自媒体素材视频网站,四位一体网站开发,交互式网站和非交互式网站,获得网站所有关键字逻辑回归实现多分类任务#xff08;pythonTensorFlowmnist#xff09; 逻辑回归是统计学中的一种经典方法#xff0c;虽然叫回归#xff0c;但在机器学习领域#xff0c;逻辑回归通常情况下当成一个分类任务#xff0c;softmax就是由其演变而来#xff0c;逻辑回归一般用…逻辑回归实现多分类任务pythonTensorFlowmnist 逻辑回归是统计学中的一种经典方法虽然叫回归但在机器学习领域逻辑回归通常情况下当成一个分类任务softmax就是由其演变而来逻辑回归一般用于二分类任务但通过softmax可以轻易的扩展至多分类任务。 下面的程序通过逻辑回归实现对手写数字的分类 环境python3.6、TensorFlow框架数据集MNIST # 逻辑回归实现手写数字分类 import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data import numpy as np import matplotlib.pyplot as pltmnist input_data.read_data_sets(D:\MNIST_data, one_hotTrue)x tf.placeholder(tf.float32, [None, 784]) y tf.placeholder(tf.float32, [None, 10]) W tf.Variable(tf.zeros([784, 10]), tf.float32) b tf.Variable(tf.zeros([10]), tf.float32) pre tf.nn.softmax(tf.matmul(x, W) b) # cost function cost tf.reduce_mean(tf.reduce_sum(-y*tf.log(pre), reduction_indices1)) train tf.train.GradientDescentOptimizer(0.01).minimize(cost) pred tf.equal(tf.argmax(y, 1), tf.argmax(pre, 1)) # accuracy acc tf.reduce_mean(tf.cast(pred, tf.float32))# initializer init tf.global_variables_initializer() batch_size 100 display_step 5 step 100 with tf.Session() as sess:sess.run(init)num_batch int(mnist.train.num_examples/batch_size)for i in range(step):for j in range(num_batch):batch_xs, batch_ys mnist.train.next_batch(100)sess.run(train, feed_dict{x: batch_xs, y: batch_ys})if i % display_step 0:train_acc sess.run(acc, feed_dict{x: batch_xs, y: batch_ys})test_acc sess.run(acc, feed_dict{x: mnist.test.images, y: mnist.test.labels})print(Train accuracy:%2f% train_acc, Test accuracy:%2f % test_acc) 训练100次之后的测试精度为 Train accuracy:0.940000 Test accuracy:0.921500