我的环境：ubuntu14.04+python3.5+anaconda+pygame
RNN不像传统的神经网络-它们的输出输出是固定的，而RNN允许我们输入输出向量序列。RNN是为了对序列数据进行建模而产生的。
样本序列性：样本间存在顺序关系，每个样本和它之前的样本存在关联。比如说，在文本中，一个词和它前面的词是有关联的；在气象数据中，一天的气温和前几天的气温是有关联的。
例如本帖要使用RNN生成古诗，你给它输入一堆古诗词，它会学着生成和前面相关联的字词。如果你给它输入一堆姓名，它会学着生成姓名；给它输入一堆古典乐/歌词，它会学着生成古典乐/歌词，甚至可以给它输入源代码。
本帖代码移植自char-rnn，它是基于Torch的洋文模型，稍加修改即可应用于中文。char-rnn使用文本文件做为输入、训练RNN模型，然后使用它生成和训练数据类似的文本。
使用的数据集：全唐诗(43030首)：https://pan.baidu.com/s/1o7QlUhO
训练：
import collections
import numpy as np
import tensorflow as tf
#-------------------------------数据预处理---------------------------#
poetry_file ='poetry.txt'
# 诗集
poetrys = []
with open(poetry_file, "r", encoding='utf-8',) as f:
for line in f:
try:
title, content = line.strip().split(':')
content = content.replace(' ','')
if '_' in content or '(' in content or '（' in content or '《' in content or '[' in content:
continue
if len(content) < 5 or len(content) > 79:
continue
content = '[' + content + ']'
poetrys.append(content)
except Exception as e:
pass
# 按诗的字数排序
poetrys = sorted(poetrys,key=lambda line: len(line))
print('唐诗总数: ', len(poetrys))
# 统计每个字出现次数
all_words = []
for poetry in poetrys:
all_words += [word for word in poetry]
counter = collections.Counter(all_words)
count_pairs = sorted(counter.items(), key=lambda x: -x[1])
words, _ = zip(*count_pairs)
# 取前多少个常用字
words = words[:len(words)] + (' ',)
# 每个字映射为一个数字ID
word_num_map = dict(zip(words, range(len(words))))
# 把诗转换为向量形式，参考TensorFlow练习1
to_num = lambda word: word_num_map.get(word, len(words))
poetrys_vector = [ list(map(to_num, poetry)) for poetry in poetrys]
#[[314, 3199, 367, 1556, 26, 179, 680, 0, 3199, 41, 506, 40, 151, 4, 98, 1],
#[339, 3, 133, 31, 302, 653, 512, 0, 37, 148, 294, 25, 54, 833, 3, 1, 965, 1315, 377, 1700, 562, 21, 37, 0, 2, 1253, 21, 36, 264, 877, 809, 1]
#....]
# 每次取64首诗进行训练
batch_size = 64
n_chunk = len(poetrys_vector) // batch_size
x_batches = []
y_batches = []
for i in range(n_chunk):
start_index = i * batch_size
end_index = start_index + batch_size
batches = poetrys_vector[start_index:end_index]
length = max(map(len,batches))
xdata = np.full((batch_size,length), word_num_map[' '], np.int32)
for row in range(batch_size):
xdata[row,:len(batches[row])] = batches[row]
ydata = np.copy(xdata)
ydata[:,:-1] = xdata[:,1:]
"""
xdata ydata
[6,2,4,6,9] [2,4,6,9,9]
[1,4,2,8,5] [4,2,8,5,5]
"""
x_batches.append(xdata)
y_batches.append(ydata)
#---------------------------------------RNN--------------------------------------#
input_data = tf.placeholder(tf.int32, [batch_size, None])
output_targets = tf.placeholder(tf.int32, [batch_size, None])
# 定义RNN
def neural_network(model='lstm', rnn_size=128, num_layers=2):
if model == 'rnn':
cell_fun = tf.nn.rnn_cell.BasicRNNCell
elif model == 'gru':
cell_fun = tf.nn.rnn_cell.GRUCell
elif model == 'lstm':
cell_fun = tf.nn.rnn_cell.BasicLSTMCell
cell = cell_fun(rnn_size, state_is_tuple=True)
cell = tf.nn.rnn_cell.MultiRNNCell([cell] * num_layers, state_is_tuple=True)
initial_state = cell.zero_state(batch_size, tf.float32)
