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b91c1a3 Apr 4, 2017
@suriyadeepan @ronanki
183 lines (150 sloc) 7.26 KB
import tensorflow as tf
import numpy as np
import sys
class Seq2Seq(object):
def __init__(self, xseq_len, yseq_len,
xvocab_size, yvocab_size,
emb_dim, num_layers, ckpt_path,
epochs=100000, model_name='seq2seq_model'):
# attach these arguments to self
self.xseq_len = xseq_len
self.yseq_len = yseq_len
self.ckpt_path = ckpt_path
self.epochs = epochs
self.model_name = model_name
# build thy graph
# attach any part of the graph that needs to be exposed, to the self
def __graph__():
# placeholders
# encoder inputs : list of indices of length xseq_len
self.enc_ip = [ tf.placeholder(shape=[None,],
name='ei_{}'.format(t)) for t in range(xseq_len) ]
# labels that represent the real outputs
self.labels = [ tf.placeholder(shape=[None,],
name='ei_{}'.format(t)) for t in range(yseq_len) ]
# decoder inputs : 'GO' + [ y1, y2, ... y_t-1 ]
self.dec_ip = [ tf.zeros_like(self.enc_ip[0], dtype=tf.int64, name='GO') ] + self.labels[:-1]
# Basic LSTM cell wrapped in Dropout Wrapper
self.keep_prob = tf.placeholder(tf.float32)
# define the basic cell
basic_cell = tf.contrib.rnn.core_rnn_cell.DropoutWrapper(
tf.contrib.rnn.core_rnn_cell.BasicLSTMCell(emb_dim, state_is_tuple=True),
# stack cells together : n layered model
stacked_lstm = tf.contrib.rnn.core_rnn_cell.MultiRNNCell([basic_cell]*num_layers, state_is_tuple=True)
# for parameter sharing between training model
# and testing model
with tf.variable_scope('decoder') as scope:
# build the seq2seq model
# inputs : encoder, decoder inputs, LSTM cell type, vocabulary sizes, embedding dimensions
self.decode_outputs, self.decode_states = tf.contrib.legacy_seq2seq.embedding_rnn_seq2seq(self.enc_ip,self.dec_ip, stacked_lstm,
xvocab_size, yvocab_size, emb_dim)
# share parameters
# testing model, where output of previous timestep is fed as input
# to the next timestep
self.decode_outputs_test, self.decode_states_test = tf.contrib.legacy_seq2seq.embedding_rnn_seq2seq(
self.enc_ip, self.dec_ip, stacked_lstm, xvocab_size, yvocab_size,emb_dim,
# now, for training,
# build loss function
# weighted loss
# TODO : add parameter hint
loss_weights = [ tf.ones_like(label, dtype=tf.float32) for label in self.labels ]
self.loss = tf.contrib.legacy_seq2seq.sequence_loss(self.decode_outputs, self.labels, loss_weights, yvocab_size)
# train op to minimize the loss
self.train_op = tf.train.AdamOptimizer(learning_rate=lr).minimize(self.loss)
sys.stdout.write('<log> Building Graph ')
# build comput graph
Training and Evaluation
# get the feed dictionary
def get_feed(self, X, Y, keep_prob):
feed_dict = {self.enc_ip[t]: X[t] for t in range(self.xseq_len)}
feed_dict.update({self.labels[t]: Y[t] for t in range(self.yseq_len)})
feed_dict[self.keep_prob] = keep_prob # dropout prob
return feed_dict
# run one batch for training
def train_batch(self, sess, train_batch_gen):
# get batches
batchX, batchY = train_batch_gen.__next__()
# build feed
feed_dict = self.get_feed(batchX, batchY, keep_prob=0.5)
_, loss_v =[self.train_op, self.loss], feed_dict)
return loss_v
def eval_step(self, sess, eval_batch_gen):
# get batches
batchX, batchY = eval_batch_gen.__next__()
# build feed
feed_dict = self.get_feed(batchX, batchY, keep_prob=1.)
loss_v, dec_op_v =[self.loss, self.decode_outputs_test], feed_dict)
# dec_op_v is a list; also need to transpose 0,1 indices
# (interchange batch_size and timesteps dimensions
dec_op_v = np.array(dec_op_v).transpose([1,0,2])
return loss_v, dec_op_v, batchX, batchY
# evaluate 'num_batches' batches
def eval_batches(self, sess, eval_batch_gen, num_batches):
losses = []
for i in range(num_batches):
loss_v, dec_op_v, batchX, batchY = self.eval_step(sess, eval_batch_gen)
return np.mean(losses)
# finally the train function that
# runs the train_op in a session
# evaluates on valid set periodically
# prints statistics
def train(self, train_set, valid_set, sess=None ):
# we need to save the model periodically
saver = tf.train.Saver()
# if no session is given
if not sess:
# create a session
sess = tf.Session()
# init all variables
sys.stdout.write('\n<log> Training started </log>\n')
# run M epochs
for i in range(self.epochs):
self.train_batch(sess, train_set)
if i and i% (self.epochs//100) == 0: # TODO : make this tunable by the user
# save model to disk, self.ckpt_path + self.model_name + '.ckpt', global_step=i)
# evaluate to get validation loss
val_loss = self.eval_batches(sess, valid_set, 16) # TODO : and this
# print stats
print('\nModel saved to disk at iteration #{}'.format(i))
print('val loss : {0:.6f}'.format(val_loss))
except KeyboardInterrupt: # this will most definitely happen, so handle it
print('Interrupted by user at iteration {}'.format(i))
self.session = sess
return sess
def restore_last_session(self):
saver = tf.train.Saver()
# create a session
sess = tf.Session()
# get checkpoint state
ckpt = tf.train.get_checkpoint_state(self.ckpt_path)
# restore session
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
# return to user
return sess
# prediction
def predict(self, sess, X):
feed_dict = {self.enc_ip[t]: X[t] for t in range(self.xseq_len)}
feed_dict[self.keep_prob] = 1.
dec_op_v =, feed_dict)
# dec_op_v is a list; also need to transpose 0,1 indices
# (interchange batch_size and timesteps dimensions
dec_op_v = np.array(dec_op_v).transpose([1,0,2])
# return the index of item with highest probability
return np.argmax(dec_op_v, axis=2)