def main(_):
#load data
index_to_char, char_to_index = getDicts(vocab_size)
data = read_poems(char_to_index)
print(data.shape)
train_data = data
val_data = data[0:10000]
with tf.Graph().as_default(), tf.Session() as session:
initializer = tf.random_uniform_initializer(-0.1, 0.1)
with tf.variable_scope("trainModel",
reuse=None,
initializer=initializer):
t_train = trainModel(training=True)
with tf.variable_scope("trainModel",
reuse=True,
initializer=initializer):
t_valid = trainModel(training=False)
tf.initialize_all_variables().run()
print("-------", type(tf.all_variables()))
for a in tf.all_variables():
print(a.name)
print("-------")
# number of epoch for training
for i in range(100):
#let learning rate decay
learning_decay = lr_decay**max(i, 0.0)
tf.assign(t_train._learning_rate, learning_decay).eval()
print("Epoch ", i + 1)
#train
train_perplexity = train(session,
t_train,
train_data,
t_train._train,
index_to_char,
verbose=True)
print("train_perplexity: ", train_perplexity)
checkpoint_path = os.path.join("", 'model.ckpt')
t_train._saver.save(session, checkpoint_path, global_step=i)
#print("have saved checkpoint")
#validate
val_perplexity = train(session, t_valid, val_data, tf.no_op(),
index_to_char)
print("val_perplexity: ", val_perplexity)
def main(_):
#load data
index_to_char, char_to_index = getDicts(vocab_size)
data = read_poems(char_to_index)
#print(data.shape)
train_data = data[1000:147541]
val_data = data[0:1000]
with tf.Graph().as_default(), tf.Session() as session:
initializer = tf.random_uniform_initializer(-0.1, 0.1)
with tf.variable_scope("trainModel", reuse=None, initializer=initializer):
t_train = trainModel(training=True)
with tf.variable_scope("trainModel", reuse=True, initializer=initializer):
t_valid = trainModel(training=False)
tf.initialize_all_variables().run()
print("-------", type(tf.all_variables()))
for a in tf.all_variables():
print(a.name)
print("-------")
for i in range(1):
#let learning rate decay
learning_decay = lr_decay ** max(i, 0.0)
tf.assign(t_train._learning_rate, learning_decay).eval()
print("Epoch ", i+1)
#train
train_perplexity = train(session, t_train, train_data, t_train._train, index_to_char, verbose=True)
print("train_perplexity: ", train_perplexity)
checkpoint_path = os.path.join("", 'model.ckpt')
t_train._saver.save(session, checkpoint_path, global_step=i)
#print("have saved checkpoint")
#validate
val_perplexity = train(session, t_valid, val_data, tf.no_op(), index_to_char)
print("val_perplexity: ", val_perplexity)
import os
import sys
from reader import getDicts
from reader import read_poems
from train import trainModel
checkpoint_dir = os.path.join('.')
exclusion = ['*']
print 'Character: ', sys.argv[1]
vocab_size = 2000
index_to_char, char_to_index = getDicts(vocab_size)
data = read_poems(char_to_index)
with tf.variable_scope("trainModel"):
model = trainModel(training=False, infer=True)
with tf.Session() as sess:
tf.initialize_all_variables().run()
saver = tf.train.Saver(tf.all_variables())
print '-------', type(tf.all_variables())
for a in tf.all_variables():
print a.name
print '-------'
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
print 'ckpt.model_checkpoint_path: ', ckpt.model_checkpoint_path