def experiment_fn(run_config, params):
data = Data(FLAGS)
data.initialize_word_vectors()
model = Seq2seq(data.vocab_size, FLAGS, data.embeddings_mat)
estimator = tf.estimator.Estimator(
model_fn=model.make_graph,
# model_dir=FLAGS.model_dir,
config=run_config,
params=FLAGS)
train_input_fn, train_feed_fn = data.make_input_fn('train')
eval_input_fn, eval_feed_fn = data.make_input_fn('test')
print_vars = [
'source', 'predict'
# 'decoder_output',
# 'actual'
]
print_inputs = tf.train.LoggingTensorHook(print_vars,
every_n_iter=FLAGS.print_every,
formatter=data.get_formatter(
['source', 'predict']))
experiment = tf.contrib.learn.Experiment(
estimator=estimator,
train_input_fn=train_input_fn,
eval_input_fn=eval_input_fn,
train_steps=FLAGS.iterations,
min_eval_frequency=FLAGS.print_every,
train_monitors=[tf.train.FeedFnHook(train_feed_fn), print_inputs],
eval_hooks=[tf.train.FeedFnHook(eval_feed_fn)],
eval_steps=10)
return experiment
import tensorflow as tf
from seq2seq import Seq2seq
from data_handler import Data
from train import FLAGS
from tensorflow.python import debug as tf_debug
from tensorflow.contrib.learn import learn_runner
tf.logging.set_verbosity(tf.logging.INFO)
run_config = tf.contrib.learn.RunConfig()
run_config = run_config.replace(model_dir=FLAGS.experiment_dir)
data = Data(FLAGS)
data.initialize_word_vectors()
input_fn, _ = data.make_input_fn()
format_fn = lambda seq: ' '.join([data.rev_vocab.get(x, '<UNK>') for x in seq])
model = Seq2seq(data.vocab_size, FLAGS, data.embeddings_mat)
estimator = tf.estimator.Estimator(model_fn=model.make_graph,
config=run_config,
params=FLAGS)
def predict_feed_fn(phrase):
tokens = data.tokenize_and_map(phrase, mode='test') + [data.END_TOKEN]
def feed_fn():
return {'source_in:0': [tokens]}