rnn_lstm']:
encoder = RNNEncoder(self.FLAGS.hidden_size,
self.keep_prob,
cell_type=self.FLAGS.cell_type)
context_hiddens = encoder.build_graph(
self.context_embs,
self.context_mask) # (batch_size, context_len, hidden_size*2)
question_hiddens = encoder.build_graph(
self.qn_embs,
self.qn_mask) # (batch_size, question_len, hidden_size*2)
elif self.FLAGS.cell_type == 'qanet':
encoder = QAEncoder(num_blocks=self.FLAGS.emb_num_blocks, num_layers=self.FLAGS.emb_num_layers, \
num_heads=self.FLAGS.emb_num_heads, \
filters=self.FLAGS.hidden_size, kernel_size=self.FLAGS.emb_kernel_size, \
keep_prob=self.keep_prob, input_mapping=True)
context_hiddens = encoder.build_graph(self.context_embs,
self.context_mask)
question_hiddens = encoder.build_graph(self.qn_embs, self.qn_mask)
if self.FLAGS.attention == 'basic':
# Use context hidden states to attend to question hidden states
attn_layer = BasicAttn(self.keep_prob, self.FLAGS.hidden_size * 2,
self.FLAGS.hidden_size * 2)
_, attn_output = attn_layer.build_graph(
question_hiddens, self.qn_mask, context_hiddens
) # attn_output is shape (batch_size, context_len, hidden_size*2)
# Concat attn_output to context_hiddens to get blended_reps
blended_reps = tf.concat(
[context_hiddens, attn_output],
axis=2) # (batch_size, context_len, hidden_size*4)
elif self.FLAGS.attention == 'bidaf':
attn_layer = BiDAFAttn(self.keep_prob)
blended_reps = attn_layer.build_graph(context_hiddens,
self.context_mask,
question_hiddens,
self.qn_mask)
if self.FLAGS.modeling_layer == 'basic':
# Apply fully connected layer to each blended representation
# Note, blended_reps_final corresponds to b' in the handout
# Note, tf.contrib.layers.fully_connected applies a ReLU non-linarity here by default
blended_reps_final = tf.contrib.layers.fully_connected(
blended_reps,
num_outputs=self.FLAGS.hidden_size,
weights_initializer=initializer_relu()
) # blended_reps_final is shape (batch_size, context_len, hidden_size)
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with tf.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(
blended_reps_final, self.context_mask)
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with tf.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(
blended_reps_final, self.context_mask)
elif self.FLAGS.modeling_layer == 'rnn':
encoder_start = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob, \
cell_type=self.FLAGS.cell_type, name='m1')
m1 = encoder_start.build_graph(blended_reps, self.context_mask)
encoder_end = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob, \
cell_type=self.FLAGS.cell_type, name='m2')
m2 = encoder_end.build_graph(m1, self.context_mask)
with tf.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(
tf.concat([blended_reps, m1], -1), self.context_mask)
with tf.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(
tf.concat([blended_reps, m2], -1), self.context_mask)
elif self.FLAGS.modeling_layer == 'qanet':
modeling_encoder = QAEncoder(num_blocks=self.FLAGS.model_num_blocks, \
num_layers=self.FLAGS.model_num_layers, \
num_heads=self.FLAGS.model_num_heads, \
filters=self.FLAGS.hidden_size, \
kernel_size=self.FLAGS.model_kernel_size, \
keep_prob=self.keep_prob, input_mapping=False, \
name='modeling_encoder')
m0 = tf.layers.conv1d(blended_reps, filters=self.FLAGS.hidden_size, \
kernel_size=1, padding='SAME', name='attn_mapping')
m1 = modeling_encoder.build_graph(m0, self.context_mask)
m2 = modeling_encoder.build_graph(m1, self.context_mask)
m3 = modeling_encoder.build_graph(m2, self.context_mask)
with tf.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(
tf.concat([m1, m2], -1), self.context_mask)
with tf.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(
tf.concat([m1, m3], -1), self.context_mask)
elif self.FLAGS.modeling_layer == 'qanet2':
modeling_encoder1 = QAEncoder(num_blocks=self.FLAGS.model_num_blocks, \
num_layers=self.FLAGS.model_num_layers, \
num_heads=self.FLAGS.model_num_heads, \
filters=self.FLAGS.hidden_size, \
kernel_size=self.FLAGS.model_kernel_size, \
keep_prob=self.keep_prob, input_mapping=False, \
name='modeling_encoder1')
'''
modeling_encoder2 = QAEncoder(num_blocks=self.FLAGS.model_num_blocks, \
num_layers=self.FLAGS.model_num_layers, \
num_heads=self.FLAGS.model_num_heads, \
filters=self.FLAGS.hidden_size, \
kernel_size=self.FLAGS.model_kernel_size, \
keep_prob=self.keep_prob, input_mapping=False, \
name='modeling_encoder2')
'''
m0 = tf.layers.conv1d(blended_reps, filters=self.FLAGS.hidden_size, \
kernel_size=1, padding='SAME', name='attn_mapping')
m1 = modeling_encoder1.build_graph(m0, self.context_mask)
m2 = modeling_encoder1.build_graph(m1, self.context_mask)
with tf.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(
tf.concat([m0, m1], -1), self.context_mask)
with tf.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(
tf.concat([m0, m2], -1), self.context_mask)
def build_graph(self):
"""
Builds the main part of the graph for the model
Defines:
self.logits_start, self.logits_end: Both tensors shape (batch_size, context_len).
These are the logits (i.e. values that are fed into the softmax function) for the start and end distribution.
Important: these are -large in the pad locations. Necessary for when we feed into the cross entropy function.
self.probdist_start, self.probdist_end: Both shape (batch_size, context_len). Each row sums to 1.
These are the result of taking (masked) softmax of logits_start and logits_end.
"""
# NOTE CHANGE: concantanate glove and elmo embedding
# How to handle elmo context_len and glove context_len mismatch?
# Just make the context_ids no max context_len
context_embs_concat = tf.concat(
[self.elmo_context_input, self.context_embs],
2) #(batch_size, qn_len, 1024+self.FLAGS.embedding_size)
qn_embs_concat = tf.concat(
[self.elmo_question_input, self.qn_embs],
2) #(batch_size, qn_len, 1024+self.FLAGS.embedding_size)
#set shape so that it can pass to dynamic lstm
context_embs_concat.set_shape(
(None, None, 1024 + self.FLAGS.embedding_size))
qn_embs_concat.set_shape(
(None, None, 1024 + self.FLAGS.embedding_size))
self.qn_mask.set_shape((None, None))
self.context_mask.set_shape((None, None))
with tf.variable_scope("biLSTM"):
Encoder = RNNEncoder(self.FLAGS.hidden_size,
keep_prob=self.keep_prob,
cell_type="lstm",
input_size=1024 + self.FLAGS.embedding_size)
#shared weights (same scope)
context_hiddens = Encoder.build_graph(
context_embs_concat,
self.context_mask,
scope="context_question_encoder"
) #(batch_size, context_len, hidden_size*2)
question_hiddens = Encoder.build_graph(
qn_embs_concat, self.qn_mask, scope="context_question_encoder"
) #(batch_size, question_len, hidden_size*2)
with tf.variable_scope("bidaf"):
bidaf_object = Bidaf(self.FLAGS.hidden_size * 2, self.keep_prob)
b = bidaf_object.build_graph(
context_hiddens, question_hiddens, self.context_mask,
self.qn_mask) #(batch_size, context_len, hidden_size*8)
with tf.variable_scope("self_attn_layer"):
SelfAttn_object = SelfAttn(self.FLAGS.hidden_size,
self.FLAGS.hidden_size * 2,
self.keep_prob,
input_size=self.FLAGS.hidden_size * 2)
M = SelfAttn_object.build_graph(
b, self.context_mask,
cell_type="lstm") #(batch_size, context_len, hidden_size*2)
#Make prediction
with tf.variable_scope('prediction_layer'):
#Encode the self-attended context first
with tf.variable_scope("final_lstm_layer"):
final_lstm_object = RNNEncoder(
self.FLAGS.hidden_size,
keep_prob=self.keep_prob,
cell_type="lstm",
input_size=self.FLAGS.hidden_size * 2)
M_prime = final_lstm_object.build_graph(
M, self.context_mask,
scope="final_lstm") #(batch_size, context_len, h*2)
#Get start distribution
with tf.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(
M_prime,
self.context_mask) #both are (batch_size, context_len)
with tf.variable_scope("EndDist"):
logit_start_expand = tf.expand_dims(
self.logits_start, axis=2) #(batch_size, context_len, 1)
blended_end_rnn_input = tf.concat(
[logit_start_expand, M_prime],
axis=2) #(batch_size, context_len, hidden_size*2)
end_dist_rnn = RNNEncoder(self.FLAGS.hidden_size,
keep_prob=self.keep_prob,
direction="unidirectional")
end_rnn_output = end_dist_rnn.build_graph(
blended_end_rnn_input,
self.context_mask,
scope="end_dist_rnn")
# Get the end dist
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(
end_rnn_output, self.context_mask)
def build_graph(self):
"""Builds the main part of the graph for the model, starting from the input embeddings to the final distributions for the answer span.
