import tensorflow_pass_warning_wgs
import tensorflow as tf, numpy
import tensorflow.contrib as con
tf.set_random_seed(1)classmodel():def__init__(self, sentence):
self.sentence = sentence
self.sequnec_length =10# 序列长度
self.X_data, self.Y_data = self.get_data(sentence)
self.hidden_size =50# 隐藏神经元数量
self.num_classes =len(self.idx2char)
self.batch_size =len(self.X_data)defget_data(self, sentence):# 词集向量
self.idx2char =list(set(sentence))# 转字典
char_dict ={
w: i for i, w inenumerate(self.idx2char)}# 构造数据集
x_data, y_data =[],[]for i inrange(0,len(sentence)- self.sequnec_length):
x_str = sentence[i: i + self.sequnec_length]# 措开取
y_str = sentence[i +1: i + self.sequnec_length +1]print(i, x_str,'->', y_str)# 词袋模型
x, y =[char_dict[c]for c in x_str],[char_dict[c]for c in y_str]
x_data.append(x)
y_data.append(y)return x_data, y_data
# 训练deftrain(self):
X, Y = tf.placeholder(tf.int32,[None, self.sequnec_length]), tf.placeholder(tf.int32,[None, self.sequnec_length])
X_oneHot = tf.one_hot(X, self.num_classes)
cells =[con.rnn.LSTMCell(num_units=self.hidden_size)for _ inrange(2)]# 深层RNN，多个RNN基础单元
mul_cells = con.rnn.MultiRNNCell(cells)# 堆叠RNN基础单元
outputs, state = tf.nn.dynamic_rnn(mul_cells, X_oneHot, dtype=tf.float32)
outputs = tf.reshape(outputs, shape=[-1, self.hidden_size])
logits = con.layers.fully_connected(outputs, self.num_classes, activation_fn=None)# 全连接
logits = tf.reshape(logits, shape=[self.batch_size, self.sequnec_length, self.num_classes])
weight = tf.ones(shape=[self.batch_size, self.sequnec_length])
cost = tf.reduce_mean(con.seq2seq.sequence_loss(logits=logits, targets=Y, weights=weight))
optimizer = tf.train.AdamOptimizer(0.1).minimize(cost)with tf.Session()as sess:
sess.run(tf.global_variables_initializer())for i inrange(500):
c_, lo_, _ = sess.run([cost, logits, optimizer], feed_dict={
X: self.X_data, Y: self.Y_data})for j, res inenumerate(lo_):
index = numpy.argmax(res,1)if j ==0:
ret =''.join([self.idx2char[c]for c in index])else:
ret += self.idx2char[index[-1]]print(i, c_, ret)if ret == self.sentence[1:]:break
sentence =("if you want to build a ship, don't drum up people together to ""collect wood and don't assign them tasks and work, but rather ""teach them to long for the endless immensity of the sea.")
m = model(sentence)
m.train()
普通方法
from __future__ import print_function
import tensorflow as tf
import numpy as np
from tensorflow.contrib import rnn
import tensorflow.contrib as rnnn
from tensorflow.python.ops.rnn import dynamic_rnn
tf.set_random_seed(777)# reproducibility
sentence =("if you want to build a ship, don't drum up people together to ""collect wood and don't assign them tasks and work, but rather ""teach them to long for the endless immensity of the sea.")
char_set =list(set(sentence))# print(len(char_set)) #25
char_dic ={
w: i for i, w inenumerate(char_set)}
hidden_size =50# len(char_set) #25
sequence_length =10# Any arbitrary number
data_dim =len(char_set)# 25
num_classes =len(char_set)# 25
learning_rate =0.1# 构造数据集
dataX =[]
dataY =[]for i inrange(0,len(sentence)- sequence_length):
x_str = sentence[i:i + sequence_length]
y_str = sentence[i +1: i + sequence_length +1]print(i, x_str,'->', y_str)
x =[char_dic[c]for c in x_str]# x str to index 字符转数字
y =[char_dic[c]for c in y_str]# y str to index
dataX.append(x)
dataY.append(y)
batch_size =len(dataX)# 170print(batch_size)
X = tf.placeholder(tf.int32,[None, sequence_length])
Y = tf.placeholder(tf.int32,[None, sequence_length])
X_one_hot = tf.one_hot(X, num_classes)# 独热编码 #print(X_one_hot) (?, 10, 25)# 建一个有隐藏单元的LSTM，Make a lstm cell with hidden_size (each unit output vector size)defcell():# cell = rnn.BasicLSTMCell(hidden_size, state_is_tuple=True)# cell = rnn.GRUCell(hidden_size)
cell = rnn.LSTMCell(hidden_size, state_is_tuple=True)return cell
multi_cells = rnn.MultiRNNCell([cell()for _ inrange(2)], state_is_tuple=True)# outputs:展开隐藏层 unfolding size x hidden size, state = hidden size
outputs, _states = dynamic_rnn(multi_cells, X_one_hot, dtype=tf.float32)# 全连接层FC layer
X_for_fc = tf.reshape(outputs,[-1, hidden_size])
outputs = tf.contrib.layers.fully_connected(X_for_fc, num_classes, activation_fn=None)# outputs = rnnn.layers.fully_connected(X_for_fc,num_classes,activation_fn=None)# print(outputs.shape) #(?,25)# 改变维度准备计算序列损失reshape out for sequence_loss
outputs = tf.reshape(outputs,[batch_size, sequence_length, num_classes])# (170, 10, 25)
weights = tf.ones([batch_size, sequence_length])# 所有的权重都是1 All weights are 1 (equal weights)# 计算损失值
sequence_loss = tf.contrib.seq2seq.sequence_loss(logits=outputs, targets=Y, weights=weights)# sequence_loss = rnnn.seq2seq.sequence_loss(logits=outputs,targets=Y,weights=weights)
mean_loss = tf.reduce_mean(sequence_loss)
train_op = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(mean_loss)
sess = tf.Session()
sess.run(tf.global_variables_initializer())for i inrange(500):# (500)
_, lossval, results = sess.run([train_op, mean_loss, outputs], feed_dict={
X: dataX, Y: dataY})# print(results.shape) (170,10,25)# if i == 49:# for j, result in enumerate(results): #j:[0,170) result:(10,25)# index = np.argmax(result, axis=1)# print(i, j, ''.join([char_set[t] for t in index]), l)
results = sess.run(outputs, feed_dict={
X: dataX})# (170,10,25)for j, result inenumerate(results):
index = np.argmax(result, axis=1)# print('----------------------------------------')# print(index)if j is0:# 第一个结果10个字符组成一个句子 print all for the first result to make a sentence
ret =''.join([char_set[t]for t in index])else:# 其它取最后一个字符
ret = ret + char_set[index[-1]]print(i, lossval, ret)if ret == sentence[1:]:break# #输出每个结果的最后一个字符检测效果 Let's print the last char of each result to check it works# results = sess.run(outputs, feed_dict={X: dataX}) #(170,10,25)# for j, result in enumerate(results):# index = np.argmax(result, axis=1)# if j is 0: #第一个结果10个字符组成一个句子 print all for the first result to make a sentence# # print(''.join([char_set[t] for t in index]), end='')# ret =''.join([char_set[t] for t in index])# else: #其它取最后一个字符# # print(char_set[index[-1]], end='')# ret = ret + char_set[index[-1]]