Tensorflow训练第一个线性回归分类器

具体的python代码如下

-- coding: utf-8 --

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Created on Sun April 09 13:17:30 2017

@author: Zizhang Wu
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数据加载

import input_data
mnist = input_data.read_data_sets(“MNIST_data/”, one_hot=True)

设置占位符，用None 来表示此张量的第一个维度可以是任意长度的

import tensorflow as tf
x = tf.placeholder(“float”, [None, 784])

线性回归模型参数初始化为0

W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))

选取交叉熵作为损失函数，使用梯度下降法来进行优化

y = tf.nn.softmax(tf.matmul(x,W) + b)
y_ = tf.placeholder(“float”, [None,10])
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

初始化变量与会话

init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)

让模型训练1000次，batch_size设置为100

for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

评估模型

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, “float”))
print sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})

该模型的准确率达到91%，后面进行改进。