TensorFlow练习7: 基于RNN生成古诗词

  http://blog.topspeedsnail.com/archives/10542

主题 TensorFlow

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。

关于RNN：

• TensorFlow练习3: RNN, Recurrent Neural Networks
• http://karpathy.github.io/2015/05/21/rnn-effectiveness/

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 os
import collections
import numpyas np
import tensorflowas tf
from tensorflow.python.opsimport rnn_cell
from tensorflow.python.opsimport seq2seq
import time
 
#-------------------------------数据预处理---------------------------#
 
poetry_file ='poetry.txt'
 
# 诗集
poetrys = []
with open(poetry_file, "r", encoding='utf-8',) as f:
 for linein f:
 try:
 title, content = line.strip().split(':')
 content = content.replace(' ','')
 if '_' in contentor '(' in contentor '（' in contentor '《' in contentor '[' 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 poetryin poetrys:
 all_words += [wordfor wordin 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 poetryin 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 rowin 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 = rnn_cell.BasicRNNCell
 elif model == 'gru':
 cell_fun = rnn_cell.GRUCell
 elif model == 'lstm':
 cell_fun = rnn_cell.BasicLSTMCell
 
 cell = cell_fun(rnn_size, state_is_tuple=True)
 cell = 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 = 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 epochin range(50):
 sess.run(tf.assign(learning_rate, 0.002 * (0.97 ** epoch)))
 n = 0
 for batchein 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()

使用训练好的模型生成古诗：

import os
import collections
import numpyas np
import tensorflowas tf
from tensorflow.python.opsimport rnn_cell
from tensorflow.python.opsimport seq2seq
import time
 
#-------------------------------数据预处理---------------------------#
 
poetry_file ='poetry.txt'
 
# 诗集
poetrys = []
with open(poetry_file, "r", encoding='utf-8',) as f:
 for linein f:
 try:
 title, content = line.strip().split(':')
 content = content.replace(' ','')
 if '_' in contentor '(' in contentor '（' in contentor '《' in contentor '[' 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 poetryin poetrys:
 all_words += [wordfor wordin 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 poetryin 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 = 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 rowin 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 = rnn_cell.BasicRNNCell
 elif model == 'gru':
 cell_fun = rnn_cell.GRUCell
 elif model == 'lstm':
 cell_fun = rnn_cell.BasicLSTMCell
 
 cell = cell_fun(rnn_size, state_is_tuple=True)
 cell = 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.initialize_all_variables())
 
 saver = tf.train.Saver(tf.all_variables())
 saver.restore(sess, 'poetry.module-49')
 
 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())

生成的“诗词”（至少格式对上了）：

新犬随风起，一璃迹阵悲。
浅昏罢庄哉，清插去园空。
双叶坐成鉴，王妓水正苑。
鸟声不成影，胙滩朱瓮声。
无斑红芜踏，那期日正闲。
吾燕登无士，无处得赵名。

生成藏头诗：

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-7')
 
 state_ = sess.run(cell.zero_state(1, tf.float32))
 poem = ''
 i = 0
 for wordin 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'))

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