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match_lstm.py
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match_lstm.py
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import logging
import tensorflow as tf
import util
FLAGS = tf.app.flags.FLAGS
logger = logging.getLogger("hw4")
logger.setLevel(logging.DEBUG)
logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.DEBUG)
class MatchLstmModel():
def __init__(self, embeddings, debug_shape=False):
self.pretrained_embeddings = embeddings
self.build(debug_shape)
def add_placeholders(self):
self.question_placeholder = tf.placeholder(tf.int32,
shape=(FLAGS.batch_size, FLAGS.max_question_size),
name="question_placeholder")
self.question_mask_placeholder = tf.placeholder(tf.bool,
shape=(FLAGS.batch_size, FLAGS.max_question_size),
name="question_mask_placeholder")
self.question_seq_placeholder = tf.placeholder(tf.int32,
shape=(FLAGS.batch_size),
name="question_seq_placeholder")
self.document_placeholder = tf.placeholder(tf.int32,
shape=(FLAGS.batch_size, FLAGS.max_document_size),
name="document_placeholder")
self.document_mask_placeholder = tf.placeholder(tf.bool,
shape=(FLAGS.batch_size, FLAGS.max_document_size),
name="document_mask_placeholder")
self.document_seq_placeholder = tf.placeholder(tf.int32,
shape=(FLAGS.batch_size),
name="document_seq_placeholder")
self.span_placeholder = tf.placeholder(tf.int32,
shape=(FLAGS.batch_size, 2),
name="span_placeholder")
self.answer_placeholder = tf.placeholder(tf.int32,
shape=(FLAGS.batch_size, FLAGS.max_answer_size),
name="answer_placeholder")
self.answer_mask_placeholder = tf.placeholder(tf.bool,
shape=(FLAGS.batch_size, FLAGS.max_answer_size),
name="answer_mask_placeholder")
self.answer_seq_placeholder = tf.placeholder(tf.int32,
shape=(FLAGS.batch_size, ),
name="answer_seq_placeholder")
self.dropout_placeholder = tf.placeholder(tf.float32,
name="dropout_placeholder")
def create_feed_dict(self, data_batch, dropout=1):
feed_dict = {
self.question_placeholder: data_batch['q'],
self.question_mask_placeholder: data_batch['q_m'],
self.question_seq_placeholder: data_batch['q_s'],
self.document_placeholder: data_batch['c'],
self.document_mask_placeholder: data_batch['c_m'],
self.document_seq_placeholder: data_batch['c_s']
}
if dropout is not None:
feed_dict[self.dropout_placeholder] = dropout
if data_batch['s'] is not None:
feed_dict[self.span_placeholder] = data_batch['s']
if data_batch['a'] is not None:
feed_dict[self.answer_placeholder] = data_batch['a']
if data_batch['a_m'] is not None:
feed_dict[self.answer_mask_placeholder] = data_batch['a_m']
if data_batch['a_s'] is not None:
feed_dict[self.answer_seq_placeholder] = data_batch['a_s']
return feed_dict
def add_embedding(self):
all_embeddings = tf.cast(
tf.get_variable("embeddings", initializer=self.pretrained_embeddings, dtype=tf.float64),
tf.float32)
question_embeddings = tf.nn.embedding_lookup(params=all_embeddings, ids=self.question_placeholder)
document_embeddings = tf.nn.embedding_lookup(params=all_embeddings, ids=self.document_placeholder)
return question_embeddings, document_embeddings
###################################
##### LSTM preprocessing Layer ###
####################################
def add_preprocessing_op(self, debug_shape=False):
q_input,d_input = self.add_embedding()
dropout_rate = self.dropout_placeholder
with tf.variable_scope("Q_LSTM"):
cell = tf.nn.rnn_cell.LSTMCell(num_units=FLAGS.state_size)
initial_state = cell.zero_state(FLAGS.batch_size, tf.float32)
(output, _) = tf.nn.dynamic_rnn(cell=cell,
inputs=q_input,
initial_state=initial_state,
sequence_length=self.question_seq_placeholder
