1 # -*- coding: utf-8 -*- 2 """ 3 Created on Thu Mar 9 19:20:51 2017 4 5 @author: Jarvis 6 """ 7 ''' 8 tnesorflow 做机器学习的几个步骤 9 1.定义公式 10 2.定义loss function，选择优化器，并制定优化器优化loss 11 3.迭代地对数据进行训练 12 4。在测试集或验证集对准确率进行评测 13 14 15 ''' 16 17 import tensorflow as tf 18 import pandas as pd 19 import random 20 #自己定义的一个选取batch进行训练的一个取batch函数 21 def next_batch(mnist, num,ilen = 55): 22 size = len(mnist) 23 selected_n = set([]) 24 25 while(len(selected_n) < num): 26 t = random.choice(range(size)) 27 selected_n.add(t) 28 l = list(selected_n) 29 30 batch_xs = [] 31 batch_ys = [] 32 33 batch_xs = mnist.iloc[l,range(2,54)] 34 35 batch_ys = mnist.iloc[l,range(54,62)] 36 return batch_xs,batch_ys 37 38 39 #对数据进行读取 40 org_mnist = pd.read_csv("NDVI_NDWI.csv",header = None,encoding = 'gbk') 41 mnist = pd.get_dummies(org_mnist) 42 #创建session 43 #input_data.read_data_sets("MNIST_data/",one_hot = True) 44 sess = tf.InteractiveSession() 45 46 47 #定义算法公式，在此处就是神经网络的结构方式 48 in_units = 52#每一条instance具有52个输入 49 h1_units = 30 50 h2_units = 20 51 h3_units = 10 52 h4_units = 5 53 54 #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 55 W1 = tf.Variable(tf.truncated_normal([in_units,h1_units],stddev = 0.1)) 56 b1 = tf.Variable(tf.zeros([h1_units])) 57 W2 = tf.Variable(tf.zeros([h1_units,h2_units]))#[h1_units,8] 58 b2 = tf.Variable(tf.zeros([h2_units]))#10 59 W3 = tf.Variable(tf.zeros([h2_units,h3_units])) 60 b3 = tf.Variable(tf.zeros([h3_units])) 61 W4 = tf.Variable(tf.zeros([h3_units,8])) 62 b4 = tf.Variable(tf.zeros([8])) 63 64 ''' 65 W4 = tf.Variable(tf.zeros([h3_units,h4_units])) 66 b4 = tf.Variable(tf.zeros([h4_units])) 67 W5 = tf.Variable(tf.zeros([h4_units,8])) 68 b5 = tf.Variable(tf.zeros([8])) 69 ''' 70 x = tf.placeholder(tf.float32,[None, in_units]) 71 keep_prob = tf.placeholder(tf.float32)#dropout 的比例 keep_prob 72 73 hidden1 = tf.nn.sigmoid(tf.matmul(x,W1)+b1) 74 hidden1_drop = tf.nn.dropout(hidden1,keep_prob) 75 hidden2 = tf.nn.sigmoid(tf.matmul(hidden1_drop,W2)+b2) 76 hidden2_drop = tf.nn.dropout(hidden2,keep_prob) 77 hidden3 = tf.nn.sigmoid(tf.matmul(hidden2_drop,W3)+b3) 78 hidden3_drop = tf.nn.dropout(hidden3,keep_prob) 79 #hidden4 = tf.nn.sigmoid(tf.matmul(hidden3_drop,W4)+b4) 80 #hidden4_drop = tf.nn.dropout(hidden4,keep_prob) 81 82 y = tf.nn.softmax(tf.matmul(hidden3_drop,W4)+b4) 83 y_ = tf.placeholder(tf.float32,[None,8])#[None,10] 84 #设置优化函数 85 cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y),reduction_indices=[1])) 86 train_step = tf.train.AdagradOptimizer(0.3).minimize(cross_entropy) 87 88 tf.global_variables_initializer().run() 89 90 91 for i in range(2010):#2000 92 batch_xs, batch_ys = next_batch(mnist,1000)#1000 3 93 train_step.run( {x : batch_xs, y_ : batch_ys,keep_prob: 1}) 94 95 correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) 96 accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) 97 batch_xs, batch_ys = next_batch(mnist,10000) 98 print(accuracy.eval({x:batch_xs,y_:batch_ys,keep_prob:1.0}))