本文实例为大家分享了使用RNN进行文本分类，python代码实现，供大家参考，具体内容如下

10年积累的成都网站制作、成都做网站经验，可以快速应对客户对网站的新想法和需求。提供各种问题对应的解决方案。让选择我们的客户得到更好、更有力的网络服务。我虽然不认识你，你也不认识我。但先网站设计后付款的网站建设流程，更有盐津免费网站建设让你可以放心的选择与我们合作。

1、本博客项目由来是oxford 的nlp 深度学习课程第三周作业，作业要求使用LSTM进行文本分类。和上一篇CNN文本分类类似，本此代码风格也是仿照sklearn风格，三步走形式（模型实体化，模型训练和模型预测）但因为训练时间较久不知道什么时候训练比较理想，因此在次基础上加入了继续训练的功能。

2、构造文本分类的rnn类，(保存文件为ClassifierRNN.py)

2.1 相应配置参数因为较为繁琐，不利于阅读，因此仿照tensorflow源码形式，将代码分成 网络配置参数 nn_config 和计算配置参数： calc_config，也相应声明了其对应的类：NN_config，CALC_config。

2.2 声明 ClassifierRNN类，该类的主要函数有：（init, build_inputs, build_rnns, build_loss, build_optimizer, random_batches,fit, load_model, predict_accuracy, predict),代码如下：

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import os
import time
class NN_config(object):
 def __init__(self,num_seqs=1000,num_steps=10,num_units=128,num_classes = 8,\
    num_layers = 1,embedding_size=100,vocab_size = 10000,\
    use_embeddings=False,embedding_init=None):
  self.num_seqs = num_seqs
  self.num_steps = num_steps
  self.num_units = num_units
  self.num_classes = num_classes
  self.num_layers = num_layers
  self.vocab_size = vocab_size
  self.embedding_size = embedding_size
  self.use_embeddings = use_embeddings
  self.embedding_init = embedding_init

class CALC_config(object):
 def __init__(self,batch_size=64,num_epoches = 20,learning_rate = 1.0e-3, \
     keep_prob=0.5,show_every_steps = 10,save_every_steps=100):
  self.batch_size  = batch_size
  self.num_epoches = num_epoches
  self.learning_rate = learning_rate
  self.keep_prob  = keep_prob
  self.show_every_steps = show_every_steps
  self.save_every_steps = save_every_steps

class ClassifierRNN(object):
 def __init__(self, nn_config, calc_config):
  # assign revalent parameters
  self.num_seqs = nn_config.num_seqs
  self.num_steps = nn_config.num_steps
  self.num_units = nn_config.num_units
  self.num_layers = nn_config.num_layers
  self.num_classes = nn_config.num_classes
  self.embedding_size = nn_config.embedding_size
  self.vocab_size  = nn_config.vocab_size
  self.use_embeddings = nn_config.use_embeddings
  self.embedding_init = nn_config.embedding_init
  # assign calc ravalant values
  self.batch_size  = calc_config.batch_size
  self.num_epoches = calc_config.num_epoches
  self.learning_rate = calc_config.learning_rate
  self.train_keep_prob= calc_config.keep_prob
  self.show_every_steps = calc_config.show_every_steps
  self.save_every_steps = calc_config.save_every_steps
  # create networks models
  tf.reset_default_graph()
  self.build_inputs()
  self.build_rnns()
  self.build_loss()
  self.build_optimizer()
  self.saver = tf.train.Saver()

 def build_inputs(self):
  with tf.name_scope('inputs'):
   self.inputs = tf.placeholder(tf.int32, shape=[None,self.num_seqs],\
                name='inputs')
   self.targets = tf.placeholder(tf.int32, shape=[None, self.num_classes],\
                name='classes')
   self.keep_prob = tf.placeholder(tf.float32,name='keep_prob')
   self.embedding_ph = tf.placeholder(tf.float32, name='embedding_ph')

