def gen_poem(begin_word):
batch_size=1
print("[INFO]loading corpus from %s"%FLAGS.file_path)
peoms_vector,word_int_map,vocabularies=process_poems(FLAGS.file_path)
input_data=tf.placeholder(tf.int32,[batch_size,None])
end_points = rnn_model(model="lstm",input_data=input_data,output_data=None,vocab_size=len(vocabularies),\
rnn_size=128,num_layers=2,batch_size=64,learning_rate=FLAGS.learning_rate)
saver=tf.train.Saver(tf.global_variables())
init_op=tf.group(tf.global_variables_initializer(),tf.local_variables_initializer())
with tf.Session() as sess:
sess.run(init_op)
checkpoint=tf.train.latest_checkpoint(FLAGS.checkpoints_dir)
saver.restore(sess,checkpoint)
x =np.array([list(map(word_int_map.get,start_token))])
[predict,last_state]=sess.run([end_points['prediction'],end_points['last_state']],\
feed_dict={input_data:x})
if begin_word:
word = begin_word
else:
word=to_word(predict,vocabularies)
poem=""
while word != end_token:
poem +=word
x=np.zeros((1,1))
x[0,0] = word_int_map[word]
[predict,last_state] = sess.run([end_points['prediction'],end_points['last_state']],\
feed_dict={input_data:x,end_points['initial_state']:last_state})
word = to_word(predict,vocabularies)
return poem
def run_training():
if not os.path.exists(os.path.dirname(FLAGS.checkpoints_dir)):
os.mkdir(os.path.dirname(FLAGS.checkpoints_dir))
if not os.path.exists(FLAGS.checkpoints_dir):
os.mkdir(FLAGS.checkpoints_dir)
poems_vector,word_to_int,vocabularies=process_poems(FLAGS.file_path)
batch_inputs,batch_outputs=generate_batch(FLAGS.batch_size,poems_vector,word_to_int)
input_data=tf.placeholder(tf.int32,[FLAGS.batch_size,None])
output_targets=tf.placeholder(tf.int32,[FLAGS.batch_size,None])
end_points=rnn_model(model="lstm",input_data=input_data,\
output_data=output_targets,vocab_size=len(vocabularies),\
rnn_size=128,num_layers=2,batch_size=64,learning_rate=FLAGS.learning_rate)
saver=tf.train.Saver(tf.global_variables())
init_op=tf.group(tf.global_variables_initializer(),tf.local_variables_initializer())
with tf.Session() as sess:
sess.run(init_op)
start_epoch=0
checkpoint=tf.train.latest_checkpoint(FLAGS.checkpoints_dir)
if checkpoint:
saver.restore(sess,checkpoint)
print("[INFO] restore from the checkpoint{0}".format(checkpoint))
start_epoch +=int(checkpoint.split('-')[-1])
print("[INFO] start trianing...")
try:
for epoch in range(start_epoch,FLAGS.epoches):
n=0
n_chunk = len(poems_vector)//FLAGS.batch_size
for batch in range(n_chunk):
loss,_,_=sess.run([end_points['total_loss'],end_points['last_state'],end_points['train_op']]\
,feed_dict={input_data:batch_inputs[n],output_targets:batch_outputs[n]})
n+=1
print("[INFO]Epoch:%d,batch:%d,training loss:%6f"%(epoch,batch,loss))
if epoch %6==0:
saver.save(sess,os.path.join(FLAGS.checkpoints_dir,FLAGS.model_prefix),global_step=epoch)
except KeyboardInterrupt:
print("[INFO] Interrupt manually,try saving checkpoint for now...")