Defines:
self.logits_start, self.logits_end: Both tensors shape (batch_size, context_len).
These are the logits (i.e. values that are fed into the softmax function) for the start and end distribution.
Important: these are -large in the pad locations. Necessary for when we feed into the cross entropy function.
self.probdist_start, self.probdist_end: Both shape (batch_size, context_len). Each row sums to 1.
These are the result of taking (masked) softmax of logits_start and logits_end.
"""
# ***********************
# *** Highway network ***
# ***********************
highway_net = HighWayNetwork()
self.context_embs = highway_net.build_graph(self.context_embs)
self.qn_embs = highway_net.build_graph(self.qn_embs)
# **********************************
# *** Contextual Embedding layer ***
# **********************************
# Use a biLSTM to get hidden states for the context and the question
# Note: here the biLSTMEncoder is shared (i.e. the weights are the same) between the context and the question.
# biLSTM encoding utilizes contextual clues from surrounding words to refine the embeddings.
encoder = biLSTMEncoder(self.FLAGS.hidden_size, self.keep_prob)
context_hiddens = encoder.build_graph(self.context_embs, self.context_mask) # (batch_size, context_len, hidden_size*2)
question_hiddens = encoder.build_graph(self.qn_embs, self.qn_mask) # (batch_size, question_len, hidden_size*2)
# ****************************
# *** Attention Flow layer ***
# ****************************
# Couples query and context vectors and produces a set of query-aware feature vectors for ea. word in the document
attn_layer = BiDAFAttn(self.keep_prob, self.FLAGS.hidden_size*2, self.FLAGS.hidden_size*2)
_, c2q_attn_output, _, q2c_attn_output = attn_layer.build_graph(question_hiddens, self.qn_mask,
context_hiddens, self.context_mask)
# c2q_attn_output is shape (batch_size, context_len, 2h), q2c_attn_output is (batch_size, 1, 2h)
# Concat attn_output to context_hiddens to get blended_reps
blended_reps = tf.concat([context_hiddens, c2q_attn_output, tf.multiply(context_hiddens, c2q_attn_output),
tf.multiply(context_hiddens, q2c_attn_output)], axis=2,
name="blended_reps") # (batch_size, context_len, hidden_size*8)
# **********************
# *** Modeling layer ***
# **********************
# Scans the context
Modeling_layer = Modeling_layer_biLSTM_Encoder(self.FLAGS.hidden_size, self.keep_prob)
blended_reps_final = Modeling_layer.build_graph(blended_reps)
# ********************
# *** Output layer ***
# ********************
# Provide an answer to the query
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with vs.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(tf.concat([blended_reps, blended_reps_final], 2),
self.context_mask)
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
encoder_out = Output_layer_biLSTM_Encoder(self.FLAGS.hidden_size, self.keep_prob)
blended_reps_final_hiddens = encoder_out.build_graph(blended_reps_final) # (batch_size, context_len, hidden_size*2)
with vs.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(tf.concat([blended_reps, blended_reps_final_hiddens], 2),
self.context_mask)
def build_graph(self):
"""Builds the main part of the graph for the model, starting from the input embeddings to the final distributions for the answer span.
Defines:
self.logits_start, self.logits_end: Both tensors shape (batch_size, context_len).
These are the logits (i.e. values that are fed into the softmax function) for the start and end distribution.
Important: these are -large in the pad locations. Necessary for when we feed into the cross entropy function.
self.probdist_start, self.probdist_end: Both shape (batch_size, context_len). Each row sums to 1.
These are the result of taking (masked) softmax of logits_start and logits_end.
"""
if self.FLAGS.use_char_cnn:
with vs.variable_scope('char_encoding'):
self.context_char_encodings, self.cnn_filters1 = char_encoder2(
self.context_char_embs, self.FLAGS.context_len,
self.FLAGS.word_len, self.FLAGS.cnn_filter_width,
self.FLAGS.char_embedding_size, self.FLAGS.n_cnn_filters)
#self.context_char_encodings = tf.nn.dropout(self.context_char_encodings, self.keep_prob)
tf.get_variable_scope().reuse_variables()
self.qn_char_encodings, self.cnn_filters2 = char_encoder2(
self.qn_char_embs, self.FLAGS.question_len,
self.FLAGS.word_len, self.FLAGS.cnn_filter_width,
self.FLAGS.char_embedding_size, self.FLAGS.n_cnn_filters)
#self.qn_char_encodings = tf.nn.dropout(self.qn_char_encodings, self.keep_prob)
joined_context_embs = tf.concat(
[self.context_embs, self.context_char_encodings], axis=2)
joined_qn_embs = tf.concat([self.qn_embs, self.qn_char_encodings],
axis=2)
assert joined_context_embs.shape[
2] == self.FLAGS.embedding_size + self.FLAGS.n_cnn_filters
assert joined_qn_embs.shape[
2] == self.FLAGS.embedding_size + self.FLAGS.n_cnn_filters
else:
joined_context_embs = self.context_embs
joined_qn_embs = self.qn_embs
# Use a RNN to get hidden states for the context and the question
# Note: here the RNNEncoder is shared (i.e. the weights are the same)
# between the context and the question.
with vs.variable_scope('embedding_layer'):
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
context_hiddens = encoder.build_graph(
joined_context_embs,
self.context_mask) # (batch_size, context_len, hidden_size*2)
question_hiddens = encoder.build_graph(
joined_qn_embs,
self.qn_mask) # (batch_size, question_len, hidden_size*2)
attn_layer = BDAttn(self.keep_prob, self.FLAGS.hidden_size * 2,
self.FLAGS.hidden_size * 2)
attn_output = attn_layer.build_graph(
question_hiddens,
self.qn_mask,
context_hiddens,
self.context_mask,
q2c=self.FLAGS.use_q2c_attention
) # attn_output is shape (batch_size, context_len, hidden_size*6)
blended_reps = tf.concat(
[context_hiddens, attn_output],
axis=2) # (batch_size, context_len, hidden_size*8)
# Use context hidden states to attend to question hidden states
#attn_layer = BasicAttn(self.keep_prob, self.FLAGS.hidden_size*2, self.FLAGS.hidden_size*2)
#_, attn_output = attn_layer.build_graph(question_hiddens, self.qn_mask, context_hiddens) # attn_output is shape (batch_size, context_len, hidden_size*2)
with vs.variable_scope('layer1'):
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
layer1_reps = encoder.build_graph(blended_reps, self.context_mask)
with vs.variable_scope('layer2'):
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
layer2_reps = encoder.build_graph(layer1_reps, self.context_mask)
# Apply fully connected layer to each blended representation
# Note, blended_reps_final corresponds to b' in the handout
# Note, tf.contrib.layers.fully_connected applies a ReLU non-linarity here by default
final_reps = tf.contrib.layers.fully_connected(
layer2_reps, num_outputs=self.FLAGS.hidden_size
) # blended_reps_final is shape (batch_size, context_len, hidden_size)
#final_reps = layer2_reps
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with vs.variable_scope("StartDist"):
softmax_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_start.build_graph(
final_reps, self.context_mask)
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with vs.variable_scope("EndDist"):
#encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
#end_hiddens = encoder.build_graph(final_reps, self.context_mask) # (batch_size, context_len, hidden_size*2)
softmax_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_end.build_graph(
final_reps, self.context_mask)
def build_graph(self):
"""Builds the main part of the graph for the model, starting from the input embeddings to the final distributions for the answer span.
Defines:
self.logits_start, self.logits_end: Both tensors shape (batch_size, context_len).
These are the logits (i.e. values that are fed into the softmax function) for the start and end distribution.
Important: these are -large in the pad locations. Necessary for when we feed into the cross entropy function.
self.probdist_start, self.probdist_end: Both shape (batch_size, context_len). Each row sums to 1.
These are the result of taking (masked) softmax of logits_start and logits_end.