   if self.use_embeddings == False:
    self.embeddings = tf.Variable(tf.random_uniform([self.vocab_size,\
        self.embedding_size],-0.1,0.1),name='embedding_flase') 
    self.rnn_inputs = tf.nn.embedding_lookup(self.embeddings,self.inputs)
   else:
    embeddings = tf.Variable(tf.constant(0.0,shape=[self.vocab_size,self.embedding_size]),\
               trainable=False,name='embeddings_true')
    self.embeddings = embeddings.assign(self.embedding_ph)
    self.rnn_inputs = tf.nn.embedding_lookup(self.embeddings,self.inputs)
    print('self.rnn_inputs.shape:',self.rnn_inputs.shape)

 def build_rnns(self):
  def get_a_cell(num_units,keep_prob):
   rnn_cell = tf.contrib.rnn.BasicLSTMCell(num_units=num_units)
   drop = tf.contrib.rnn.DropoutWrapper(rnn_cell, output_keep_prob=keep_prob)
   return drop
  with tf.name_scope('rnns'):
   self.cell = tf.contrib.rnn.MultiRNNCell([get_a_cell(self.num_units,self.keep_prob) for _ in range(self.num_layers)]) 
   self.initial_state = self.cell.zero_state(self.batch_size,tf.float32)
   self.outputs, self.final_state = tf.nn.dynamic_rnn(self.cell,tf.cast(self.rnn_inputs,tf.float32),\
    initial_state = self.initial_state )
   print('rnn_outputs',self.outputs.shape)

 def build_loss(self):
  with tf.name_scope('loss'):
   self.logits = tf.contrib.layers.fully_connected(inputs = tf.reduce_mean(self.outputs, axis=1), \
           num_outputs = self.num_classes, activation_fn = None)
   print('self.logits.shape:',self.logits.shape)
   self.cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=self.logits,\
          labels = self.targets))
   print('self.cost.shape',self.cost.shape)
   self.predictions = self.logits
   self.correct_predictions = tf.equal(tf.argmax(self.predictions, axis=1), tf.argmax(self.targets, axis=1))
   self.accuracy = tf.reduce_mean(tf.cast(self.correct_predictions,tf.float32))
   print(self.cost.shape)
   print(self.correct_predictions.shape)

 def build_optimizer(self):
  with tf.name_scope('optimizer'):
   self.optimizer = tf.train.AdamOptimizer(self.learning_rate).minimize(self.cost)

 def random_batches(self,data,shuffle=True):
  data = np.array(data)
  data_size = len(data)
  num_batches_per_epoch = int(data_size/self.batch_size)

  #del data
  for epoch in range(self.num_epoches):
   if shuffle :
    shuffle_index = np.random.permutation(np.arange(data_size))
    shuffled_data = data[shuffle_index]
   else:
    shuffled_data = data  
   for batch_num in range(num_batches_per_epoch):
    start = batch_num * self.batch_size
    end = min(start + self.batch_size,data_size)
    yield shuffled_data[start:end] 

 def fit(self,data,restart=False):
  if restart :
   self.load_model()
  else:
   self.session = tf.Session()
   self.session.run(tf.global_variables_initializer())
  with self.session as sess:   
   step = 0
   accuracy_list = []
   # model saving
   save_path = os.path.abspath(os.path.join(os.path.curdir, 'models'))     
   if not os.path.exists(save_path):
    os.makedirs(save_path)   
   plt.ion()
   #new_state = sess.run(self.initial_state)
   new_state = sess.run(self.initial_state)
   batches = self.random_batches(data)
   for batch in batches:
    x,y = zip(*batch)
    x = np.array(x)
    y = np.array(y)
    print(len(x),len(y),step)
    step += 1
    start = time.time()
    if self.use_embeddings == False:
     feed = {self.inputs :x,
      self.targets:y,
      self.keep_prob : self.train_keep_prob,
      self.initial_state: new_state}
    else:
     feed = {self.inputs :x,
      self.targets:y,
      self.keep_prob : self.train_keep_prob,
      self.initial_state: new_state,
      self.embedding_ph: self.embedding_init}    
    batch_loss, new_state, batch_accuracy , _ = sess.run([self.cost,self.final_state,\
               self.accuracy, self.optimizer],feed_dict = feed)
    end = time.time()
    accuracy_list.append(batch_accuracy)
    # control the print lines
    if step%self.show_every_steps == 0:
     print('steps/epoch:{}/{}...'.format(step,self.num_epoches),
      'loss:{:.4f}...'.format(batch_loss),
      '{:.4f} sec/batch'.format((end - start)),
      'batch_Accuracy:{:.4f}...'.format(batch_accuracy)
      )
     plt.plot(accuracy_list)
     plt.pause(0.5)
    if step%self.save_every_steps == 0:
     self.saver.save(sess,os.path.join(save_path, 'model') ,global_step = step)
   self.saver.save(sess, os.path.join(save_path, 'model'), global_step=step) 

