STM32CubeMX AI 评估TensorFlow手写字例程

STM32CubeMX AI：这篇文章足够详细了，我只参考了cubemx的使用方法。
https://blog.csdn.net/bigmaxPP/article/details/104500092

算法，直接用的TensorFlow官方入门教程：手写字 就可以

官网打不开，国内的相关blog多如牛毛：

https://blog.csdn.net/weixin_43272781/article/details/110293351

https://blog.csdn.net/dusin/article/details/108544965

保存为了h5，STM32CubeMX 用的时候也没压缩，空间足够

model.save("tf-helloworld.h5")

 

本文只说一下怎么输入图片数据：

stm32里面就是直接输入一个数组，一维数组。这段程序就可以打印一个数组了

#导入工具包
import tensorflow as tf
import pandas as pd
import numpy as np

from ctypes import *
import numpy
 
 
def print2DArray(ary):
    for i in range(len(ary)):
        for j in range(len(ary[i])):
                print("%f, " % ary[i][j])

    return



mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

# print(x_test[0])


print(type(x_test[0][0][0]))

　　最后输入c图片文件可以像这样：

AI_ALIGNED(4) ai_float data_in_1[AI_NETWORK_IN_1_SIZE] = 
{
	0,0,0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
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0,0,0,0,0,0,
0,0,0,0,0.14901961,0.99607843
,0.42745098,0,0,0,0,0,
0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0.34117647,0.98823529
,0.32156863,0,0,0,0,0,
0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0.52941176,0.94509804,
0,0,0,0,0,0,
0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
0,0,0,0.17647059,0.95686275,0.58823529,
0,0,0,0,0,0,
0,0,0,0,
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0,0,0,0,0,0,
0,0,0,0.32941176,0.99607843,0.24705882,
0,0,0,0,0,0,
0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
0,0,0,0.79215686,0.8745098,0.04313725,
0,0,0,0,0,0,
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0,0,0.1254902,0.99607843,0.84705882,0,
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0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
0,0,0.37254902,0.99607843,0.76470588,0,
0,0,0,0,0,0,
0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
0,0,0.54901961,0.99607843,0.30196078,0,
0,0,0,0,0,0,
0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
0,0.22352941,0.92941176,0.80392157,0.03137255,0,
0,0,0,0,0,0,
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0,0,0,0,0,0,
0,0,0,0,0,0,
0,0.48627451,1,0.64705882,0,0,
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0,0,0,0,0,0,
0,0.67058824,0.99607843,0.31764706,0,0,
0,0,0,0,0,0,
0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
0.09411765,0.90980392,0.84313725,0,0,0,
0,0,0,0,0,0,
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0,0,0,0,0,0,
0.47058824,0.99607843,0.62352941,0,0,0,
0,0,0,0,0,0,
0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
0.59215686,0.99607843,0.55686275,0,0,0,
0,0,0,0,0,0,
0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
0.89411765,0.99607843,0.25882353,0,0,0,
0,0,0,0,0,0,
0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0.23921569,
0.98431373,0.99607843,0.25882353,0,0,0,
0,0,0,0,0,0,
0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0.55294118
,0.99607843,0.80392157,0.01176471,0,0,0,
0,0,0,0,0,0,
0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0.03921569,0.84313725
,0.99607843,0.4745098,0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0.01960784,0.77647059
,0.69019608,0.03921569,0,0,0,0,
0,0,0,0,0,0,
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0,0,0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
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0,0,0,0,0,0,
0,0,0,0,0,0,
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};