TensorFlow入门测试程序

 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)

 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.initialize_all_variables().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.arg_max(y,1),tf.arg_max(y_,1))
 accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

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

运行结果：