)
H_Q = output
with tf.variable_scope("P_LSTM"):
cell = tf.nn.rnn_cell.LSTMCell(num_units=FLAGS.state_size)
initial_state = cell.zero_state(FLAGS.batch_size, tf.float32)
(output, _) = tf.nn.dynamic_rnn(cell=cell,
inputs=tf.transpose(d_input,perm=[1,0,2]),
initial_state=initial_state,
sequence_length=self.question_seq_placeholder,
time_major=True
)
H_P = output
preprocessing_rep = (H_Q,H_P)
if debug_shape:
return preprocessing_rep + (
tf.shape(d_input,name="debug_PLL_d_input"),
tf.shape(q_input,name="debug_PLL_q_input"),
tf.shape(H_Q,name="debug_PLL_H_Q"),
tf.shape(H_P,name="debug_PLL_H_p")
)
return preprocessing_rep
####################################
##### Match LSTM Layer #########
####################################
def add_match_lstm_op(self, preprocessing_rep, debug_shape=False):
H_Q = preprocessing_rep[0]
H_P = tf.unpack(preprocessing_rep[1])
with tf.variable_scope("Match_LSTM"):
W_q =tf.get_variable(name='W_q',
shape = [FLAGS.state_size, FLAGS.state_size],
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer()
)
W_p =tf.get_variable(name='W_p',
shape = [FLAGS.state_size, FLAGS.state_size],
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer()
)
W_r =tf.get_variable(name='W_r',
shape = [FLAGS.state_size, FLAGS.state_size],
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer()
)
b_p =tf.get_variable(name='b_p',
shape = [FLAGS.state_size],
dtype=tf.float32,
initializer=tf.constant_initializer(0.0)
)
w =tf.get_variable(name='w',
shape = [FLAGS.state_size, 1],
dtype=tf.float32,
initializer=tf.constant_initializer(0.0)
)
b =tf.get_variable(name='b',
shape = (1,),
dtype=tf.float32,
initializer=tf.constant_initializer(0.0)
)
cell = tf.nn.rnn_cell.LSTMCell(num_units=FLAGS.state_size)
hr = cell.zero_state(FLAGS.batch_size, tf.float32)
Hr = []
for i, H_Pi in enumerate(H_P):
if i>0:
tf.get_variable_scope().reuse_variables()
Wq_HQ = tf.einsum('ijk,kl->ijl', H_Q, W_q)
Wp_HPi = tf.matmul(H_P[i], W_p)
Wr_Hr = tf.matmul(hr[1], W_r)
Gi = Wp_HPi + Wr_Hr + b_p
Gi = tf.reshape(
tensor=tf.tile(Gi, [1,FLAGS.max_question_size]),
shape=[FLAGS.batch_size, FLAGS.max_question_size, FLAGS.state_size]
)
Gi = tf.nn.tanh(Gi + Wq_HQ)
wt_Gi = tf.reshape(tf.einsum('ijk,kl->ijl', Gi, w),[FLAGS.batch_size, FLAGS.max_question_size])
alphai = tf.nn.softmax(wt_Gi + tf.tile(b, [FLAGS.max_question_size]))
alphai = tf.reshape(alphai,[FLAGS.batch_size, 1,FLAGS.max_question_size])
HQ_alphai = tf.einsum('ijk,ikl->ijl', alphai, H_Q)
HQ_alphai = tf.reshape(HQ_alphai, [FLAGS.batch_size, FLAGS.state_size])
zi = tf.concat(1, [H_P[i], HQ_alphai])
_, hr = cell(zi, hr)
Hr.append(hr[1])
Hr = tf.pack(Hr,1)
match_lstm_rep = (Hr,)
if debug_shape:
return match_lstm_rep + (
tf.shape(H_P,name="debug_MLL_HP"),
tf.shape(H_Q,name="debug_MLL_HQ"),
tf.shape(H_P[0],name="debug_MLL_HP0"),
tf.shape(Wq_HQ,name="debug_MLL_Wq_HQ"),
tf.shape(Wp_HPi,name="debug_MLL_Wp_HPi"),
tf.shape(Wr_Hr,name="debug_MLL_Wr_Hr"),
tf.shape(Gi,name="debug_MLL_Gi"),
tf.shape(wt_Gi,name="debug_MLL_wt_Gi"),
tf.shape(alphai,name="debug_MLL_alphai"),
tf.shape(HQ_alphai,name="debug_MLL_HQ_alphai"),
tf.shape(zi,name="debug_MLL_zi"),
tf.shape(Hr,name="debug_MLL_Hr"),
) + preprocessing_rep
return match_lstm_rep + preprocessing_rep
####################################
##### Answer Pointer Layer #########
####################################
def add_answer_pointer_op(self, match_lstm_rep, debug_shape=False):
Hr = match_lstm_rep[0]
Hr = tf.concat(1, [tf.zeros(shape=[FLAGS.batch_size,1,FLAGS.state_size]), Hr] )
with tf.variable_scope("ANSWER_POINTER"):
V =tf.get_variable(name='V',
shape = [FLAGS.state_size, FLAGS.state_size],
