logging.info("Source 7 length %d" % len(linModel.all_src[6]))
with tf.Session() as session:
session.run(init)
for i in range(config.n_epochs):
loss = linModel.train_on_batch(session, targets_batch)
logging.info("loss: %d" % loss)
linModel.config.eps = 0.5 * linModel.config.eps
linModel.prepareSGNMT(args) # reset hypos
linModel.cur_hypos = linModel.all_hypos[5:5 + linModel.config.
batch_size]
targets_batch = linModel.all_trg[5:5 +
linModel.config.batch_size]
def create_training_session(config):
"""Creates a MonitoredTrainingSession for training"""
return training.MonitoredTrainingSession(checkpoint_dir=config.output_path,
save_checkpoint_secs=1200)
if __name__ == "__main__":
# MAIN CODE STARTS HERE
# Load configuration from command line arguments or configuration file
args = get_args()
validate_args(args)
utils.switch_to_t2t_indexing()
config = Config(args)
do_multi_epoch_train(args, False)
import logging
import sys
import traceback
import time
import numpy as np
import pickle
from cam.sgnmt.decoding import core
from cam.sgnmt import decode_utils
from cam.sgnmt import utils
from cam.sgnmt.ui import get_args
from cam.sgnmt.predictors.core import UnboundedVocabularyPredictor
# Load configuration from command line arguments or configuration file
args = get_args()
decode_utils.base_init(args)
class ForcedDecoder(core.Decoder):
"""Forced decoder implementation. The decode() function returns
the same hypos as the GreedyDecoder with forced predictor. However,
this implementation keeps track of all posteriors along the way,
which are dumped to the file system afterwards.
"""
def __init__(self, decoder_args):
"""Initialize the decoder and load target sentences."""
super(ForcedDecoder, self).__init__(decoder_args)
self.trg_sentences = load_sentences(decoder_args.trg_test, "target")
def decode(self, src_sentence):