"""
# Use a RNN to get hidden states for the context and the question
# Note: here the RNNEncoder is shared (i.e. the weights are the same)
# between the context and the question.
context_input_lens = tf.reshape(
tf.reduce_sum(tf.cast(tf.cast(self.context_char_ids, tf.bool),
tf.int32),
axis=2), [-1])
qn_input_lens = tf.reshape(
tf.reduce_sum(tf.cast(tf.cast(self.qn_char_ids, tf.bool),
tf.int32),
axis=2), [-1])
cell_fw = rnn_cell.GRUCell(self.FLAGS.hidden_size)
cell_bw = rnn_cell.GRUCell(self.FLAGS.hidden_size)
_, (state_fw,
state_bw) = tf.nn.bidirectional_dynamic_rnn(cell_fw,
cell_bw,
self.context_char_embs,
context_input_lens,
dtype=tf.float32)
ch_emb = tf.reshape(
tf.concat([state_fw, state_bw], axis=1),
[-1, self.FLAGS.context_len, 2 * self.FLAGS.hidden_size])
self.context_embs = tf.concat([self.context_embs, ch_emb], axis=2)
_, (state_fw,
state_bw) = tf.nn.bidirectional_dynamic_rnn(cell_fw,
cell_bw,
self.qn_char_embs,
qn_input_lens,
dtype=tf.float32)
qh_emb = tf.reshape(
tf.concat([state_fw, state_bw], axis=1),
[-1, self.FLAGS.question_len, 2 * self.FLAGS.hidden_size])
self.qn_embs = tf.concat([self.qn_embs, qh_emb], axis=2)
# ToDo Deep encoder
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
context_hiddens = encoder.build_graph(
self.context_embs,
self.context_mask) # (batch_size, context_len, hidden_size*2)
question_hiddens = encoder.build_graph(
self.qn_embs,
self.qn_mask) # (batch_size, question_len, hidden_size*2)
# Use context hidden states to attend to question hidden states
attn_layer = BasicAttn(self.keep_prob, self.FLAGS.hidden_size * 2,
self.FLAGS.hidden_size * 2)
_, attn_output = attn_layer.build_graph(
question_hiddens, self.qn_mask, context_hiddens
) # attn_output is shape (batch_size, context_len, hidden_size*2)
# Concat attn_output to context_hiddens to get blended_reps
blended_reps = tf.concat(
[context_hiddens, attn_output],
axis=2) # (batch_size, context_len, hidden_size*4)
# Apply fully connected layer to each blended representation
# Note, blended_reps_final corresponds to b' in the handout
# Note, tf.contrib.layers.fully_connected applies a ReLU non-linarity here by default
blended_reps_final = tf.contrib.layers.fully_connected(
blended_reps, num_outputs=self.FLAGS.hidden_size
) # blended_reps_final is shape (batch_size, context_len, hidden_size)
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with vs.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(
blended_reps_final, self.context_mask)
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with vs.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(
blended_reps_final, self.context_mask)
def build_graph(self):
"""Builds the main part of the graph for the model, starting from the input embeddings to the final distributions for the answer span.
Defines:
self.logits_start, self.logits_end: Both tensors shape (batch_size, context_len).
These are the logits (i.e. values that are fed into the softmax function) for the start and end distribution.
Important: these are -large in the pad locations. Necessary for when we feed into the cross entropy function.
self.probdist_start, self.probdist_end: Both shape (batch_size, context_len). Each row sums to 1.
These are the result of taking (masked) softmax of logits_start and logits_end.
"""
# Use a RNN to get hidden states for the context and the question
# Note: here the RNNEncoder is shared (i.e. the weights are the same)
# between the context and the question.
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
context_hiddens = encoder.build_graph(self.context_embs, self.context_mask) # (batch_size, context_len, hidden_size*2)
question_hiddens = encoder.build_graph(self.qn_embs, self.qn_mask) # (batch_size, question_len, hidden_size*2)
##### Calculate multiple attention models #####
### Use context hidden states to attend to question hidden states ###
"""
basicAttn_layer = BasicAttn(self.keep_prob,
self.FLAGS.hidden_size*2, self.FLAGS.hidden_size*2)
# attn_output is shape (batch_size, context_len, hidden_size*2)
_, basicAttn_output = basicAttn_layer.build_graph(question_hiddens,
self.qn_mask, context_hiddens)
"""
### Bidirectional attention flow ###
biDirAttn_layer = bidirectionalAttn(self.keep_prob, self.FLAGS.context_len,
self.FLAGS.hidden_size*2, self.FLAGS.question_len, self.FLAGS.hidden_size*2)
# attn_output is shape (batch_size, context_len, 2*hidden_size)
biDirAttn_output = biDirAttn_layer.build_graph(question_hiddens,
self.qn_mask, context_hiddens, self.context_mask)
biDir_output = tf.contrib.layers.fully_connected(
biDirAttn_output, num_outputs=self.FLAGS.hidden_size)
### Coattention ###
coAttn_layer = coattention(self.keep_prob, self.FLAGS.context_len,
self.FLAGS.hidden_size*2, self.FLAGS.question_len, self.FLAGS.hidden_size*2)
# attn_output is shape (batch_size, context_len, 2*hidden_size)
coAttn_output = coAttn_layer.build_graph(question_hiddens, self.qn_mask,
context_hiddens, self.context_mask)
coAttn_output = tf.contrib.layers.fully_connected(
coAttn_output, num_outputs=self.FLAGS.hidden_size)
### Combine attention models ###
# Weight attentions
attentions = tf.concat([biDir_output, coAttn_output], axis=2)
attn_weight_calc = get_attn_weights(2, self.FLAGS.question_len, self.FLAGS.context_len,
self.FLAGS.hidden_size, self.keep_prob)
attn_weights = attn_weight_calc.build_graph(question_hiddens, self.qn_mask, attentions)
gatedAttns = tf.multiply(attentions, attn_weights)
print("Gattn", gatedAttns.shape.as_list())
# Concat attn_output to context_hiddens to get blended_reps
blended_reps = tf.concat([context_hiddens, gatedAttns], axis=2) # (batch_size, context_len, hidden_size*12)
"""
# Apply fully connected layer to each blended representation
# Note, blended_reps_final corresponds to b' in the handout
# Note, tf.contrib.layers.fully_connected applies a ReLU non-linarity here by default
blended_reps_layer1 = tf.contrib.layers.fully_connected(blended_reps, num_outputs=self.FLAGS.hidden_size*2)
blended_reps_layer1_DO = tf.nn.dropout(blended_reps_layer1, self.keep_prob)
blended_reps_layer2 = tf.contrib.layers.fully_connected(blended_reps_layer1_DO, num_outputs=self.FLAGS.hidden_size)
blended_reps_layer2_DO = tf.nn.dropout(blended_reps_layer2, self.keep_prob)
blended_reps_final = tf.layers.dense(blended_reps_layer2_DO, self.FLAGS.hidden_size) # blended_reps_final is shape (batch_size, context_len, hidden_size)
"""
attnSize = gatedAttns.shape.as_list()[2]
FClayer1 = tf.contrib.layers.fully_connected(gatedAttns, attnSize, scope="FC1")
FClayer1_DO = tf.nn.dropout(FClayer1, self.keep_prob)
wordStart = tf.concat([FClayer1_DO[:,0,:], FClayer1_DO[:,0,:], FClayer1_DO[:,1,:]], axis=1, name="wStart")
wordMidd = tf.concat([FClayer1_DO[:,:-2,:], FClayer1_DO[:,1:-1,:], FClayer1_DO[:,2:,:]], axis=2, name="wMidd")
wordEnd = tf.concat([FClayer1_DO[:,-2,:], FClayer1_DO[:,-1,:], FClayer1_DO[:,-1,:]], axis=1, name="wEnd")
wordCC = tf.concat([tf.expand_dims(wordStart, 1), wordMidd, tf.expand_dims(wordEnd, 1)], axis=1, name="wCC")
print("wordCC", wordCC.shape.as_list())
FClayer2 = tf.contrib.layers.fully_connected(wordCC, 3*attnSize, scope="FC2")
FClayer2_DO = tf.nn.dropout(FClayer2, self.keep_prob)
conv1 = tf.layers.conv1d(FClayer2_DO, self.FLAGS.hidden_size, kernel_size=5, padding='same')
conv1_DO = tf.nn.dropout(conv1, self.keep_prob)
conv2 = tf.layers.conv1d(conv1_DO, self.FLAGS.hidden_size, kernel_size=5, padding='same')
conv2_DO = tf.nn.dropout(conv2, self.keep_prob)
print("conv2", conv1.shape.as_list())
lstmInp = tf.concat([conv1_DO, conv2_DO, attentions], axis=2)
with vs.variable_scope("outputLSTM"):
lstmCell = rnn_cell.LSTMCell(self.FLAGS.hidden_size*2)
lstmDO = DropoutWrapper(lstmCell, input_keep_prob=self.keep_prob)
lstmOutputs,states = tf.nn.dynamic_rnn(lstmDO, lstmInp, dtype=tf.float32)
lstmOut_layer1 = tf.contrib.layers.fully_connected(lstmOutputs, num_outputs=self.FLAGS.hidden_size*2)
lstmOut_layer1_DO = tf.nn.dropout(lstmOut_layer1, self.keep_prob)
lstmOut_layer2 = tf.contrib.layers.fully_connected(lstmOut_layer1_DO, num_outputs=self.FLAGS.hidden_size)
lstmOut_layer2_DO = tf.nn.dropout(lstmOut_layer2, self.keep_prob)
lstmOut_final = tf.layers.dense(lstmOut_layer2_DO, self.FLAGS.hidden_size)
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with vs.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(lstmOut_final, self.context_mask)
#self.logits_start, self.probdist_start = softmax_layer_start.build_graph(blended_reps_final, self.context_mask)
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with vs.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(lstmOut_final, self.context_mask)
def build_graph(self):
"""Builds the main part of the graph for the model, starting from the input embeddings to the final distributions for the answer span.