saver.save(sess,os.path.join(FLAGS.checkpoints_dir,FLAGS.model_prefix),global_step=epoch)
print("[INFO]Last epoch were saved,next time will start from epoch{}".format(epoch))
def run_training():
# 模型保存路径不存在则创建
if not os.path.exists(FLAGS.model_dir):
os.makedirs(FLAGS.model_dir)
# process_poems对古诗进行预处理
poems_vector, word_to_int, vocabularies = process_poems(FLAGS.file_path)
# batches_inputs, batches_outputs = generate_batch(FLAGS.batch_size, poems_vector, word_to_int)
# 占位向量
input_data = tf.placeholder(tf.int32, [FLAGS.batch_size, None])
output_targets = tf.placeholder(tf.int32, [FLAGS.batch_size, None])
# 使用lstm模型进行训练
end_points = rnn_model(model='lstm',
input_data=input_data,
output_data=output_targets,
vocab_size=len(vocabularies),
rnn_size=128,
num_layers=2,
batch_size=FLAGS.batch_size,
learning_rate=FLAGS.learning_rate)
saver = tf.train.Saver(tf.global_variables())
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
with tf.Session() as sess:
# sess = tf_debug.LocalCLIDebugWrapperSession(sess=sess)
# sess.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan)
sess.run(init_op)
start_epoch = 0
checkpoint = tf.train.latest_checkpoint(FLAGS.model_dir)
# 如果之前训练过就找回之前的训练结果
if checkpoint:
saver.restore(sess, checkpoint)
print("## restore from the checkpoint {0}".format(checkpoint))
start_epoch += int(checkpoint.split('-')[-1])
print('## start training...')
try:
n_chunk = len(poems_vector) // FLAGS.batch_size
for epoch in range(start_epoch, FLAGS.epochs):
#每次对其中的数据shuffle一次
# print(type(poems_vector))
random.shuffle(poems_vector)
# print(type(poems_vector))
# 这里有每一次输入的训练数据的列表
batches_inputs, batches_outputs = generate_batch(
FLAGS.batch_size, poems_vector, word_to_int)
n = 0
for batch in range(n_chunk):
loss, _, _ = sess.run(
[
end_points['total_loss'], end_points['last_state'],
end_points['train_op']
],
feed_dict={
input_data: batches_inputs[n],
output_targets: batches_outputs[n]
})
n += 1
print('Epoch: %d/%d, batch: %d/%d, training loss: %.6f' %
(epoch, FLAGS.epochs - 1, batch, n_chunk - 1, loss))
if epoch % 6 == 0:
saver.save(sess,
os.path.join(FLAGS.model_dir,
FLAGS.model_prefix),
global_step=epoch)
alltime = time.time() - start_time
print("Time: %d h %d min % ds" %
(alltime // 3600,
(alltime - alltime // 3600 * 3600) // 60, alltime % 60))
except KeyboardInterrupt:
print('## Interrupt manually, try saving checkpoint for now...')
saver.save(sess,
os.path.join(FLAGS.model_dir, FLAGS.model_prefix),
global_step=epoch)
print(
'## Last epoch were saved, next time will start from epoch {}.'
.format(epoch))
def run_training():
if not os.path.exists(os.path.dirname(FLAGS.checkpoints_dir)):
os.mkdir(os.path.dirname(FLAGS.checkpoints_dir))
if not os.path.exists(FLAGS.checkpoints_dir):
os.mkdir(FLAGS.checkpoints_dir)
# 单词转化的数字:向量，单词和数字一一对应的字典，单词
poems_vector, word_to_int, vocabularies = process_poems(FLAGS.file_path)
# 真实值和目标值
batches_inputs, batches_outputs = generate_batch(FLAGS.batch_size,
poems_vector, word_to_int)
# 数据占位符
input_data = tf.placeholder(tf.int32, [FLAGS.batch_size, None])
output_targets = tf.placeholder(tf.int32, [FLAGS.batch_size, None])
end_points = rnn_model(model='lstm',
input_data=input_data,
output_data=output_targets,
vocab_size=len(vocabularies),
rnn_size=128,
num_layers=2,
batch_size=64,
learning_rate=FLAGS.learning_rate)
# 实例化保存模型
saver = tf.train.Saver(tf.global_variables())
# 全局变量进行初始化
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
with tf.Session() as sess:
# sess = tf_debug.LocalCLIDebugWrapperSession(sess=sess)
# sess.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan)
# 先执行，全局变量初始化
sess.run(init_op)
start_epoch = 0
# 把之前训练过的checkpoint拿出来
checkpoint = tf.train.latest_checkpoint(FLAGS.checkpoints_dir)
if checkpoint:
# 拿出训练保存模型
saver.restore(sess, checkpoint)
print("[INFO] restore from the checkpoint {0}".format(checkpoint))
start_epoch += int(checkpoint.split('-')[-1])
print('[INFO] start training...')
