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")