承前
接上节代码『TensotFlow』RNN中文文本_上，
import numpy as np
import tensorflow as tf
from collections import Counter poetry_file = 'poetry.txt' poetrys = []
with open(poetry_file, 'r', encoding='utf-8') as f:
for line in f:
try:
title, content = line.strip().split(':')
content = content.replace(' ','') # 去空格，实际上没用到
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 # 依照每个元素的长度排序
poetrys = sorted(poetrys, key=lambda poetry: len(poetry))
print('唐诗数量：', len(poetrys)) # 统计字出现次数
all_words = []
for poetry in poetrys:
all_words += [word for word in poetry]
counter = Counter(all_words)
# print(counter.items())
# item会把字典中的每一项变成一个2元素元组，字典变成大list
count_pairs = sorted(counter.items(), key=lambda x:-x[1])
# 利用zip提取，因为是原生数据结构，在切片上远不如numpy的结构灵活
words, _ = zip(*count_pairs)
# print(words) words = words[:len(words)] + (' ',) # 后面要用' '来补齐诗句长度
# print(words)
# 转换为字典
word_num_map = dict(zip(words, range(len(words))))
# 把诗词转换为向量
to_num = lambda word: word_num_map.get(word, len(words))
poetry_vector = [list(map(to_num, poetry)) for poetry in poetrys] batch_size = 1
n_chunk = len(poetry_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 = poetry_vector[start_index:end_index]
length = max(map(len, batches)) # 记录下最长的诗句的长度
xdata = np.full((batch_size, length), word_num_map[' '], np.int32)
for row in range(batch_size):
xdata[row,:len(batches[row])] = batches[row]
# print(len(xdata[0])) 每个batch中数据长度不相等
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) # (n_chunk, batch, length)
y_batches.append(ydata)
这里将数据预处理为3维的数据结构，每次输入后两维度，并将最后的每一个数字映射为一个数组，这是承袭上节的数据处理逻辑结构。
然后我们来看RNN部分。
启后
input_data = tf.placeholder(tf.int32, [batch_size, None])
output_targets = tf.placeholder(tf.int32, [batch_size, None])
下面是RNN网络主体，为了深化对与数据在RNN中流动的理解，我把中间的数据维度进行了输出，注意，
- 由于我在测试时batch_size设定为1，所以下面的1表示的是batch_size
- RNN网络的特性决定了不同的batch之间的time_steps可以不相等，但同一个batch中的必须相等，所以输出?，对应的placeholder中的相应维度输入None
- 这里对应的输入数据维度input_data：（1，？），output_target：（1，？）
# 单层RNN
def neural_network(model='lstm',rnn_size=128,num_layers=2): cell = tf.contrib.rnn.BasicLSTMCell(rnn_size,state_is_tuple=True)
# cell = tf.contrib.rnn.MultiRNNCell([cell for _ in range(num_layers)]) 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])
# print(softmax_w) # 128,6111
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) # print(input_data) # 1,?
# print(inputs) # 1,?,128 outputs,last_state = tf.nn.dynamic_rnn(cell,inputs,initial_state=initial_state,scope='rnnlm')
output = tf.reshape(outputs,[-1,rnn_size])
# print(outputs) # 1,?,128
# print(output) # ?,128 # ?,128 * 128,6111 -> ?,6111
logits = tf.matmul(output,softmax_w) + softmax_b
probs = tf.nn.softmax(logits)
return logits,last_state
训练部分相关函数在『TensorFlow』梯度优化相关中均有介绍，当然这里采用了比较麻烦的做法... ...练习么，
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)])
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())
saver = tf.train.Saver() 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 n % 5000 == 1:
# saver.save(sess,'poetry.module',global_step=epoch)
saver.save(sess,'./model.ckpt',global_step=epoch) # if epoch % 1 == 0:
# #saver.save(sess,'poetry.module',global_step=epoch)
# saver.save(sess,'./model/model.ckpt',global_step=epoch)
train_neural_network()
实际训练不要忘记把batch_size改大一点，只是个训练程序，没什么其他的补充了。
下面给出利用模型生成文本的部分，
思路是指定初始字符串'['，转换为向量后送入RNN，得到state和下一个字符，利用他们两个进行后续迭代，直到']'出现，生成的字符串即为结果。
def gen_poetry():
def to_word(weights):
t = np.cumsum(weights)
s = np.sum(weights)
sample = np.searchsorted(t,(np.random.rand(1) * s)[0])
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')
ckpt = tf.train.get_checkpoint_state('./')
saver = tf.train.import_meta_graph(ckpt.model_checkpoint_path + '.meta')
saver.restore(sess,ckpt.model_checkpoint_path) state_ = sess.run(cell.zero_state(1,tf.float32)) x = np.array([list(map(word_num_map.get,'['))])
print(x)
[probs_,state_] = sess.run([probs,last_state],feed_dict={input_data: x,initial_state: state_})
print(probs_.shape)
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_)]
print(poem)
return poem print(gen_poetry())
按照正常思路应该使用word = words[np.argmax(probs_)]来还原字符，但这样不收敛（不出现’]‘导致诗句不能够正常结束），所以有上面的另一种生成字符串的方法，不过由于手头没有高性能电脑（迭代次数不够），多层RNN的尝试也不太成功（网络性能不佳），所以也不能说孰优孰劣，给出一个似乎比较成功的例子，Tensorflow-3-使用RNN生成中文小说可以作为日后继续探究的参考（多层RNN的构建&向量还原字符串的方法&更为复杂的中文文本的预处理方法）。
后记
明天就回北京了，这大概就是研究生报道（九月三号）前的最后一篇博客了，回想起4月份至今，学到了不少东西，再回想其去年7月份至今，也真的是有很多成功的尝试，当然也有不少失败的努力，不过即使这样我感觉也蛮不错了，开学后很有可能会被学了4年的计算机本专业的同学吊打，所以我要提前警戒自己的就是既然在这个领域上你能做到的不多，那么就不要贪心勉强，选定一个目标，不要迷茫，或者即使迷茫也不能驻足，前进和不断前进，既然你能做到的事不多，那就把它做得登峰造极。虽然很理想化，但我始终坚信，一个还算合理的大方向规划加上孜孜不倦的努力是可以得到好的结果的，而瓶颈就在于那个说起来容易的‘孜孜不倦’上，我不相信奇迹，或者说我不相信我有得到奇迹的好运气，那么就尽量让所谓的好结果来的水到渠成一点吧，加油，我看好你，不要让我失望。