TensorFlow实现Softmax Regression识别手写数字

本章已机器学习领域的Hello World任务----MNIST手写识别做为TensorFlow的开始。MNIST是一个非常简单的机器视觉数据集，是由几万张28像素*28像素的手写数字组成，这些图片只包含灰度值信息。

import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

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

print (mnist.train.images.shape,mnist.train.labels.shape)
print (mnist.test.images.shape,mnist.test.labels.shape)
print (mnist.validation.images.shape,mnist.validation.labels.shape)

import tensorflow as tf
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32,[None,784])

W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,W)+b)

y_ = tf.placeholder(tf.float32,[None,10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y),reduction_indices=[1]))

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
tf.global_variables_initializer().run()

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

correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
print (accuracy.eval({x:mnist.test.images,y_:mnist.test.labels}))