with tf.variable_scope('rnnlm'):
softmax_w = tf.get_variable("softmax_w", [rnn_size, len(words)+1])
softmax_b = tf.get_variable("softmax_b", [len(words)+1])
with tf.device("/cpu:0"):
embedding = tf.get_variable("embedding", [len(words)+1, rnn_size])
inputs = tf.nn.embedding_lookup(embedding, input_data)
outputs, last_state = tf.nn.dynamic_rnn(cell, inputs, initial_state=initial_state, scope='rnnlm')
output = tf.reshape(outputs,[-1, rnn_size])
logits = tf.matmul(output, softmax_w) + softmax_b
probs = tf.nn.softmax(logits)
return logits, last_state, probs, cell, initial_state
#训练
def train_neural_network():
logits, last_state, _, _, _ = neural_network()
targets = tf.reshape(output_targets, [-1])
loss = tf.contrib.legacy_seq2seq.sequence_loss_by_example([logits], [targets], [tf.ones_like(targets, dtype=tf.float32)], len(words))
cost = tf.reduce_mean(loss)
learning_rate = tf.Variable(0.0, trainable=False)
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(cost, tvars), 5)
optimizer = tf.train.AdamOptimizer(learning_rate)
train_op = optimizer.apply_gradients(zip(grads, tvars))
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
saver = tf.train.Saver(tf.all_variables())
for epoch in range(50):
sess.run(tf.assign(learning_rate, 0.002 * (0.97 ** epoch)))
n = 0
for batche in range(n_chunk):
train_loss, _ , _ = sess.run([cost, last_state, train_op], feed_dict={input_data: x_batches[n], output_targets: y_batches[n]})
n += 1
print(epoch, batche, train_loss)
if epoch % 7 == 0:
saver.save(sess, 'poetry.module', global_step=epoch)
train_neural_network()
大概要训练50次也就是目录下产生poetry.module-50文件。 生成古诗
import collections
import numpy as np
import tensorflow as tf
#-------------------------------数据预处理---------------------------#
poetry_file ='poetry.txt'
# 诗集
poetrys = []
with open(poetry_file, "r", encoding='utf-8',) as f:
for line in f:
try:
title, content = line.strip().split(':')
content = content.replace(' ','')
if '_' in content or '(' in content or '（' in content or '《' in content or '[' in content:
continue
if len(content) < 5 or len(content) > 79:
continue
content = '[' + content + ']'
poetrys.append(content)
except Exception as e:
pass
# 按诗的字数排序
poetrys = sorted(poetrys,key=lambda line: len(line))
print('唐诗总数: ', len(poetrys))
# 统计每个字出现次数
all_words = []
for poetry in poetrys:
all_words += [word for word in poetry]
counter = collections.Counter(all_words)
count_pairs = sorted(counter.items(), key=lambda x: -x[1])
words, _ = zip(*count_pairs)
# 取前多少个常用字
words = words[:len(words)] + (' ',)
# 每个字映射为一个数字ID
word_num_map = dict(zip(words, range(len(words))))
# 把诗转换为向量形式，参考TensorFlow练习1
to_num = lambda word: word_num_map.get(word, len(words))
poetrys_vector = [ list(map(to_num, poetry)) for poetry in poetrys]
#[[314, 3199, 367, 1556, 26, 179, 680, 0, 3199, 41, 506, 40, 151, 4, 98, 1],
#[339, 3, 133, 31, 302, 653, 512, 0, 37, 148, 294, 25, 54, 833, 3, 1, 965, 1315, 377, 1700, 562, 21, 37, 0, 2, 1253, 21, 36, 264, 877, 809, 1]
#....]
