查看“Jetson Nano系列教程7：TensorFlow入门介绍(三)”的源代码



一、前言

上篇用JetsonNano+TensorFlow跑MNIST手写数字分类的Demo，本篇通过PS生成的0~9共10个数字的28*28像素图片进行测试。
二、测试
生成黑底白字28*28像素的图片如下图所示：



[[File:190742l8bdow8myoyd81ya.png]]

上篇构建的神经网络基础上加入读取图片和运行测试相关代码
<syntaxhighlight lang="python">
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf  
#导入图片处理相关库
from PIL import Image
import numpy as np
from itertools import chain 

#读取0~9其中一张图片进行测试，读者感兴趣可以尝试修改数字进行其它图片测试
image_tem = Image.open('./2.PNG')
image_array = np.array(image_tem)
image_array=np.asarray(image_array,dtype="float32") 
image_array = list(chain.from_iterable(image_array))
image_array = [image_array]

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

x = tf.placeholder(tf.float32,[None,784])
y = tf.placeholder(tf.float32, [None,10])

w1 = tf.Variable(tf.truncated_normal([784,1024]),dtype=tf.float32)
b1 = tf.Variable(tf.zeros([1,1024]),dtype=tf.float32)
a = tf.nn.relu(tf.matmul(x,w1) + b1) 

w2 = tf.Variable(tf.ones([1024,10]))
b2 = tf.Variable(tf.zeros([1,10]))
y_= tf.nn.softmax(tf.matmul(a,w2) + b2) 

#将图片输入到神经网络中去
b = tf.nn.relu(tf.matmul(image_array,w1)+b1)
y_image = tf.nn.softmax(tf.matmul(b,w2)+b2)

loss= tf.reduce_mean(-tf.reduce_sum(y*tf.log(y_),axis=1))
train_step = tf.train.AdamOptimizer(0.0001).minimize(loss)

init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
correct_prediction = tf.equal(tf.argmax(y_,axis=1), tf.argmax(y,axis=1))accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

for i in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys})

#待神经网络权重偏置调整完成后，直接运行待测试的图片
print(sess.run(y_image))</syntaxhighlight>
 当读取图片数字为2的时候，实际测试结果如下，感兴趣读者可测试其它图片：

[[File:193401ias5p5dtzap6kva8.png]]

相关文件请点击右边下载 --> <a class="attach" href="portal.php?mod=attachment&amp;id=1640" target="_blank">MNIST_TEST.zip

用户可使用SSH登录JetsonNano终端，
不熟悉用户点击我参阅SSH登录
，在终端下输入下面命令测试：
<syntaxhighlight lang="python">
wget http://{{SERVERNAME}}/study/portal.php?mod=attachment&amp;id=1640
unzip -o MNIST_TEST.zip
sudo python3 mnist_test.py</syntaxhighlight>
以上资料由waveshare team整理<!--5f39ae17-8c62-4a45-bc43-b32064c9388a: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--><!--5f39ae17-8c62-4a45-bc43-b32064c9388a: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--> 

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Jetson Nano系列教程 TensorFlow python