def create_model(session, forward_only, beam_search):
dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
model = seq2seq_model.Seq2SeqModel(FLAGS.post_vocab_size,
FLAGS.response_vocab_size,
_buckets,
FLAGS.size,
FLAGS.num_layers,
FLAGS.max_gradient_norm,
FLAGS.batch_size,
FLAGS.learning_rate,
FLAGS.learning_rate_decay_factor,
embedding_size=FLAGS.embedding_size,
forward_only=forward_only,
beam_search=beam_search,
beam_size=FLAGS.beam_size,
category=FLAGS.category,
use_emb=FLAGS.use_emb,
use_imemory=FLAGS.use_imemory,
use_ememory=FLAGS.use_ememory,
emotion_size=FLAGS.emotion_size,
imemory_size=FLAGS.imemory_size,
dtype=dtype)
see_variable = True
if see_variable == True:
for i in tf.all_variables():
print(i.name, i.get_shape())
ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
pre_ckpt = tf.train.get_checkpoint_state(FLAGS.pretrain_dir)
if ckpt: #and tf.gfile.Exists(ckpt.model_checkpoint_path+".index"):
if FLAGS.load_model == 0:
print("Reading model parameters from %s" %
ckpt.model_checkpoint_path)
model.saver.restore(session, ckpt.model_checkpoint_path)
else:
path = ckpt.model_checkpoint_path[:ckpt.model_checkpoint_path.
find('-') + 1] + str(
FLAGS.load_model)
print("Reading model parameters from %s" % path)
model.saver.restore(session, path)
else:
if pre_ckpt:
session.run(tf.initialize_variables(model.initial_var))
if FLAGS.pretrain > -1:
path = pre_ckpt.model_checkpoint_path[:pre_ckpt.
model_checkpoint_path.
find('-') + 1] + str(
FLAGS.pretrain)
print("Reading pretrain model parameters from %s" % path)
model.pretrain_saver.restore(session, path)
else:
print("Reading pretrain model parameters from %s" %
pre_ckpt.model_checkpoint_path)
model.pretrain_saver.restore(session,
pre_ckpt.model_checkpoint_path)
else:
print("Created model with fresh parameters.")
session.run(tf.initialize_all_variables())
with open('word2vec.npy', 'rb') as f:
wordvec = np.load(f)
vec_post = wordvec
vec_response = wordvec
initvec_post = tf.constant(vec_post,
dtype=dtype,
name='init_wordvector_post')
initvec_response = tf.constant(vec_response,
dtype=dtype,
name='init_wordvector_response')
embedding_post = [
x for x in tf.trainable_variables() if x.name ==
'embedding_attention_seq2seq/RNN/EmbeddingWrapper/embedding:0'
][0]
embedding_response = [
x for x in tf.trainable_variables() if x.name ==
'embedding_attention_seq2seq/embedding_attention_decoder/embedding:0'
][0]
session.run(embedding_post.assign(initvec_post))
session.run(embedding_response.assign(initvec_response))
if FLAGS.use_ememory:
vec_ememory = data_utils.get_ememory(FLAGS.data_dir,
FLAGS.response_vocab_size)
initvec_ememory = tf.constant(vec_ememory,
dtype=dtype,
name='init_ememory')
ememory = [
x for x in tf.all_variables() if x.name ==
'embedding_attention_seq2seq/embedding_attention_decoder/external_memory:0'
][0]
session.run(ememory.assign(initvec_ememory))
return model
def create_model(session, forward_only, beam_search):
dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
model = seq2seq_model.Seq2SeqModel(FLAGS.post_vocab_size,
FLAGS.response_vocab_size,
_buckets,
FLAGS.hidden_size,
FLAGS.num_layers,
FLAGS.max_gradient_norm,
FLAGS.batch_size,
FLAGS.learning_rate,
FLAGS.learning_rate_decay_factor,
embedding_size=FLAGS.embedding_size,
forward_only=forward_only,
beam_search=beam_search,
beam_size=FLAGS.beam_size,
category=FLAGS.category,
use_emb=FLAGS.use_emb,
use_autoEM=FLAGS.use_autoEM,
use_imemory=FLAGS.use_imemory,
use_ememory=FLAGS.use_ememory,
emotion_size=FLAGS.emotion_size,
imemory_size=FLAGS.imemory_size,
dtype=dtype)
see_variable = True
if see_variable == True:
for i in tf.all_variables():
print(i.name, i.get_shape())
ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
pre_ckpt = tf.train.get_checkpoint_state(FLAGS.pretrain_dir)
if ckpt:
if FLAGS.load_model == 0:
print("Reading model parameters from %s" %
ckpt.model_checkpoint_path)
model.saver.restore(session, ckpt.model_checkpoint_path)
else:
path = ckpt.model_checkpoint_path[:ckpt.model_checkpoint_path.
find('-') + 1] + str(
FLAGS.load_model)
print("Reading model parameters from %s" % path)
model.saver.restore(session, path)
else:
if pre_ckpt:
session.run(tf.initialize_variables(model.initial_var))
if FLAGS.pretrain > -1:
path = pre_ckpt.model_checkpoint_path[:pre_ckpt.
model_checkpoint_path.
find('-') + 1] + str(
FLAGS.pretrain)
print("Reading pretrain model parameters from %s" % path)
model.pretrain_saver.restore(session, path)
else:
print("Reading pretrain model parameters from %s" %
pre_ckpt.model_checkpoint_path)
model.pretrain_saver.restore(session,
pre_ckpt.model_checkpoint_path)
else:
print("Created model with fresh parameters.")
session.run(tf.initialize_all_variables())
vec_post, vec_response = data_utils.get_word_embedding(
FLAGS.data_dir, FLAGS.post_vocab_size,
FLAGS.response_vocab_size) # 40000*100
initvec_post = tf.constant(vec_post,
dtype=dtype,
name='init_wordvector_post')
initvec_response = tf.constant(vec_response,
dtype=dtype,
name='init_wordvector_response')
embedding_post = [
x for x in tf.trainable_variables() if x.name ==
'embedding_attention_seq2seq/RNN/EmbeddingWrapper/embedding:0'
][0]
embedding_response = [
x for x in tf.trainable_variables() if x.name ==
'embedding_attention_seq2seq/embedding_attention_decoder/embedding:0'
][0]
session.run(embedding_post.assign(initvec_post))
session.run(embedding_response.assign(initvec_response))
if FLAGS.use_ememory:
vec_ememory = data_utils.get_ememory(
FLAGS.data_dir, FLAGS.response_vocab_size) # 6*40000
initvec_ememory = tf.constant(vec_ememory,
dtype=dtype,
name='init_ememory')
ememory = [
x for x in tf.all_variables() if x.name ==
'embedding_attention_seq2seq/embedding_attention_decoder/external_memory:0'
][0]
session.run(ememory.assign(initvec_ememory))
if FLAGS.use_autoEM:
senti_embedding, grammar_embedding = data_utils.get_pretrained_embedding(
FLAGS.data_dir, FLAGS.post_vocab_size)
initvec_senti = tf.constant(senti_embedding,
dtype=dtype,
name='initvec_senti')
initvec_grammar = tf.constant(grammar_embedding,
dtype=dtype,
name='initvec_grammar')
senti_tensor = [
x for x in tf.trainable_variables()
if x.name == 'classify_model_with_buckets/senti_embed:0'
][0]
grammar_tensor = [
x for x in tf.trainable_variables()
if x.name == 'classify_model_with_buckets/grammar_embed:0'
][0]
session.run(senti_tensor.assign(initvec_senti))
session.run(grammar_tensor.assign(initvec_grammar))
return model