tensorflow+python MNIST数据抽样可视化+前向运算过程

TensorFlow下运用自带input_data接口图取数据，抽样单个数据，1*784 reshape为 28*28 ，最后可视化显示

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import matplotlib.pyplot as plt

with tf.Session() as sess:
    sess.run(init)
    for epoch in range(1):
        for batch in range(n_batch):
            batch_xs,batch_ys=mnist.train.next_batch(batch_size)
            sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})
      
        acc=sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
        print("Iter " + str(epoch) + ",Testing Accuracy " + str(acc))


    #forward detection
    test_x,test_y=mnist.train.next_batch(1)
    print("test_x_shape=",x.shape,",test_y_shape=",y.shape)    
    result_value=sess.run(prediction,feed_dict={x:test_x})
    img=test_x.reshape(28,28)
    fig = plt.figure()  
    plotwindow = fig.add_subplot(111)  
    plt.imshow(img,cmap='gray')  
    plt.show()  
    truth=tf.argmax(test_y,1)
    dec=tf.argmax(result_value,1)
    print("dec=",sess.run(dec),",origin=",sess.run(truth))