Defines:
self.logits_start, self.logits_end: Both tensors shape (batch_size, context_len).
These are the logits (i.e. values that are fed into the softmax function) for the start and end distribution.
Important: these are -large in the pad locations. Necessary for when we feed into the cross entropy function.
self.probdist_start, self.probdist_end: Both shape (batch_size, context_len). Each row sums to 1.
These are the result of taking (masked) softmax of logits_start and logits_end.
"""
print("Building Pointer Model")
# Use a RNN to get hidden states for the context and the question
# Note: here the RNNEncoder is shared (i.e. the weights are the same)
# between the context and the question.
encoder = RNNEncoder(self.FLAGS.hidden_size,
self.keep_prob,
num_layers=self.FLAGS.num_layers,
mode=self.FLAGS.rnn_cell)
context_hiddens = encoder.build_graph(
self.context_embs,
self.context_mask) # (batch_size, context_len, hidden_size*2)
question_hiddens = encoder.build_graph(
self.qn_embs,
self.qn_mask) # (batch_size, question_len, hidden_size*2)
# Use context hidden states to attend to question hidden states
# BIDAG LAYER
bidaf_layer = BidirectionAttn(self.keep_prob, self.FLAGS.hidden_size)
_, _, bidaf_output = bidaf_layer.build_graph(question_hiddens,
self.qn_mask,
context_hiddens,
self.context_mask)
# attn_output is shape (batch_size, context_len, hidden_size*6)
bidaf_output = tf.concat([context_hiddens, bidaf_output],
axis=2) # bs, c_l, 8h
#SELF ATTENTION LAYER
self_attn_layer = SelfAttn(self.keep_prob, 8 * self.FLAGS.hidden_size,
self.FLAGS.selfattn_size)
_, self_attn_output = self_attn_layer.build_graph(
bidaf_output,
self.context_mask) # batch_size, context_len, 8 * hidden_size
# Concat attn_output to context_hiddens to get blended_reps
blended_reps = tf.concat(
[bidaf_output, self_attn_output],
axis=2) # (batch_size, context_len, hidden_size*16)
self_attention_encoder = RNNEncoder(self.FLAGS.hidden_size,
self.keep_prob,
num_layers=self.FLAGS.num_layers,
name="AttentionEncoder")
blended_reps = self_attention_encoder.build_graph(
blended_reps,
self.context_mask) # batch_size, context_len, hidden_size * 2
# MODELING LAYER
modeling_encoder = RNNEncoder(self.FLAGS.hidden_size,
self.keep_prob,
num_layers=self.FLAGS.num_layers,
name="ModelingEncoder")
modeling_output = modeling_encoder.build_graph(blended_reps,
self.context_mask)
modeling_encoder_two = RNNEncoder(self.FLAGS.hidden_size,
self.keep_prob,
num_layers=self.FLAGS.num_layers,
name="ModelingEncoder2")
modeling_output_two = modeling_encoder_two.build_graph(
modeling_output, self.context_mask)
total_reps_start = tf.concat([blended_reps, modeling_output], axis=2)
total_reps_end = tf.concat([blended_reps, modeling_output_two], axis=2)
# OUTPUT LAYER
with vs.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(
total_reps_start, self.context_mask)
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with vs.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(
total_reps_end, self.context_mask)
def build_graph(self):
"""Builds the main part of the graph for the model, starting from the input embeddings to the final distributions for the answer span.
Defines:
self.logits_start, self.logits_end: Both tensors shape (batch_size, context_len).
These are the logits (i.e. values that are fed into the softmax function) for the start and end distribution.
Important: these are -large in the pad locations. Necessary for when we feed into the cross entropy function.
self.probdist_start, self.probdist_end: Both shape (batch_size, context_len). Each row sums to 1.
These are the result of taking (masked) softmax of logits_start and logits_end.
"""
with tf.variable_scope('context_conv1') as scope:
context_conv1_filter = truncated_normal_var(
name='context_conv1_filter',
shape=[1, 3, 50, self.FLAGS.CONV_SHAPE],
dtype=tf.float32)
strides = [1, 1, 1, 1]
context_conv1 = tf.nn.conv2d(self.context_character_embs,
context_conv1_filter,
strides,
padding='SAME')
context_conv1_bias = zero_var(name='context_conv1_bias',
shape=[self.FLAGS.CONV_SHAPE],
dtype=tf.float32)
context_conv1_add_bias = tf.nn.bias_add(context_conv1,
context_conv1_bias)
context_relu_conv1 = tf.nn.relu(context_conv1_add_bias)
pool_size = [1, 1, 2, 1]
context_pool1 = tf.nn.max_pool(context_relu_conv1,
ksize=pool_size,
strides=pool_size,
padding='SAME',
name='context_pool_layer1')
with tf.variable_scope('context_conv2') as scope:
context_conv2_filter = truncated_normal_var(
name='context_conv2_filter',
shape=[1, 3, self.FLAGS.CONV_SHAPE, self.FLAGS.CONV_SHAPE],
dtype=tf.float32)
strides = [1, 1, 1, 1]
context_conv2 = tf.nn.conv2d(context_pool1,
context_conv2_filter,
strides,
padding='SAME')
context_conv2_bias = zero_var(name='context_conv2_bias',
shape=[self.FLAGS.CONV_SHAPE],
dtype=tf.float32)
context_conv2_add_bias = tf.nn.bias_add(context_conv2,
context_conv2_bias)
context_relu_conv2 = tf.nn.relu(context_conv2_add_bias)
pool_size = [1, 1, 3, 1]
context_pool2 = tf.nn.max_pool(context_relu_conv2,
ksize=pool_size,
strides=pool_size,
padding='SAME',
name='context_pool_layer2')
with tf.variable_scope('context_conv3') as scope:
context_conv3_filter = truncated_normal_var(
name='context_conv3_filter',
shape=[1, 3, self.FLAGS.CONV_SHAPE, self.FLAGS.CONV_SHAPE],
dtype=tf.float32)
strides = [1, 1, 1, 1]
context_conv3 = tf.nn.conv2d(context_pool2,
context_conv3_filter,
strides,
padding='SAME')
context_conv3_bias = zero_var(name='context_conv3_bias',
shape=[self.FLAGS.CONV_SHAPE],
dtype=tf.float32)
context_conv3_add_bias = tf.nn.bias_add(context_conv3,
context_conv3_bias)
context_relu_conv3 = tf.nn.relu(context_conv3_add_bias)
pool_size = [1, 1, 4, 1]
context_pool3 = tf.nn.max_pool(context_relu_conv3,
ksize=pool_size,
strides=pool_size,
padding='SAME',
name='context_pool_layer3')
context_flattened_layer = tf.reshape(
context_pool3,
[-1, self.FLAGS.context_len, 2 * self.FLAGS.CONV_SHAPE
]) #batch,300,192
context_final = tf.concat([self.context_embs, context_flattened_layer],
axis=2)
with tf.variable_scope('qn_conv1') as scope:
qn_conv1_filter = truncated_normal_var(
name='qn_conv1_filter',
shape=[1, 3, 50, self.FLAGS.CONV_SHAPE],
dtype=tf.float32)
strides = [1, 1, 1, 1]
qn_conv1 = tf.nn.conv2d(self.qn_character_embs,
qn_conv1_filter,
strides,
padding='SAME')
qn_conv1_bias = zero_var(name='qn_conv1_bias',
shape=[self.FLAGS.CONV_SHAPE],
dtype=tf.float32)
qn_conv1_add_bias = tf.nn.bias_add(qn_conv1, qn_conv1_bias)