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer()
)
W_a =tf.get_variable(name='W_a',
shape = [FLAGS.state_size, FLAGS.state_size],
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer()
)
b_a =tf.get_variable(name='b_a',
shape = [FLAGS.state_size],
dtype=tf.float32,
initializer=tf.constant_initializer(0.0)
)
v =tf.get_variable(name='v',
shape = [FLAGS.state_size, 1],
dtype=tf.float32,
initializer=tf.constant_initializer(0.0)
)
c =tf.get_variable(name='c',
shape = (1,),
dtype=tf.float32,
initializer=tf.constant_initializer(0.0)
)
cell = tf.nn.rnn_cell.LSTMCell(num_units=FLAGS.state_size)
ha = cell.zero_state(FLAGS.batch_size, tf.float32)
betas = []
for k in range(FLAGS.max_answer_size):
if k > 0:
tf.get_variable_scope().reuse_variables()
V_Hr = tf.einsum('ijk,kl->ijl', Hr, V)
Wa_Ha = tf.matmul(ha[1], W_a)
Fk = Wa_Ha + b_a
Fk = tf.reshape(
tensor=tf.tile(Fk, [1,FLAGS.max_document_size+1]),
shape=[FLAGS.batch_size, FLAGS.max_document_size+1, FLAGS.state_size]
)
Fk = tf.nn.tanh(Fk + V_Hr)
vt_Fk = tf.reshape(tf.einsum('ijk,kl->ijl', Fk, v),[FLAGS.batch_size, FLAGS.max_document_size+1])
betak = tf.nn.softmax(vt_Fk + tf.tile(c, [FLAGS.max_document_size+1]))
betak_ = tf.reshape(betak,[FLAGS.batch_size, 1,FLAGS.max_document_size+1])
#
Hr_betak = tf.einsum('ijk,ikl->ijl', betak_, Hr)
Hr_betak = tf.reshape(Hr_betak, [FLAGS.batch_size, FLAGS.state_size])
#
betas.append(betak)
_, ha = cell(Hr_betak, ha)
betas = tf.pack(betas, 1)
pred = tf.argmax(betas,2)
answer_pointer_rep = (betas, pred)
if debug_shape:
return answer_pointer_rep + (
tf.shape(V_Hr,name="debug_APL_V_Hr"),
tf.shape(Fk,name="debug_APL_Fk"),
tf.shape(vt_Fk,name="debug_APL_vt_fk"),
tf.shape(betak,name="debug_APL_betak"),
tf.shape(Hr_betak,name="debug_APL_Hr_betak"),
tf.shape(betas,name="debug_APL_betas"),
tf.shape(pred,name="debug_APL_pred"),
) + match_lstm_rep
return answer_pointer_rep + match_lstm_rep
def add_loss_op(self, answer_pointer_rep, debug_shape=False):
betas = answer_pointer_rep[0]
masked_logits = tf.boolean_mask(betas, self.answer_mask_placeholder)
masked_labels = tf.boolean_mask(self.answer_placeholder, self.answer_mask_placeholder)
L = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(masked_logits,masked_labels))
return (L,) + answer_pointer_rep
def add_training_op(self, loss, debug_shape=False):
train_op = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(loss[0])
return (train_op,) + loss
def build(self, debug_shape):
self.add_placeholders()
self.preprocessing_rep = self.add_preprocessing_op(debug_shape)
self.match_lstm_rep = self.add_match_lstm_op(self.preprocessing_rep, debug_shape)
self.answer_pointer_rep = self.add_answer_pointer_op(self.match_lstm_rep, debug_shape)
self.loss = self.add_loss_op(self.answer_pointer_rep, debug_shape)
self.train_op = self.add_training_op(self.loss, debug_shape)
def debug_shape(self, sess, data_batch):
feed = self.create_feed_dict(data_batch)
train_op_output = sess.run(
fetches = util.tuple_to_list(*self.train_op),
feed_dict=feed
)
for i, tensor in enumerate(self.train_op):
if tensor.name.startswith("debug_"):
logger.debug("Shape of {} == {}".format(tensor.name[6:], train_op_output[i]))
def predict_on_batch(self, sess, data_batch):
feed = self.create_feed_dict(data_batch)
answer_pointer_rep = sess.run(
fetches = util.tuple_to_list(*self.answer_pointer_rep),
feed_dict=feed
)
pred = answer_pointer_rep[1]
return pred
def train_on_batch(self, sess, data_batch):
feed = self.create_feed_dict(data_batch)
train_op = sess.run(
fetches = util.tuple_to_list(*self.train_op),
feed_dict=feed
)
loss = train_op[1]
pred = train_op[2]
return loss, pred