使用tensorflow怎么实现逻辑回归模型-创新互联
这期内容当中小编将会给大家带来有关使用tensorflow怎么实现逻辑回归模型,文章内容丰富且以专业的角度为大家分析和叙述,阅读完这篇文章希望大家可以有所收获。
站在用户的角度思考问题,与客户深入沟通,找到双河网站设计与双河网站推广的解决方案,凭借多年的经验,让设计与互联网技术结合,创造个性化、用户体验好的作品,建站类型包括:成都网站设计、做网站、企业官网、英文网站、手机端网站、网站推广、申请域名、虚拟主机、企业邮箱。业务覆盖双河地区。逻辑回归模型
逻辑回归是应用非常广泛的一个分类机器学习算法,它将数据拟合到一个logit函数(或者叫做logistic函数)中,从而能够完成对事件发生的概率进行预测。
import numpy as np import tensorflow as tf import matplotlib.pyplot as plt from tensorflow.examples.tutorials.mnist import input_data #下载好的mnist数据集存在F:/mnist/data/中 mnist = input_data.read_data_sets('F:/mnist/data/',one_hot = True) print(mnist.train.num_examples) print(mnist.test.num_examples) trainimg = mnist.train.images trainlabel = mnist.train.labels testimg = mnist.test.images testlabel = mnist.test.labels print(type(trainimg)) print(trainimg.shape,) print(trainlabel.shape,) print(testimg.shape,) print(testlabel.shape,) nsample = 5 randidx = np.random.randint(trainimg.shape[0],size = nsample) for i in randidx: curr_img = np.reshape(trainimg[i,:],(28,28)) curr_label = np.argmax(trainlabel[i,:]) plt.matshow(curr_img,cmap=plt.get_cmap('gray')) plt.title(""+str(i)+"th Training Data"+"label is"+str(curr_label)) print(""+str(i)+"th Training Data"+"label is"+str(curr_label)) plt.show() x = tf.placeholder("float",[None,784]) y = tf.placeholder("float",[None,10]) W = tf.Variable(tf.zeros([784,10])) b = tf.Variable(tf.zeros([10])) # actv = tf.nn.softmax(tf.matmul(x,W)+b) #计算损失 cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(actv),reduction_indices=1)) #学习率 learning_rate = 0.01 #随机梯度下降 optm = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #求1位置索引值 对比预测值索引与label索引是否一样,一样返回True pred = tf.equal(tf.argmax(actv,1),tf.argmax(y,1)) #tf.cast把True和false转换为float类型 0,1 #把所有预测结果加在一起求精度 accr = tf.reduce_mean(tf.cast(pred,"float")) init = tf.global_variables_initializer() """ #测试代码 sess = tf.InteractiveSession() arr = np.array([[31,23,4,24,27,34],[18,3,25,4,5,6],[4,3,2,1,5,67]]) #返回数组的维数 2 print(tf.rank(arr).eval()) #返回数组的行列数 [3 6] print(tf.shape(arr).eval()) #返回数组中每一列中大元素的索引[0 0 1 0 0 2] print(tf.argmax(arr,0).eval()) #返回数组中每一行中大元素的索引[5 2 5] print(tf.argmax(arr,1).eval()) J""" #把所有样本迭代50次 training_epochs = 50 #每次迭代选择多少样本 batch_size = 100 display_step = 5 sess = tf.Session() sess.run(init) #循环迭代 for epoch in range(training_epochs): avg_cost = 0 num_batch = int(mnist.train.num_examples/batch_size) for i in range(num_batch): batch_xs,batch_ys = mnist.train.next_batch(batch_size) sess.run(optm,feed_dict = {x:batch_xs,y:batch_ys}) feeds = {x:batch_xs,y:batch_ys} avg_cost += sess.run(cost,feed_dict = feeds)/num_batch if epoch % display_step ==0: feeds_train = {x:batch_xs,y:batch_ys} feeds_test = {x:mnist.test.images,y:mnist.test.labels} train_acc = sess.run(accr,feed_dict = feeds_train) test_acc = sess.run(accr,feed_dict = feeds_test) #每五个epoch打印一次信息 print("Epoch:%03d/%03d cost:%.9f train_acc:%.3f test_acc: %.3f" %(epoch,training_epochs,avg_cost,train_acc,test_acc)) print("Done")
程序训练结果如下:
Epoch:000/050 cost:1.177228655 train_acc:0.800 test_acc: 0.855 Epoch:005/050 cost:0.440933891 train_acc:0.890 test_acc: 0.894 Epoch:010/050 cost:0.383387268 train_acc:0.930 test_acc: 0.905 Epoch:015/050 cost:0.357281335 train_acc:0.930 test_acc: 0.909 Epoch:020/050 cost:0.341473956 train_acc:0.890 test_acc: 0.913 Epoch:025/050 cost:0.330586549 train_acc:0.920 test_acc: 0.915 Epoch:030/050 cost:0.322370980 train_acc:0.870 test_acc: 0.916 Epoch:035/050 cost:0.315942993 train_acc:0.940 test_acc: 0.916 Epoch:040/050 cost:0.310728854 train_acc:0.890 test_acc: 0.917 Epoch:045/050 cost:0.306357428 train_acc:0.870 test_acc: 0.918 Done
上述就是小编为大家分享的使用tensorflow怎么实现逻辑回归模型了,如果刚好有类似的疑惑,不妨参照上述分析进行理解。如果想知道更多相关知识,欢迎关注创新互联行业资讯频道。
分享标题:使用tensorflow怎么实现逻辑回归模型-创新互联
当前路径:http://pwwzsj.com/article/dphcps.html