qn_relu_conv1 = tf.nn.relu(qn_conv1_add_bias)
pool_size = [1, 1, 2, 1]
qn_pool1 = tf.nn.max_pool(qn_relu_conv1,
ksize=pool_size,
strides=pool_size,
padding='SAME',
name='qn_pool_layer1')
with tf.variable_scope('qn_conv2') as scope:
qn_conv2_filter = truncated_normal_var(
name='qn_conv2_filter',
shape=[1, 3, self.FLAGS.CONV_SHAPE, self.FLAGS.CONV_SHAPE],
dtype=tf.float32)
strides = [1, 1, 1, 1]
qn_conv2 = tf.nn.conv2d(qn_pool1,
qn_conv2_filter,
strides,
padding='SAME')
qn_conv2_bias = zero_var(name='qn_conv2_bias',
shape=[self.FLAGS.CONV_SHAPE],
dtype=tf.float32)
qn_conv2_add_bias = tf.nn.bias_add(qn_conv2, qn_conv2_bias)
qn_relu_conv2 = tf.nn.relu(qn_conv2_add_bias)
pool_size = [1, 1, 3, 1]
qn_pool2 = tf.nn.max_pool(qn_relu_conv2,
ksize=pool_size,
strides=pool_size,
padding='SAME',
name='qn_pool_layer2')
with tf.variable_scope('qn_conv3') as scope:
qn_conv3_filter = truncated_normal_var(
name='qn_conv3_filter',
shape=[1, 3, self.FLAGS.CONV_SHAPE, self.FLAGS.CONV_SHAPE],
dtype=tf.float32)
strides = [1, 1, 1, 1]
qn_conv3 = tf.nn.conv2d(qn_pool2,
qn_conv3_filter,
strides,
padding='SAME')
qn_conv3_bias = zero_var(name='qn_conv3_bias',
shape=[self.FLAGS.CONV_SHAPE],
dtype=tf.float32)
qn_conv3_add_bias = tf.nn.bias_add(qn_conv3, qn_conv3_bias)
qn_relu_conv3 = tf.nn.relu(qn_conv3_add_bias)
pool_size = [1, 1, 3, 1]
qn_pool3 = tf.nn.max_pool(qn_relu_conv3,
ksize=pool_size,
strides=pool_size,
padding='SAME',
name='qn_pool_layer3')
qn_flattened_layer = tf.reshape(
qn_pool3, [-1, self.FLAGS.question_len, 2 * self.FLAGS.CONV_SHAPE
]) #batch,30,128
qn_final = tf.concat([self.qn_embs, qn_flattened_layer], axis=2)
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
print("context_final final shape %s" % (context_final.get_shape()))
print("context_mask final shape %s" % (self.context_mask.get_shape()))
print("qn_final final shape %s" % (qn_final.get_shape()))
print("qn_mask final shape %s" % (self.qn_mask.get_shape()))
context_hiddens = encoder.build_graph(
context_final,
self.context_mask) # (batch_size, context_len, hidden_size*2+192)
question_hiddens = encoder.build_graph(
qn_final,
self.qn_mask) # (batch_size, question_len, hidden_size*2)
# Use a RNN to get hidden states for the context and the question
# Note: here the RNNEncoder is shared (i.e. the weights are the same)
# between the context and the question.
#encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
#context_hiddens = encoder.build_graph(self.context_embs, self.context_mask) # (batch_size, context_len, hidden_size*2)
#question_hiddens = encoder.build_graph(self.qn_embs, self.qn_mask) # (batch_size, question_len, hidden_size*2)
# Use context hidden states to attend to question hidden states
attn_layer = BiDAFAttn(self.keep_prob, self.FLAGS.hidden_size * 2,
self.FLAGS.hidden_size * 2)
#_, attn_output = attn_layer.build_graph(question_hiddens, self.qn_mask, context_hiddens,self.context_mask) # attn_output is shape (batch_size, context_len, hidden_size*2)
# Concat attn_output to context_hiddens to get blended_reps
#blended_reps = tf.concat([context_hiddens, attn_output], axis=2) # (batch_size, context_len, hidden_size*4)
#blended_reps=attn_layer.build_graph(question_hiddens, self.qn_mask, context_hiddens,self.context_mask) # attn_output is shape (batch_size, context_len, hidden_size*8)
#print("blended_reps shape %s" % (blended_reps.get_shape()))
#model_encoder_1 = RNNModelEncoder(self.FLAGS.hidden_size, self.keep_prob,'model_layer_1')
#model_layer_1=model_encoder_1.build_graph(blended_reps,self.qn_mask)
#model_encoder_2= RNNModelEncoder(self.FLAGS.hidden_size, self.keep_prob, 'model_layer_2')
#model_layer_2=model_encoder_2.build_graph(model_layer_1,self.context_mask)
attn_output = attn_layer.build_graph(
question_hiddens, self.qn_mask, context_hiddens, self.context_mask
) # attn_output is shape (batch_size, context_len, hidden_size*8)
blended_reps = tf.concat(
[context_hiddens, attn_output],
axis=2) # (batch_size, context_len, hidden_size*8)
model_encoder1 = RNNModelEncoder(self.FLAGS.hidden_size,
self.keep_prob,
model_name="RNNModelEncoder1")
blended_reps_thro_model_layer1 = model_encoder1.build_graph(
blended_reps,
self.context_mask) # (batch_size, context_len, hidden_size*2)
model_encoder2 = RNNModelEncoder(self.FLAGS.hidden_size,
self.keep_prob,
model_name="RNNModelEncoder2")
blended_reps_thro_model_layer2 = model_encoder2.build_graph(
blended_reps_thro_model_layer1,
self.context_mask) # (batch_size, context_len, hidden_size*2)
model_encoder3 = RNNModelEncoder(self.FLAGS.hidden_size,
self.keep_prob,
model_name="RNNModelEncoder3")
blended_reps_thro_model_layer3 = model_encoder3.build_graph(
blended_reps_thro_model_layer2,
self.context_mask) # (batch_size, context_len, hidden_size*2)
# Apply fully connected layer to each blended representation
# Note, blended_reps_final corresponds to b' in the handout
# Note, tf.contrib.layers.fully_connected applies a ReLU non-linarity here by default
#blended_reps_final = tf.contrib.layers.fully_connected(blended_reps, num_outputs=self.FLAGS.hidden_size) # blended_reps_final is shape (batch_size, context_len, hidden_size)
blended_reps_final = tf.contrib.layers.fully_connected(
blended_reps_thro_model_layer3, num_outputs=self.FLAGS.hidden_size
) # blended_reps_final is shape (batch_size, context_len, hidden_size)
#blended_reps_final = tf.contrib.layers.fully_connected(model_layer_1,num_outputs=self.FLAGS.hidden_size) # blended_reps_final is shape (batch_size, context_len, hidden_size)
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with vs.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(
blended_reps_final, self.context_mask)
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with vs.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(
blended_reps_final, self.context_mask)
def build_graph(self):
"""Builds the main part of the graph for the model, starting from the input embeddings
to the final distributions for the answer span.
Defines:
self.logits_start, self.logits_end: Both tensors shape (batch_size, context_len).
These are the logits (i.e. values fed into the softmax function) for the start and end distribution.
Important: these are -large in the pad locations. Necessary for when we feed into cross entropy function.
self.pdist_start, self.pdist_end: Both shape (batch_size, context_len). Each row sums to 1.
These are the result of taking (masked) softmax of logits_start and logits_end.