batch_size = 1
n_chunk = len(poetrys_vector) // batch_size
x_batches = []
y_batches = []
for i in range(n_chunk):
start_index = i * batch_size
end_index = start_index + batch_size
batches = poetrys_vector[start_index:end_index]
length = max(map(len,batches))
xdata = np.full((batch_size,length), word_num_map[' '], np.int32)
for row in range(batch_size):
xdata[row,:len(batches[row])] = batches[row]
ydata = np.copy(xdata)
ydata[:,:-1] = xdata[:,1:]
"""
xdata ydata
[6,2,4,6,9] [2,4,6,9,9]
[1,4,2,8,5] [4,2,8,5,5]
"""
x_batches.append(xdata)
y_batches.append(ydata)
#---------------------------------------RNN--------------------------------------#
input_data = tf.placeholder(tf.int32, [batch_size, None])
output_targets = tf.placeholder(tf.int32, [batch_size, None])
# 定义RNN
def neural_network(model='lstm', rnn_size=128, num_layers=2):
if model == 'rnn':
cell_fun = tf.nn.rnn_cell.BasicRNNCell
elif model == 'gru':
cell_fun = tf.nn.rnn_cell.GRUCell
elif model == 'lstm':
cell_fun = tf.nn.rnn_cell.BasicLSTMCell
cell = cell_fun(rnn_size, state_is_tuple=True)
cell = tf.nn.rnn_cell.MultiRNNCell([cell] * num_layers, state_is_tuple=True)
initial_state = cell.zero_state(batch_size, tf.float32)
with tf.variable_scope('rnnlm'):
softmax_w = tf.get_variable("softmax_w", [rnn_size, len(words)+1])
softmax_b = tf.get_variable("softmax_b", [len(words)+1])
with tf.device("/cpu:0"):
embedding = tf.get_variable("embedding", [len(words)+1, rnn_size])
inputs = tf.nn.embedding_lookup(embedding, input_data)
outputs, last_state = tf.nn.dynamic_rnn(cell, inputs, initial_state=initial_state, scope='rnnlm')
output = tf.reshape(outputs,[-1, rnn_size])
logits = tf.matmul(output, softmax_w) + softmax_b
probs = tf.nn.softmax(logits)
return logits, last_state, probs, cell, initial_state
#-------------------------------生成古诗---------------------------------#
# 使用训练完成的模型
def gen_poetry():
def to_word(weights):
t = np.cumsum(weights)
s = np.sum(weights)
sample = int(np.searchsorted(t, np.random.rand(1)*s))
return words[sample]
_, last_state, probs, cell, initial_state = neural_network()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.all_variables())
saver.restore(sess, 'poetry.module-0')
state_ = sess.run(cell.zero_state(1, tf.float32))
x = np.array([list(map(word_num_map.get, '['))])
[probs_, state_] = sess.run([probs, last_state], feed_dict={input_data: x, initial_state: state_})
word = to_word(probs_)
#word = words[np.argmax(probs_)]
poem = ''
while word != ']':
poem += word
x = np.zeros((1,1))
x[0,0] = word_num_map[word]
[probs_, state_] = sess.run([probs, last_state], feed_dict={input_data: x, initial_state: state_})
word = to_word(probs_)
#word = words[np.argmax(probs_)]
return poem
print(gen_poetry())
替换你自己的 poetry.module-50训练模型。
生成藏头诗
import collections
import numpy as np
import tensorflow as tf
#-------------------------------数据预处理---------------------------#
poetry_file ='poetry.txt'
# 诗集
poetrys = []
with open(poetry_file, "r", encoding='utf-8',) as f:
for line in f:
try:
title, content = line.strip().split(':')
content = content.replace(' ','')
if '_' in content or '(' in content or '（' in content or '《' in content or '[' in content:
continue
if len(content) < 5 or len(content) > 79:
continue
content = '[' + content + ']'
poetrys.append(content)
except Exception as e:
pass
# 按诗的字数排序
poetrys = sorted(poetrys,key=lambda line: len(line))
print('唐诗总数: ', len(poetrys))
# 统计每个字出现次数
all_words = []
for poetry in poetrys:
all_words += [word for word in poetry]
counter = collections.Counter(all_words)
count_pairs = sorted(counter.items(), key=lambda x: -x[1])
words, _ = zip(*count_pairs)
# 取前多少个常用字
words = words[:len(words)] + (' ',)
# 每个字映射为一个数字ID
word_num_map = dict(zip(words, range(len(words))))
# 把诗转换为向量形式，参考TensorFlow练习1
to_num = lambda word: word_num_map.get(word, len(words))
poetrys_vector = [ list(map(to_num, poetry)) for poetry in poetrys]
#[[314, 3199, 367, 1556, 26, 179, 680, 0, 3199, 41, 506, 40, 151, 4, 98, 1],
#[339, 3, 133, 31, 302, 653, 512, 0, 37, 148, 294, 25, 54, 833, 3, 1, 965, 1315, 377, 1700, 562, 21, 37, 0, 2, 1253, 21, 36, 264, 877, 809, 1]
#....]
