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
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)
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