"""
# Apply EncoderBlock for the stacked embedding encoder layer
with tf.variable_scope("StackedEmbeddingEncoder"):
emb_encoder = EncoderBlock(self.flags.num_blocks_enc,
self.keep_prob,
self.flags.kernel_size_enc,
self.flags.d_model,
self.flags.num_conv_enc,
self.flags.num_heads,
self.flags.d_ff,
l2_lambda=self.flags.l2_lambda)
c_enc = emb_encoder.build_graph(self.c_embs,
self.c_longest,
self.c_mask,
reduce_input_dim=True,
reuse=None)
q_enc = emb_encoder.build_graph(self.q_embs,
self.q_longest,
self.q_mask,
reduce_input_dim=True,
reuse=True)
# Apply bidirectional attention for the context-query attention layer
with tf.variable_scope("ContextQueryAttention"):
bidaf = BiDAFAttn(self.keep_prob, l2_lambda=self.flags.l2_lambda)
# Shape: [batch_size, context_len, vec_size*8].
attn_outputs = bidaf.build_graph(c_enc, self.c_mask,
self.c_longest, q_enc,
self.q_mask, self.q_longest)
# Apply EncoderBlock x3 for the modeling layer
with tf.variable_scope("ModelEncoder"):
model_encoder = EncoderBlock(self.flags.num_blocks_mod,
self.keep_prob,
self.flags.kernel_size_mod,
self.flags.d_model,
self.flags.num_conv_mod,
self.flags.num_heads,
self.flags.d_ff,
l2_lambda=self.flags.l2_lambda)
model_1 = model_encoder.build_graph(attn_outputs,
self.c_longest,
self.c_mask,
reduce_input_dim=True)
model_2 = model_encoder.build_graph(model_1,
self.c_longest,
self.c_mask,
reuse=True)
model_3 = model_encoder.build_graph(model_2,
self.c_longest,
self.c_mask,
reuse=True)
# Use a simple softmax output layer to compute start and end probability distributions
with tf.variable_scope("Output"):
with tf.variable_scope("StartDistribution"):
start_inputs = tf.concat([model_1, model_2], axis=-1)
softmax_layer_start = SimpleSoftmaxLayer(
l2_lambda=self.flags.l2_lambda)
self.logits_start, self.pdist_start = softmax_layer_start.build_graph(
start_inputs, self.c_mask)
with tf.variable_scope("EndDistribution"):
end_inputs = tf.concat([model_1, model_3], axis=-1)
softmax_layer_end = SimpleSoftmaxLayer(
l2_lambda=self.flags.l2_lambda)
self.logits_end, self.pdist_end = softmax_layer_end.build_graph(
end_inputs, self.c_mask)
def build_graph(self):
"""Builds the main part of the graph for the model, starting from the input embeddings to the final distributions for the answer span.
Defines:
self.logits_start, self.logits_end: Both tensors shape (batch_size, context_len).
These are the logits (i.e. values that are fed into the softmax function) for the start and end distribution.
Important: these are -large in the pad locations. Necessary for when we feed into the cross entropy function.
self.probdist_start, self.probdist_end: Both shape (batch_size, context_len). Each row sums to 1.
These are the result of taking (masked) softmax of logits_start and logits_end.
"""
# Use a RNN to get hidden states for the context and the question
# Note: here the RNNEncoder is shared (i.e. the weights are the same)
# between the context and the question.
with vs.variable_scope("encoder_layer1", reuse=tf.AUTO_REUSE):
if self.FLAGS.use_stacked_encoder:
encoder = StackedRNNEncoder(self.FLAGS.hidden_size, self.FLAGS.num_encoding_layers, self.keep_prob)
else:
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
context_hiddens = encoder.build_graph(self.context_embs, self.context_mask) # (batch_size, context_len, hidden_size*2)
question_hiddens = encoder.build_graph(self.qn_embs, self.qn_mask) # (batch_size, question_len, hidden_size*2)
if self.FLAGS.num_encoding_layers > 1:
with vs.variable_scope("encoder_layer2", reuse=tf.AUTO_REUSE):
encoder2 = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
context_hiddens = encoder2.build_graph(context_hiddens, self.context_mask)
question_hiddens = encoder2.build_graph(question_hiddens, self.qn_mask)
# Use context hidden states to attend to question hidden states
if self.FLAGS.bidaf:
attn_layer = BiDirAttnFlow(self.keep_prob, self.FLAGS.hidden_size*2)
blended_reps = attn_layer.build_graph(question_hiddens, self.qn_mask, context_hiddens, self.context_mask) # (batch_size, context_len, hidden_size*8)
else:
attn_layer = BasicAttn(self.keep_prob, self.FLAGS.hidden_size*2, self.FLAGS.hidden_size*2)
_, attn_output = attn_layer.build_graph(question_hiddens, self.qn_mask, context_hiddens) # attn_output is shape (batch_size, context_len, hidden_size*2)
# Concat attn_output to context_hiddens to get blended_reps
blended_reps = tf.concat([context_hiddens, attn_output], axis=2) # (batch_size, context_len, hidden_size*4)
# Self-attention layer
if self.FLAGS.self_attend:
self_attn_layer = SelfAttn(self.keep_prob, blended_reps.shape[-1], self.FLAGS.self_attend_hidden_sz)
blended_reps = self_attn_layer.build_graph(blended_reps, self.context_mask) # (batch_size, context_len, 2*self_attend_hidden_sz)
# Apply fully connected layer to each blended representation
# Note, blended_reps_final corresponds to b' in the handout
# Note, tf.contrib.layers.fully_connected applies a ReLU non-linarity here by default
blended_reps_final = tf.contrib.layers.fully_connected(blended_reps, num_outputs=self.FLAGS.hidden_size) # blended_reps_final is shape (batch_size, context_len, hidden_size)
# TODO: Modeling layer from BiDAF. We can add another RNN (two stacked
# from BiDAF paper) to the hidden states from the attention layer.
if self.FLAGS.modeling_layer:
with vs.variable_scope("Model_Layer", reuse=tf.AUTO_REUSE):
model_layer = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
blended_reps_final = model_layer.build_graph(blended_reps_final, self.context_mask)
if self.FLAGS.modeling_layer and self.FLAGS.num_model_rnn_layers > 1:
with vs.variable_scope("Model_layer2", reuse=tf.AUTO_REUSE):
model_layer2 = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
blended_reps_final = model_layer2.build_graph(blended_reps_final, self.context_mask)
# modeling_layer = StackedRNNEncoder(blended_reps_final.shape[-1], self.FLAGS.num_model_rnn_layers, self.keep_prob)
# blended_reps_final = modeling_layer.build_graph(blended_reps_final, self.context_mask)
if self.FLAGS.pointer_network: #TODO: define flag
with vs.variable_scope("OutputLayer", reuse=tf.AUTO_REUSE):
pointer_network = PointerNetwork(self.keep_prob, blended_reps_final.shape[-1].value, self.FLAGS.hidden_size)
(self.logits_start, self.probdist_start, _, self.logits_end, self.probdist_end, _) = \
pointer_network.build_graph(blended_reps_final, self.context_mask)
else:
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with vs.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(blended_reps_final, self.context_mask)
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with vs.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(blended_reps_final, self.context_mask)
def build_graph(self):
"""Builds the main part of the graph for the model, starting from the input embeddings to the final distributions for the answer span.
Defines:
self.logits_start, self.logits_end: Both tensors shape (batch_size, context_len).
These are the logits (i.e. values that are fed into the softmax function) for the start and end distribution.
Important: these are -large in the pad locations. Necessary for when we feed into the cross entropy function.
self.probdist_start, self.probdist_end: Both shape (batch_size, context_len). Each row sums to 1.
These are the result of taking (masked) softmax of logits_start and logits_end.
"""
