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.flag_values_dict()
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
print("{}={}".format(attr.upper(), value))
print("")
if FLAGS.eval_filepath is None or FLAGS.vocab_filepath is None or FLAGS.model is None:
print("Eval or Vocab filepaths are empty.")
exit()
# load data and map id-transform based on training time vocabulary
inpH = InputHelper()
x1_test, x2_test, y_test = inpH.getTestDataSet(FLAGS.eval_filepath, FLAGS.vocab_filepath, 30)
print("\nEvaluating...\n")
# Evaluation
# ==================================================
checkpoint_file = FLAGS.model
print(checkpoint_file)
graph = tf.Graph()
with 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)
with sess.as_default():
# 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.flag_values_dict()
if FLAGS.training_files is None:
print("Input Files List is empty. use --training_files argument.")
exit()
inpH = InputHelper()
train_set, dev_set, vocab_processor, sum_no_of_batches = inpH.getDataSets(FLAGS.training_files,
FLAGS.max_document_length, FLAGS.percent_dev,
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 = SiameseNet(
from utils.input_helpers import InputHelper
inpH = InputHelper()
inpH.getTsvDataCharBased("./train.txt")