TensorFlow逻辑回归代码实现
# -*- coding: UTF-8 -*-
import tensorflow as tf
#导入数据集
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("data/", one_hot=True)
#变量
batch_size = 100
#训练的x(image),y(label)
# x = tf.Variable() 不使用于大量数据
# y = tf.Variable() 不使用于大量数据
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])
#模型权重
#[55000, 784]* w = [55000, 10]
w = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
#用softmax构建逻辑回归模型
pred = tf.nn.softmax(tf.matmul(x,w) + b)
#损失函数（交叉熵）
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred),1))
# 梯度下降
optimizer = tf.train.GradientDescentOptimizer(0.01).minimize(cost)
#初始变量
init = tf.initialize_all_variables()
#加载session图
with tf.Session() as sess:
sess.run(init)
#开始训练
for epoch in range(50):
avg_cost = 0
total_batch= int(mnist.train.num_examples/batch_size)
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
sess.run(optimizer,{x: batch_xs,y: batch_ys})
#计算平均损失
avg_cost += sess.run(cost,{x:batch_xs,y:batch_ys}) / total_batch
if (epoch+1) % 5 == 0:
print "avg_cost",avg_cost
print "运行完成"
#测试准确率
correct = tf.equal(tf.argmax(pred, 1),tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
print "正确率",accuracy.eval({x: mnist.test.images,y: mnist.test.labels})# -*- coding: UTF-8 -*-
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