Tensorflow的hello world！——Softmax Regression 识别手 4000 写数字

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  #载入tensorflow模块

sess=tf.InteractiveSession()  #创建一个新的session 使用这个命令还会使这个session注册为默认的

x=tf.placeholder(tf.float32,[None,784]) #创建一个placeholder 输入数据的地方 数据类型：float  数据维度：[不限条数，784维]

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]) #创建一个placeholder 输入是真实的label
#loss function
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)  #选择优化算法 随机梯度下降 SGD

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})

correc_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
accuracy=tf.reduce_mean(tf.cast(correc_prediction,tf.float32))

print(accuracy.eval({x:mnist.test.images,y_:mnist.test.labels}))