# Use a RNN to get hidden states for the context and the question
# Note: here the RNNEncoder is shared (i.e. the weights are the same)
# between the context and the question.
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
context_hiddens = encoder.build_graph(
self.context_embs,
self.context_mask) # (batch_size, context_len, hidden_size*2)
question_hiddens = encoder.build_graph(
self.qn_embs,
self.qn_mask) # (batch_size, question_len, hidden_size*2)
question_variation = tf.layers.dense(question_hiddens,
question_hiddens.get_shape()[2],
activation=tf.tanh)
# In[]
#question_length = tf.placeholder(tf.int32, (None,), name='question_length')
#document_length = tf.placeholder(tf.int32, (None,), name='paragraph_length')
unmasked_affinity = tf.einsum(
'ndh,nqh->ndq', context_hiddens,
question_variation) # [N, D, Q] or [N, 1+D, 1+Q] if sentinel
affinity = maybe_mask_affinity(unmasked_affinity, self.document_length)
attention_p = tf.nn.softmax(affinity, dim=1)
unmasked_affinity_t = tf.transpose(
unmasked_affinity,
[0, 2, 1]) # [N, Q, D] or [N, 1+Q, 1+D] if sentinel
affinity_t = maybe_mask_affinity(unmasked_affinity_t,
self.question_length)
attention_q = tf.nn.softmax(affinity_t, dim=1)
summary_q = tf.einsum(
'ndh,ndq->nqh', context_hiddens,
attention_p) # [N, Q, 2H] or [N, 1+Q, 2H] if sentinel
summary_d = tf.einsum(
'nqh,nqd->ndh', question_variation,
attention_q) # [N, D, 2H] or [N, 1+D, 2H] if sentinel
coattention_d = tf.einsum('nqh,nqd->ndh', summary_q, attention_q)
encoder1 = RNNEncoder1(self.FLAGS.hidden_size, self.keep_prob)
context2 = encoder1.build_graph(
summary_d,
self.context_mask) # (batch_size, context_len, hidden_size*2)
question2 = encoder1.build_graph(
summary_q,
self.qn_mask) # (batch_size, question_len, hidden_size*2)
unmasked_affinity1 = tf.einsum(
'ndh,nqh->ndq', context2,
question2) # [N, D, Q] or [N, 1+D, 1+Q] if sentinel
affinity1 = maybe_mask_affinity(unmasked_affinity1,
self.document_length)
attention_p1 = tf.nn.softmax(affinity1, dim=1)
unmasked_affinity_t1 = tf.transpose(
unmasked_affinity1,
[0, 2, 1]) # [N, Q, D] or [N, 1+Q, 1+D] if sentinel
affinity_t1 = maybe_mask_affinity(unmasked_affinity_t1,
self.question_length)
attention_q1 = tf.nn.softmax(affinity_t1, dim=1)
summary_q1 = tf.einsum(
'ndh,ndq->nqh', context2,
attention_p1) # [N, Q, 2H] or [N, 1+Q, 2H] if sentinel
summary_d1 = tf.einsum(
'nqh,nqd->ndh', question2,
attention_q1) # [N, D, 2H] or [N, 1+D, 2H] if sentinel
coattention_d1 = tf.einsum('nqh,nqd->ndh', summary_q1, attention_q1)
# In[]
document_representations = [
context_hiddens, # E^D_1
context2, # E^D_2
summary_d, # S^D_1
summary_d1, # S^D_2
coattention_d, # C^D_1
coattention_d1, # C^D_2
]
document_representation = tf.concat(document_representations, 2)
encoder2 = RNNEncoder2(self.FLAGS.hidden_size, self.keep_prob)
U = encoder2.build_graph(document_representation, self.context_mask)
# Concat attn_output to context_hiddens to get blended_reps
blended_reps = tf.concat(
[context_hiddens, U],
axis=2) # (batch_size, context_len, hidden_size*4)
# Apply fully connected layer to each blended representation
# Note, blended_reps_final corresponds to b' in the handout
# Note, tf.contrib.layers.fully_connected applies a ReLU non-linarity here by default
blended_reps_final = tf.contrib.layers.fully_connected(
blended_reps, num_outputs=self.FLAGS.hidden_size
) # blended_reps_final is shape (batch_size, context_len, hidden_size)
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with vs.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(
blended_reps_final, self.context_mask)
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with vs.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(
blended_reps_final, self.context_mask)
def build_graph(self):
"""Builds the main part of the graph for the model, starting from the input embeddings to the final distributions for the answer span.
Defines:
self.logits_start, self.logits_end: Both tensors shape (batch_size, context_len).
These are the logits (i.e. values that are fed into the softmax function) for the start and end distribution.
Important: these are -large in the pad locations. Necessary for when we feed into the cross entropy function.
self.probdist_start, self.probdist_end: Both shape (batch_size, context_len). Each row sums to 1.
These are the result of taking (masked) softmax of logits_start and logits_end.
"""
# Use a RNN to get hidden states for the context and the question
# Note: here the RNNEncoder is shared (i.e. the weights are the same)
# between the context and the question.
with vs.variable_scope("EmbedLayer"):
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
context_hiddens = encoder.build_graph(
self.context_embs,
self.context_mask) # (batch_size, context_len, hidden_size*2)
question_hiddens = encoder.build_graph(
self.qn_embs,
self.qn_mask) # (batch_size, question_len, hidden_size*2)
# Use context hidden states to attend to question hidden states
attn_layer = BiDAFAttn(self.keep_prob, self.FLAGS.hidden_size * 2,
self.FLAGS.hidden_size * 2)
_, a, c = attn_layer.build_graph(
question_hiddens, self.qn_mask, context_hiddens, self.context_mask
) # attn_output is shape (batch_size, context_len, hidden_size*2)
# Concat attn_output to context_hiddens to get blended_reps
c = tf.expand_dims(c, 1)
blended_reps = tf.concat(
[context_hiddens, a, context_hiddens * a, context_hiddens * c],
axis=2) # (batch_size, context_len, hidden_size*4)
# Apply fully connected layer to each blended representation
# Note, blended_reps_final corresponds to b' in the handout
# Note, tf.contrib.layers.fully_connected applies a ReLU non-linarity here by default
#blended_reps_final = tf.contrib.layers.fully_connected(blended_reps, num_outputs=self.FLAGS.hidden_size) # blended_reps_final is shape (batch_size, context_len, hidden_size)
with vs.variable_scope("startModelLayer"):
modeling_layer_encoder = RNNEncoder(self.FLAGS.hidden_size,
self.keep_prob)
m1 = modeling_layer_encoder.build_graph(blended_reps,
self.context_mask)
with vs.variable_scope("endModelLayer"):
modeling_layer_encoder = RNNEncoder(self.FLAGS.hidden_size,
self.keep_prob)
m2 = modeling_layer_encoder.build_graph(blended_reps,
self.context_mask)
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with vs.variable_scope("StartDist"):
blended_reps_final_start = tf.concat([m1, blended_reps], axis=2)
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(
blended_reps_final_start, self.context_mask)
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with vs.variable_scope("EndDist"):
blended_reps_final_end = tf.concat([m2, blended_reps], axis=2)
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(
blended_reps_final_end, self.context_mask)
def build_graph(self):
"""Builds the main part of the graph for the model, starting from the input embeddings to the final distributions for the answer span.
Defines:
self.logits_start, self.logits_end: Both tensors shape (batch_size, context_len).
These are the logits (i.e. values that are fed into the softmax function) for the start and end distribution.
Important: these are -large in the pad locations. Necessary for when we feed into the cross entropy function.
self.probdist_start, self.probdist_end: Both shape (batch_size, context_len). Each row sums to 1.
These are the result of taking (masked) softmax of logits_start and logits_end.
"""
if self.FLAGS.more_single_dir_rnn:
