tensorflow 学习笔记(3)-basic_example

logistic_regression

# -*- coding: utf-8 -*-
"""
Created on Mon Jun 19 20:59:46 2017
@author: wuchengzhu
"""
from __future__ import print_function
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST_data/", one_hot = True)

learning_rate = 0.01
training_epochs = 25
batch_size = 100
display_step = 1

x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])

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

pred  = tf.nn.softmax(tf.matmul(x, W) + b)

cross_entropy =  tf.reduce_mean(- tf.reduce_sum(y * tf.log(pred)) )

optimizer  = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)

init  = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
for epoch in range(training_epochs):
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)
_, c = sess.run([optimizer, cross_entropy], feed_dict = {x: batch_xs, y: batch_ys})
avg_cost += c / total_batch
if (epoch + 1) % display_step == 0:
print("Epoch: ",'%04d' % (epoch + 1), "cost = ","{:.9f}".format(avg_cost))
print("Optimization Finished!")
correctt_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correctt_prediction, tf.float32))
print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))