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：