 def load_model(self, start_path=None):
  if start_path == None:
   model_path = os.path.abspath(os.path.join(os.path.curdir,"models"))
   ckpt = tf.train.get_checkpoint_state(model_path)
   path = ckpt.model_checkpoint_path
   print("this is the start path of model:",path)
   self.session = tf.Session()
   self.saver.restore(self.session, path)
   print("Restored model parameters is complete!")

  else:
   self.session = tf.Session()
   self.saver.restore(self.session,start_path)
   print("Restored model parameters is complete!")

 def predict_accuracy(self,data,test=True):
  # loading_model
  self.load_model()
  sess = self.session
  iterations = 0
  accuracy_list = []
  predictions = []
  epoch_temp = self.num_epoches
  self.num_epoches = 1
  batches = self.random_batches(data,shuffle=False)
  for batch in batches:
   iterations += 1
   x_inputs, y_inputs = zip(*batch)
   x_inputs = np.array(x_inputs)
   y_inputs = np.array(y_inputs)
   if self.use_embeddings == False:
    feed = {self.inputs: x_inputs,
      self.targets: y_inputs,
      self.keep_prob: 1.0}   
   else:
    feed = {self.inputs: x_inputs,
      self.targets: y_inputs,
      self.keep_prob: 1.0,
      self.embedding_ph: self.embedding_init}   
   to_train = [self.cost, self.final_state, self.predictions,self.accuracy]
   batch_loss,new_state,batch_pred,batch_accuracy = sess.run(to_train, feed_dict = feed)
   accuracy_list.append(np.mean(batch_accuracy))
   predictions.append(batch_pred)
   print('The trainning step is {0}'.format(iterations),\
     'trainning_accuracy: {:.3f}'.format(accuracy_list[-1]))    

  accuracy = np.mean(accuracy_list)
  predictions = [list(pred) for pred in predictions]
  predictions = [p for pred in predictions for p in pred]
  predictions = np.array(predictions)
  self.num_epoches = epoch_temp
  if test :
   return predictions, accuracy
  else:
   return accuracy    

 def predict(self, data):
  # load_model
  self.load_model()
  sess = self.session
  iterations = 0
  predictionss = []
  epoch_temp = self.num_epoches
  self.num_epoches = 1
  batches = self.random_batches(data)
  for batch in batches:
   x_inputs = batch
   if self.use_embeddings == False:
    feed = {self.inputs : x_inputs,
      self.keep_prob:1.0}
   else:
    feed = {self.inputs : x_inputs,
      self.keep_prob:1.0,
      self.embedding_ph: self.embedding_init}  
   batch_pred = sess.run([self.predictions],feed_dict=feed)
   predictions.append(batch_pred)

  predictions = [list(pred) for pred in predictions]
  predictions = [p for pred in predictions for p in pred]
  predictions = np.array(predictions) 
  return predictions

另外有需要云服务器可以了解下创新互联scvps.cn，海内外云服务器15元起步，三天无理由+7*72小时售后在线，公司持有idc许可证，提供“云服务器、裸金属服务器、高防服务器、香港服务器、美国服务器、虚拟主机、免备案服务器”等云主机租用服务以及企业上云的综合解决方案，具有“安全稳定、简单易用、服务可用性高、性价比高”等特点与优势，专为企业上云打造定制，能够满足用户丰富、多元化的应用场景需求。

分享文章：python使用RNN实现文本分类-创新互联
网站网址：http://csdahua.cn/article/edcps.html