TensorFlow入门测试程序
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1 import tensorflow as tf 2 from tensorflow.examples.tutorials.mnist import input_data 3 4 mnist=input_data.read_data_sets("MNIST_data/",one_hot=True) 5 6 # print(mnist.train.images.shape,mnist.train.labels.shape) 7 # print(mnist.test.images.shape,mnist.test.labels.shape) 8 # print(mnist.validation.images.shape,mnist.validation.labels.shape) 9 10 sess=tf.InteractiveSession() 11 x=tf.placeholder(tf.float32,[None,784]) 12 13 W=tf.Variable(tf.zeros([784,10])) 14 b=tf.Variable(tf.zeros([10])) 15 16 y=tf.nn.softmax(tf.matmul(x,W)+b) 17 18 y_=tf.placeholder(tf.float32,[None,10]) 19 cross_entropy=tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y),reduction_indices=[1])) 20 21 train_step=tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) 22 tf.initialize_all_variables().run() 23 24 for i in range(1000): 25 batch_xs,batch_ys=mnist.train.next_batch(100) 26 train_step.run({x:batch_xs,y_:batch_ys}) 27 28 correct_prediction=tf.equal(tf.arg_max(y,1),tf.arg_max(y_,1)) 29 accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) 30 31 print(accuracy.eval({x:mnist.test.images,y_:mnist.test.labels}))
运行结果: