tensorflow 学习笔记 多层感知机

 # -*- coding: utf-8 -*-
 """
 Created on Thu Mar  9 19:20:51 2017

 @author: Jarvis
 """
 '''
 tnesorflow 做机器学习的几个步骤
 1.定义公式
 2.定义loss function，选择优化器，并制定优化器优化loss
 3.迭代地对数据进行训练
 4。在测试集或验证集对准确率进行评测

 '''

 import tensorflow as tf
 import pandas as pd
 import random
 #自己定义的一个选取batch进行训练的一个取batch函数
 def next_batch(mnist, num,ilen = 55):
     size = len(mnist)
     selected_n = set([])

     while(len(selected_n) < num):
         t = random.choice(range(size))
         selected_n.add(t)
     l  = list(selected_n)

     batch_xs = []
     batch_ys = []

     batch_xs = mnist.iloc[l,range(2,54)]

     batch_ys = mnist.iloc[l,range(54,62)]
     return batch_xs,batch_ys

 #对数据进行读取
 org_mnist = pd.read_csv("NDVI_NDWI.csv",header = None,encoding = 'gbk')
 mnist = pd.get_dummies(org_mnist)
 #创建session
 #input_data.read_data_sets("MNIST_data/",one_hot = True)
 sess = tf.InteractiveSession()

 #定义算法公式，在此处就是神经网络的结构方式
 in_units = 52#每一条instance具有52个输入
 h1_units = 30
 h2_units = 20
 h3_units = 10
 h4_units = 5

 #tf.truncated_normal是正态分布的一个东东，主要用于初始化一些W矩阵
 W1 = tf.Variable(tf.truncated_normal([in_units,h1_units],stddev = 0.1))
 b1 = tf.Variable(tf.zeros([h1_units]))
 W2 = tf.Variable(tf.zeros([h1_units,h2_units]))#[h1_units,8]
 b2 = tf.Variable(tf.zeros([h2_units]))#
 W3 = tf.Variable(tf.zeros([h2_units,h3_units]))
 b3 = tf.Variable(tf.zeros([h3_units]))
 W4 = tf.Variable(tf.zeros([h3_units,8]))
 b4 = tf.Variable(tf.zeros([8]))

 '''
 W4 = tf.Variable(tf.zeros([h3_units,h4_units]))
 b4 = tf.Variable(tf.zeros([h4_units]))
 W5 = tf.Variable(tf.zeros([h4_units,8]))
 b5 = tf.Variable(tf.zeros([8]))
 '''
 x = tf.placeholder(tf.float32,[None, in_units])
 keep_prob = tf.placeholder(tf.float32)#dropout 的比例 keep_prob

 hidden1 = tf.nn.sigmoid(tf.matmul(x,W1)+b1)
 hidden1_drop = tf.nn.dropout(hidden1,keep_prob)
 hidden2 = tf.nn.sigmoid(tf.matmul(hidden1_drop,W2)+b2)
 hidden2_drop = tf.nn.dropout(hidden2,keep_prob)
 hidden3 = tf.nn.sigmoid(tf.matmul(hidden2_drop,W3)+b3)
 hidden3_drop = tf.nn.dropout(hidden3,keep_prob)
 #hidden4 = tf.nn.sigmoid(tf.matmul(hidden3_drop,W4)+b4)
 #hidden4_drop = tf.nn.dropout(hidden4,keep_prob)

 y = tf.nn.softmax(tf.matmul(hidden3_drop,W4)+b4)
 y_ = tf.placeholder(tf.float32,[None,8])#[None,10]
 #设置优化函数
 cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y),reduction_indices=[1]))
 train_step = tf.train.AdagradOptimizer(0.3).minimize(cross_entropy)

 tf.global_variables_initializer().run()

 for i in range(2010):#
     batch_xs, batch_ys = next_batch(mnist,1000)#1000 3
     train_step.run( {x : batch_xs, y_ : batch_ys,keep_prob: 1})

 correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
 accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
 batch_xs, batch_ys = next_batch(mnist,10000)
 print(accuracy.eval({x:batch_xs,y_:batch_ys,keep_prob:1.0}))