try:
for epoch in range(start_epoch, FLAGS.epochs):
n = 0
# 多少行唐诗//每次训练的个数
n_chunk = len(poems_vector) // FLAGS.batch_size
for batch in range(n_chunk):
loss, _, _ = sess.run(
[
end_points['total_loss'], # 损失
end_points['last_state'], # 最后一次输出
end_points['train_op'] # 训练优化损失
],
feed_dict={
input_data: batches_inputs[n],
output_targets: batches_outputs[n]
})
n += 1
print(
'[INFO] Epoch: %d , batch: %d , training loss: %.6f' %
(epoch, batch, loss))
if epoch % 6 == 0: # 每隔多少次保存
saver.save(sess, FLAGS.checkpoints_dir, global_step=epoch)
except KeyboardInterrupt:
print(
'[INFO] Interrupt manually, try saving checkpoint for now...')
saver.save(sess, FLAGS.checkpoints_dir, global_step=epoch)
print(
'[INFO] Last epoch were saved, next time will start from epoch {}.'
.format(epoch))
def gen_poem(begin_words, num):
batch_size = 1
print('[INFO] loading corpus from %s' % FLAGS.file_path)
# 单词转化的数字:向量，单词和数字一一对应的字典，单词
poems_vector, word_int_map, vocabularies = process_poems(FLAGS.file_path)
# 此时输入为1个
input_data = tf.placeholder(tf.int32, [batch_size, None])
# 损失等
end_points = rnn_model(model='lstm',
input_data=input_data,
output_data=None,
vocab_size=len(vocabularies),
rnn_size=128,
num_layers=2,
batch_size=64,
learning_rate=FLAGS.learning_rate)
saver = tf.train.Saver(tf.global_variables())
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
with tf.Session() as sess:
sess.run(init_op)
# 保存模型的位置，拿回sess
checkpoint = tf.train.latest_checkpoint(FLAGS.checkpoints_dir)
# checkpoint = tf.train.latest_checkpoint('./model/')
saver.restore(sess, checkpoint)
# saver.restore(sess,'./model/-24')
# 从字典里面获取到的开始值
x = np.array([list(map(word_int_map.get, start_token))])
[predict, last_state
] = sess.run([end_points['prediction'], end_points['last_state']],
feed_dict={input_data: x})
poem = ''
for begin_word in begin_words:
while True:
if begin_word:
word = begin_word
else:
word = to_word(predict, vocabularies)
sentence = ''
while word != end_token:
sentence += word
x = np.zeros((1, 1))
x[0, 0] = word_int_map[word]
[predict, last_state] = sess.run(
[end_points['prediction'], end_points['last_state']],
feed_dict={
input_data: x,
end_points['initial_state']: last_state
})
word = to_word(predict, vocabularies)
# word = words[np.argmax(probs_)]
if len(sentence) == 2 + 2 * num and ('，' or '？') not in sentence[:num] and ('，' or '？') not in sentence[
num + 1:-1] and \
sentence[num] == '，' and '□' not in sentence:
poem += sentence
# sentence = ''
break
else:
print("我正在写诗呢")
return poem
def gen_poem(begin_word):
batch_size = 1
print('## loading corpus from %s' % model_dir)
poems_vector, word_int_map, vocabularies = process_poems(corpus_file)
input_data = tf.placeholder(tf.int32, [batch_size, None])
end_points = rnn_model(model='lstm',
input_data=input_data,
output_data=None,
vocab_size=len(vocabularies),
rnn_size=128,
num_layers=2,
batch_size=64,
learning_rate=lr)
saver = tf.train.Saver(tf.global_variables())