# Use a RNN to get hidden states for the context and the question
# Note: here the RNNEncoder is shared (i.e. the weights are the same)
# between the context and the question.
encoder0 = RNNEncoder0(self.FLAGS.hidden_size, self.keep_prob)
# (batch_size, context_len, hidden_size)
context_hiddens0 = encoder0.build_graph(self.context_embs, self.context_mask)
# (batch_size, question_len, hidden_size)
question_hiddens0 = encoder0.build_graph(self.qn_embs, self.qn_mask)
encoder1 = RNNEncoder1(self.FLAGS.hidden_size, self.keep_prob)
# (batch_size, context_len, hidden_size*2)
context_hiddens1 = encoder1.build_graph(context_hiddens0, self.context_mask)
# (batch_size, question_len, hidden_size*2)
question_hiddens1 = encoder1.build_graph(question_hiddens0, self.qn_mask)
else:
encoder1 = RNNEncoder1(self.FLAGS.hidden_size, self.keep_prob)
# (batch_size, context_len, hidden_size*2)
context_hiddens1 = encoder1.build_graph(self.context_embs, self.context_mask)
# (batch_size, question_len, hidden_size*2)
question_hiddens1 = encoder1.build_graph(self.qn_embs, self.qn_mask)
# Use context hidden states to attend to question hidden states
basic_attn_layer = BasicAttn(self.keep_prob, self.FLAGS.hidden_size*2, self.FLAGS.hidden_size*2, self.FLAGS.advanced_basic_attn)
# attn_output is shape (batch_size, context_len, hidden_size*2)
_, basic_attn_output = basic_attn_layer.build_graph(question_hiddens1, self.qn_mask, context_hiddens1)
# Concat basic_attn_output to context_hiddens to get blended_reps0
blended_reps0 = tf.concat([context_hiddens1, basic_attn_output], axis=2) # (batch_size, context_len, hidden_size*4)
if self.FLAGS.more_single_dir_rnn:
rnnBasicAttn = RNNBasicAttn(self.FLAGS.hidden_size*4, self.keep_prob)
rnn_basic_attn_reps = rnnBasicAttn.build_graph(blended_reps0, self.context_mask) # (batch_size, context_len, hidden_size*4)
else:
rnn_basic_attn_reps = blended_reps0
# Gang: adding self attention (R-NET)
# self_attn_layer = SelfAttn(self.keep_prob, self.FLAGS.hidden_size*4)
# # (batch_size, context_len, hidden_size*4)
# _, self_attn_output = self_attn_layer.build_graph(basic_attn_output, self.context_mask)
# Gang: adding dot attention (Attention Is All You Need)
dot_attn_layer = DotAttn(self.keep_prob, self.FLAGS.hidden_size*4, self.FLAGS.advanced_dot_attn)
# (batch_size, context_len, hidden_size*4)
_, dot_attn_output = dot_attn_layer.build_graph(rnn_basic_attn_reps, self.context_mask)
# Concat dot_attn_output to blended_reps0 to get blended_reps1
blended_reps1 = tf.concat([rnn_basic_attn_reps, dot_attn_output], axis=2) # (batch_size, context_len, hidden_size*8)
# Gang: adding gated representation (R-NET)
if self.FLAGS.gated_reps:
gated_reps_layer = GatedReps(self.FLAGS.hidden_size*8)
gated_blended_reps = gated_reps_layer.build_graph(blended_reps1)
else:
gated_blended_reps = blended_reps1
rnnDotAttn = RNNDotAttn(self.FLAGS.hidden_size*8, self.keep_prob)
# (batch_size, context_len, hidden_size*16)
rnn_dot_attn_reps = rnnDotAttn.build_graph(gated_blended_reps, self.context_mask)
if self.FLAGS.use_answer_pointer:
# blended_reps_final = tf.contrib.layers.fully_connected(rnn_dot_attn_reps,
# num_outputs = self.FLAGS.hidden_size*2)
pointer_layer_start = AnswerPointerLayerStart(self.keep_prob, self.FLAGS.hidden_size, self.FLAGS.hidden_size*16)
rQ, self.logits_start, self.probdist_start = pointer_layer_start.build_graph(question_hiddens1, self.qn_mask,
rnn_dot_attn_reps, self.context_mask)
pointer_layer_end = AnswerPointerLayerEnd(self.keep_prob, self.FLAGS.hidden_size*16)
self.logits_end, self.probdist_end = pointer_layer_end.build_graph(self.probdist_start, rQ,
rnn_dot_attn_reps, self.context_mask)
else:
# Apply fully connected layer to each blended representation
# Note, blended_reps_final corresponds to b' in the handout
# Note, tf.contrib.layers.fully_connected applies a ReLU non-linarity here by default
# blended_reps_final is shape (batch_size, context_len, hidden_size)
blended_reps_final = tf.contrib.layers.fully_connected(rnn_dot_attn_reps, num_outputs=self.FLAGS.hidden_size)
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with vs.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(blended_reps_final, self.context_mask)
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with vs.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(blended_reps_final, self.context_mask)
def build_graph(self):
"""Builds the main part of the graph for the model, starting from the input embeddings to the final distributions for the answer span.
Defines:
self.logits_start, self.logits_end: Both tensors shape (batch_size, context_len).
These are the logits (i.e. values that are fed into the softmax function) for the start and end distribution.
Important: these are -large in the pad locations. Necessary for when we feed into the cross entropy function.
self.probdist_start, self.probdist_end: Both shape (batch_size, context_len). Each row sums to 1.
These are the result of taking (masked) softmax of logits_start and logits_end.
"""
# Use a RNN to get hidden states for the context and the question
# Note: here the RNNEncoder is shared (i.e. the weights are the same)
# between the context and the question.
if self.FLAGS.self_attention:
encoder = RNNEncoder(self.FLAGS.hidden_size_encoder,
self.keep_prob)
else:
encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)
context_hiddens = encoder.build_graph(
self.context_embs,
self.context_mask) # (batch_size, context_len, hidden_size*2)
question_hiddens = encoder.build_graph(
self.qn_embs,
self.qn_mask) # (batch_size, question_len, hidden_size*2)
# Use context hidden states to attend to question hidden states
if self.FLAGS.simple_attention:
attn_layer = BasicAttn(self.keep_prob, self.FLAGS.hidden_size * 2,
self.FLAGS.hidden_size * 2)
_, attn_output = attn_layer.build_graph(
question_hiddens, self.qn_mask, context_hiddens
) # attn_output is shape (batch_size, context_len, hidden_size*2)
# Concat attn_output to context_hiddens to get blended_reps
blended_reps = tf.concat(
[context_hiddens, attn_output],
axis=2) # (batch_size, context_len, hidden_size*4)
# Apply fully connected layer to each blended representation
# Note, blended_reps_final corresponds to b' in the handout
# Note, tf.contrib.layers.fully_connected applies a ReLU non-linarity here by default
blended_reps_final = tf.contrib.layers.fully_connected(
blended_reps, num_outputs=self.FLAGS.hidden_size
) # blended_reps_final is shape (batch_size, context_len, hidden_size)
if self.FLAGS.co_attention:
#This step sends the question embeddings through a fully-connected-layer to allow for variation between question_embedding and document embedding space
question_hiddens_t = tf.transpose(
question_hiddens,
perm=[0, 2, 1]) #(batch_size,hidden_size*2,question_len)
trans_question_hiddens_t = tf.contrib.layers.fully_connected(
question_hiddens_t,
num_outputs=self.FLAGS.question_len,
activation_fn=tf.nn.tanh
) #(batch_size,hidden_size*2,question_len)
trans_question_hiddens = tf.transpose(
trans_question_hiddens_t,
perm=[0, 2, 1]) #(batch_size,question_len,hidden_size*2)
#Computing the coattention context
co_attn_layer = CoAttn(self.keep_prob, self.FLAGS.hidden_size * 2,
self.FLAGS.hidden_size * 2)
co_attn_output = co_attn_layer.build_graph(
trans_question_hiddens, self.qn_mask, self.context_mask,
context_hiddens) #(batch_size,context_len,6*hidden_size)
# performing the fusion of temporal information to the coattention context via a bidirectional GRU
with tf.variable_scope("co-attn-encoder"):
co_attn_encoder = LSTMEncoder(self.FLAGS.hidden_size,
self.keep_prob)
blended_reps_final = co_attn_encoder.build_graph(
co_attn_output, self.context_mask)
if self.FLAGS.self_attention:
# implemrntation of self attention of the rnet paper
self_attention_encoder = SelfAttn(self.FLAGS.hidden_size_encoder,
self.FLAGS.hidden_size_qp,
self.FLAGS.hidden_size_pp,
self.keep_prob)
v_p = self_attention_encoder.build_graph_qp(
context_hiddens, question_hiddens, self.context_mask,
self.qn_mask, self.FLAGS.context_len, self.FLAGS.question_len)
h_p = self_attention_encoder.build_graph_pp(
context_hiddens, question_hiddens, self.context_mask,
self.qn_mask, v_p, self.FLAGS.context_len,
self.FLAGS.question_len)
blended_reps_final = tf.concat(
[context_hiddens, v_p, h_p],
axis=2) #(batch_size,context_len,5*hidden_size)
if self.FLAGS.answer_pointer:
#implementation of answer pointer as used in R-Net paper
if self.FLAGS.co_attention:
hidden_size_attn = self.FLAGS.hidden_size * 2
elif self.FLAGS.self_attention:
hidden_size_attn = 2 * self.FLAGS.hidden_size_encoder + self.FLAGS.hidden_size_qp + 2 * self.FLAGS.hidden_size_pp
else:
hidden_size_attn = self.FLAGS.hidden_size
answer_decoder = AnswerPointer(self.FLAGS.hidden_size_encoder,
hidden_size_attn,
self.FLAGS.question_len,
self.keep_prob)
p, logits = answer_decoder.build_graph_answer_pointer(
question_hiddens, context_hiddens, blended_reps_final,
self.FLAGS.question_len, self.FLAGS.context_len, self.qn_mask,
self.context_mask)
self.logits_start = logits[0]
self.probdist_start = p[0]
self.logits_end = logits[1]
self.probdist_end = p[1]
if self.FLAGS.simple_softmax:
# Use softmax layer to compute probability distribution for start location
# Note this produces self.logits_start and self.probdist_start, both of which have shape (batch_size, context_len)
with vs.variable_scope("StartDist"):
softmax_layer_start = SimpleSoftmaxLayer()
self.logits_start, self.probdist_start = softmax_layer_start.build_graph(
blended_reps_final, self.context_mask)
# Use softmax layer to compute probability distribution for end location
# Note this produces self.logits_end and self.probdist_end, both of which have shape (batch_size, context_len)
with vs.variable_scope("EndDist"):
softmax_layer_end = SimpleSoftmaxLayer()
self.logits_end, self.probdist_end = softmax_layer_end.build_graph(
blended_reps_final, self.context_mask)
