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train.py
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train.py
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#! /usr/bin/env python
import tensorflow as tf
import numpy as np
import re
import os
import time
import datetime
import gc
from input_helpers import InputHelper
from siamese_network import SiameseLSTM
from tensorflow.contrib import learn
import gzip
from random import random
# Parameters
# ==================================================
tf.flags.DEFINE_integer("embedding_dim", 100, "Dimensionality of character embedding (default: 300)")
tf.flags.DEFINE_float("dropout_keep_prob", 0.5, "Dropout keep probability (default: 0.5)")
tf.flags.DEFINE_float("l2_reg_lambda", 0.0, "L2 regularizaion lambda (default: 0.0)")
tf.flags.DEFINE_string("training_files", "person_match.train2", "training file (default: None)")
tf.flags.DEFINE_integer("hidden_units", 50, "Number of hidden units in softmax regression layer (default:50)")
# Training parameters
tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")
tf.flags.DEFINE_integer("num_epochs", 300, "Number of training epochs (default: 200)")
tf.flags.DEFINE_integer("evaluate_every", 1000, "Evaluate model on dev set after this many steps (default: 100)")
tf.flags.DEFINE_integer("checkpoint_every", 1000, "Save model after this many steps (default: 100)")
# Misc Parameters
tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")
FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
print("{}={}".format(attr.upper(), value))
print("")
if FLAGS.training_files==None:
print "Input Files List is empty. use --training_files argument."
exit()
max_document_length=30
inpH = InputHelper()
train_set, dev_set, vocab_processor,sum_no_of_batches = inpH.getDataSets(FLAGS.training_files,max_document_length, 10, FLAGS.batch_size)
# Training
# ==================================================
print("starting graph def")
with tf.Graph().as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
sess = tf.Session(config=session_conf)
print("started session")
with sess.as_default():
siameseModel = SiameseLSTM(
sequence_length=max_document_length,
vocab_size=len(vocab_processor.vocabulary_),
embedding_size=FLAGS.embedding_dim,
hidden_units=FLAGS.hidden_units,
l2_reg_lambda=FLAGS.l2_reg_lambda,
batch_size=FLAGS.batch_size)
# Define Training procedure
global_step = tf.Variable(0, name="global_step", trainable=False)
optimizer = tf.train.AdamOptimizer(1e-3)
print("initialized siameseModel object")
grads_and_vars=optimizer.compute_gradients(siameseModel.loss)
tr_op_set = optimizer.apply_gradients(grads_and_vars, global_step=global_step)
print("defined training_ops")
# Keep track of gradient values and sparsity (optional)
grad_summaries = []
for g, v in grads_and_vars:
if g is not None:
grad_hist_summary = tf.histogram_summary("{}/grad/hist".format(v.name), g)
sparsity_summary = tf.scalar_summary("{}/grad/sparsity".format(v.name), tf.nn.zero_fraction(g))
grad_summaries.append(grad_hist_summary)
grad_summaries.append(sparsity_summary)
grad_summaries_merged = tf.merge_summary(grad_summaries)
print("defined gradient summaries")
# Output directory for models and summaries
timestamp = str(int(time.time()))
out_dir = os.path.abspath(os.path.join(os.path.curdir, "runs", timestamp))
print("Writing to {}\n".format(out_dir))
# Checkpoint directory. Tensorflow assumes this directory already exists so we need to create it
checkpoint_dir = os.path.abspath(os.path.join(out_dir, "checkpoints"))
checkpoint_prefix = os.path.join(checkpoint_dir, "model")
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
saver = tf.train.Saver(tf.all_variables(), max_to_keep=100)
# Write vocabulary
vocab_processor.save(os.path.join(checkpoint_dir, "vocab"))
# Initialize all variables
sess.run(tf.initialize_all_variables())
print("init all variables")
graph_def = tf.get_default_graph().as_graph_def()
graphpb_txt = str(graph_def)
with open(os.path.join(checkpoint_dir, "graphpb.txt"), 'w') as f:
f.write(graphpb_txt)
def train_step(x1_batch, x2_batch, y_batch):
"""
A single training step
"""
if random()>0.5:
feed_dict = {
siameseModel.input_x1: x1_batch,
siameseModel.input_x2: x2_batch,
siameseModel.input_y: y_batch,
siameseModel.dropout_keep_prob: FLAGS.dropout_keep_prob,
}
else:
feed_dict = {
siameseModel.input_x1: x2_batch,
siameseModel.input_x2: x1_batch,
siameseModel.input_y: y_batch,
siameseModel.dropout_keep_prob: FLAGS.dropout_keep_prob,
}
_, step, loss, accuracy, dist = sess.run([tr_op_set, global_step, siameseModel.loss, siameseModel.accuracy, siameseModel.distance], feed_dict)
time_str = datetime.datetime.now().isoformat()
d = np.copy(dist)
d[d>=0.5]=999.0
d[d<0.5]=1
d[d>1.0]=0
accuracy = np.mean(y_batch==d)
print("TRAIN {}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
print y_batch, dist, d
def dev_step(x1_batch, x2_batch, y_batch):
"""
A single training step
"""
if random()>0.5:
feed_dict = {
siameseModel.input_x1: x1_batch,
siameseModel.input_x2: x2_batch,
siameseModel.input_y: y_batch,
siameseModel.dropout_keep_prob: FLAGS.dropout_keep_prob,
}
else:
feed_dict = {
siameseModel.input_x1: x2_batch,
siameseModel.input_x2: x1_batch,
siameseModel.input_y: y_batch,
siameseModel.dropout_keep_prob: FLAGS.dropout_keep_prob,
}
step, loss, accuracy, dist = sess.run([global_step, siameseModel.loss, siameseModel.accuracy, siameseModel.distance], feed_dict)
time_str = datetime.datetime.now().isoformat()
d = np.copy(dist)
d[d>=0.5]=999.0
d[d<0.5]=1
d[d>1.0]=0
accuracy = np.mean(y_batch==d)
print("DEV {}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
print y_batch, dist, d
return accuracy
# Generate batches
batches=inpH.batch_iter(
list(zip(train_set[0], train_set[1], train_set[2])), FLAGS.batch_size, FLAGS.num_epochs)
ptr=0
max_validation_acc=0.0
for nn in xrange(sum_no_of_batches*FLAGS.num_epochs):
batch = batches.next()
if len(batch)<1:
continue
x1_batch,x2_batch, y_batch = zip(*batch)
if len(y_batch)<1:
continue
train_step(x1_batch, x2_batch, y_batch)
current_step = tf.train.global_step(sess, global_step)
sum_acc=0.0
if current_step % FLAGS.evaluate_every == 0:
print("\nEvaluation:")
dev_batches = inpH.batch_iter(list(zip(dev_set[0],dev_set[1],dev_set[2])), FLAGS.batch_size, 1)
for db in dev_batches:
if len(db)<1:
continue
x1_dev_b,x2_dev_b,y_dev_b = zip(*db)
if len(y_dev_b)<1:
continue
acc = dev_step(x1_dev_b, x2_dev_b, y_dev_b)
sum_acc = sum_acc + acc
print("")
if current_step % FLAGS.checkpoint_every == 0:
if sum_acc >= max_validation_acc:
max_validation_acc = sum_acc
saver.save(sess, checkpoint_prefix, global_step=current_step)
tf.train.write_graph(sess.graph.as_graph_def(), checkpoint_prefix, "graph"+str(nn)+".pb", as_text=False)
print("Saved model {} with sum_accuracy={} checkpoint to {}\n".format(nn, max_validation_acc, checkpoint_prefix))