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
with tf.Session() as sess:
sess.run(init_op)
checkpoint = tf.train.latest_checkpoint(model_dir)
# print(checkpoint)
saver.restore(sess, checkpoint)
x = np.array([list(map(word_int_map.get, start_token))])
[predict, last_state
] = sess.run([end_points['prediction'], end_points['last_state']],
feed_dict={input_data: x})
poem_ = ''
word = begin_word or to_word(predict, vocabularies, poem=poem_)
i = 0
# while not poem_ or ' ' in poem_:
if 1:
print("开始作诗")
poem_ = ''
while word != end_token and word != start_token:
# while 1:
# if word == ' ':
# poem_ = gen_poem(begin_word)
# break
poem_ += word
print(poem_)
i += 1
# if i > 24:
# break
x = np.array([[word_int_map[word]]])
[predict, last_state] = sess.run(
[end_points['prediction'], end_points['last_state']],
feed_dict={
input_data: x,
end_points['initial_state']: last_state
})
word = ' '
# while word == ' ':
if 1:
time.sleep(1)
random.seed(time.time())
word = to_word(predict, vocabularies, poem_)
print('\n')
return poem_
def run_training():
if not os.path.exists(FLAGS.model_dir):
os.makedirs(FLAGS.model_dir)
poems_vector, word_to_int, vocabularies = process_poems(FLAGS.file_path)
batches_inputs, batches_outputs = generate_batch(FLAGS.batch_size,
poems_vector, word_to_int)
input_data = tf.placeholder(tf.int32, [FLAGS.batch_size, None])
output_targets = tf.placeholder(tf.int32, [FLAGS.batch_size, None])
end_points = rnn_model(model='lstm',
input_data=input_data,
output_data=output_targets,
vocab_size=len(vocabularies),
rnn_size=128,
num_layers=2,
batch_size=64,
learning_rate=FLAGS.learning_rate)
saver = tf.train.Saver(tf.global_variables())
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
with tf.Session() as sess:
# sess = tf_debug.LocalCLIDebugWrapperSession(sess=sess)
# sess.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan)
sess.run(init_op)
start_epoch = 0
checkpoint = tf.train.latest_checkpoint(FLAGS.model_dir)
if checkpoint:
saver.restore(sess, checkpoint)
print("## restore from the checkpoint {0}".format(checkpoint))
start_epoch += int(checkpoint.split('-')[-1])
print('## start training...')
try:
for epoch in range(start_epoch, FLAGS.epochs):
n = 0
n_chunk = len(poems_vector) // FLAGS.batch_size
for batch in range(n_chunk):
loss, _, _ = sess.run(
[
end_points['total_loss'], end_points['last_state'],
end_points['train_op']
],
feed_dict={
input_data: batches_inputs[n],
output_targets: batches_outputs[n]
})
n += 1
print('Epoch: %d, batch: %d, training loss: %.6f' %
(epoch, batch, loss))
if epoch % 6 == 0:
saver.save(sess,
os.path.join(FLAGS.model_dir,
FLAGS.model_prefix),
global_step=epoch)
except KeyboardInterrupt:
print('## Interrupt manually, try saving checkpoint for now...')
saver.save(sess,
os.path.join(FLAGS.model_dir, FLAGS.model_prefix),
global_step=epoch)
print(
'## Last epoch were saved, next time will start from epoch {}.'
.format(epoch))