batch_size = 1
n_chunk = len(poetrys_vector) // batch_size
x_batches = []
y_batches = []
for i in range(n_chunk):
start_index = i * batch_size
end_index = start_index + batch_size
batches = poetrys_vector[start_index:end_index]
length = max(map(len,batches))
xdata = np.full((batch_size,length), word_num_map[' '], np.int32)
for row in range(batch_size):
xdata[row,:len(batches[row])] = batches[row]
ydata = np.copy(xdata)
ydata[:,:-1] = xdata[:,1:]
"""
xdata ydata
[6,2,4,6,9] [2,4,6,9,9]
[1,4,2,8,5] [4,2,8,5,5]
"""
x_batches.append(xdata)
y_batches.append(ydata)
#---------------------------------------RNN--------------------------------------#
input_data = tf.placeholder(tf.int32, [batch_size, None])
output_targets = tf.placeholder(tf.int32, [batch_size, None])
# 定义RNN
def neural_network(model='lstm', rnn_size=128, num_layers=2):
if model == 'rnn':
cell_fun = tf.nn.rnn_cell.BasicRNNCell
elif model == 'gru':
cell_fun = tf.nn.rnn_cell.GRUCell
elif model == 'lstm':
cell_fun = tf.nn.rnn_cell.BasicLSTMCell
cell = cell_fun(rnn_size, state_is_tuple=True)
cell = tf.nn.rnn_cell.MultiRNNCell([cell] * num_layers, state_is_tuple=True)
initial_state = cell.zero_state(batch_size, tf.float32)
with tf.variable_scope('rnnlm'):
softmax_w = tf.get_variable("softmax_w", [rnn_size, len(words)+1])
softmax_b = tf.get_variable("softmax_b", [len(words)+1])
with tf.device("/cpu:0"):
embedding = tf.get_variable("embedding", [len(words)+1, rnn_size])
inputs = tf.nn.embedding_lookup(embedding, input_data)
outputs, last_state = tf.nn.dynamic_rnn(cell, inputs, initial_state=initial_state, scope='rnnlm')
output = tf.reshape(outputs,[-1, rnn_size])
logits = tf.matmul(output, softmax_w) + softmax_b
probs = tf.nn.softmax(logits)
return logits, last_state, probs, cell, initial_state
#-------------------------------生成古诗---------------------------------#
# 使用训练完成的模型
def gen_poetry_with_head(head):
def to_word(weights):
t = np.cumsum(weights)
s = np.sum(weights)
sample = int(np.searchsorted(t, np.random.rand(1)*s))
return words[sample]
_, last_state, probs, cell, initial_state = neural_network()
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
saver = tf.train.Saver(tf.all_variables())
saver.restore(sess, 'poetry.module-0')
state_ = sess.run(cell.zero_state(1, tf.float32))
poem = ''
i = 0
for word in head:
while word != '，' and word != '。':
poem += word
x = np.array([list(map(word_num_map.get, word))])
[probs_, state_] = sess.run([probs, last_state], feed_dict={input_data: x, initial_state: state_})
word = to_word(probs_)
#time.sleep(1)
if i % 2 == 0:
poem += '，'
else:
poem += '。'
i += 1
return poem
print(gen_poetry_with_head('一二三四'))
同样：替换你自己的 poetry.module-50训练模型。 读取模型的方法：
module_file = tf.train.latest_checkpoint('.')
#print(module_file)
saver.restore(sess, module_file)
https://github.com/ryankiros/neural-storyteller（根据图片生成故事）
转自：http://blog.topspeedsnail.com/archives/tag/tensorflow/page/3
