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embedding based logistic regression-神经网络逻辑回归tensorflow

时间:2017-03-16 18:36:09      阅读:264      评论:0      收藏:0      [点我收藏+]

标签:base   网络   建立   nts   表示   问题   方法   nlp   词典   

--- 灵感 --- 因为最近一直在做rnn based NLP,其中无论是什么cell,lstm, GRU或者cnn都是基于单词的embedding表示;单词的embdding就是把每个单词表示成一个向量, 然后通过bp训练这些向量的值,这种想法很奇妙,于是我尝试性的把这种思想用在logistic regression上面;

--- 问题 --- 对于logistic regression的话,很多向量都是categorial,如果碰到有1000个category怎么做?转换成1000*1的one-hot向量吗? 方法:用embedding, 每个category给一个10维的向量,然后再用传统的回归或者神经网络的方法;

--- 实验 --- 1: 数据一览; 数据来自kaggle, 是redhat那个项目,感兴趣的自己去看看; 2:方法; 标题是逻辑回归,但是本质上还是神经网络做分类;但是这个问题传统上都是用逻辑回归解决的,因为包含了很多categorial的数据,然后label是0和1,要求做分类; 运行一个logistic regression是很简单的;但是这里的问题是数据里面有个group变量和一个people向量,group大概有3k+种类,people大概有180K+种类,显然转换成dummy变量再做逻辑回归的话不合适;这里我主要是参考word embedding的思想,在tensorflow里面建立两个个词典,一个people词典一个group词典,然后训练的时候分别去查这个词典返回两个10维的实数向量,这两个实数向量就分别是people和group的特征;之后再随便弄了一点full connected的层和一些激活函数,效果不错,很快收敛到90%以上了; 3:效果; 这个数据的话,我刚开始只是想用来实验在tf.Session()的情况下怎么样batch读取tfrecords数据的,因为tfrecords数据读取的话不需要把整个数据load进去内存;之前一直用estimator的方法读tfrecords,但是用session之后似乎没有很好的解决方法; 效果还不错,主要是感觉对于多种类的问题都可以用embedding的方法来做了以后;

#encoding=utf-8
import numpy as np 
import tensorflow as tf 
import pickle
import random 
model_dir = /home/yanjianfeng/kaggle/data/model_dir/


people_dic, group_dic, dic = pickle.load(open(/home/yanjianfeng/kaggle/data/data.dump, r))
def create_train_op(loss):
    train_op = tf.contrib.layers.optimize_loss(loss = loss, 
        global_step = tf.contrib.framework.get_global_step(), 
        learning_rate = 0.1, 
        clip_gradients = 10.0, 
        optimizer = "Adam")
    return train_op 

def create_input():
    random_id = random.randint(0, len(dic[outcome])-2049)
    keys = dic.keys() 
    data = {}
    for k in keys:
        data[k] = dic[k][random_id: random_id+2048]
    return data


# 主体部分还是最好不要放在函数里面,不太容易提取出某个特定的值
# 或者直接把主体部分放在tf.Session里面比较容, 大概就是这么一个模式;


global_step = tf.Variable(0, name = global_step, trainable=False)

people_id = tf.placeholder("int64", [None])
group = tf.placeholder(int64, [None])
time = tf.placeholder(int64, [None])
peofea = tf.placeholder(int64, [None, 262])
rowfea = tf.placeholder(int64, [None, 174])
outcome = tf.placeholder("int64", [None])

name_embed = tf.get_variable(names, shape = [189120, 10])
group_embed = tf.get_variable(groups, shape = [35000, 10])
name_ = tf.nn.embedding_lookup(name_embed, people_id)
group_ = tf.nn.embedding_lookup(group_embed, group)

name_w = tf.get_variable(name_w, shape = [10, 2])
group_w = tf.get_variable(group_w, shape = [10, 5])

name_outcome = tf.matmul(name_, name_w)
group_outcome = tf.matmul(group_, group_w)

w_1 = tf.get_variable(w_1, shape = [262, 10])
w_2 = tf.get_variable(w_2, shape = [174, 10])
w_3 = tf.get_variable(w_3, shape = [1])

peofea_outcome = tf.matmul(tf.to_float(peofea), w_1)
rowfea_outcome = tf.matmul(tf.to_float(rowfea), w_2)

time_outcome = tf.mul(tf.to_float(time), w_3)
time_outcome = tf.expand_dims(time_outcome, -1)

name_outcome = tf.sigmoid(name_outcome)
group_outcome = tf.sigmoid(group_outcome)
peofea_outcome = tf.sigmoid(peofea_outcome)
rowfea_outcome = tf.sigmoid(rowfea_outcome)
time_outcome = tf.sigmoid(time_outcome)

x = tf.concat(1, [name_outcome, group_outcome, peofea_outcome, rowfea_outcome, time_outcome])

w_f = tf.get_variable(w_f, shape = [28, 28])
b = tf.get_variable(b, shape = [1])
w_f_2 = tf.get_variable(w_f_2, shape = [28, 1])

pred = tf.sigmoid(tf.matmul(x, w_f)) + b 
pred = tf.matmul(pred, w_f_2)

y = tf.expand_dims(tf.to_float(outcome), -1)

prob = tf.sigmoid(pred)
prob = tf.to_float(tf.greater(prob, 0.5))
c = tf.reduce_mean(tf.to_float(tf.equal(prob, y)))

loss = tf.nn.sigmoid_cross_entropy_with_logits(pred, y)
loss = tf.reduce_mean(loss)
train_op = create_train_op(loss)



# 这里的顺序很重要,要是在最前面用saver,则会save到最开始的情况?
saver = tf.train.Saver()
with tf.Session() as sess:

    sess.run(tf.initialize_all_variables())
    ckpt = tf.train.get_checkpoint_state(model_dir)
    if ckpt and ckpt.model_checkpoint_path:
        print the model being restored is 
        print ckpt.model_checkpoint_path 
        saver.restore(sess, ckpt.model_checkpoint_path)
        print sucesssfully restored the session

    count = global_step.eval()

    for i in range(0, 10000):
        data = create_input()
        l, _ , c_ = sess.run([loss, train_op, c], feed_dict = {people_id: data[people_id],
            group: data[group],
            time: data[time],
            peofea: data[people_features],
            rowfea: data[row_features],
            outcome: data[outcome]})
        print the loss\t + str(l) + \t\tthe count\t + str(c_)
        global_step.assign(count).eval()
        saver.save(sess, model_dir + model.ckpt, global_step = global_step)
        count += 1 

 

embedding based logistic regression-神经网络逻辑回归tensorflow

标签:base   网络   建立   nts   表示   问题   方法   nlp   词典   

原文地址:http://www.cnblogs.com/LarryGates/p/6560839.html

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