Today I tried to get packages to download on my raspberry PI, but failed. I talked with Mr. Burnham about my options, and on Friday I will re-image my raspberry PI. Of course, before then I will have already backed up all of my python code and be able to continue working on the project.
today I
1.troubleshooted packages such as keras
2.wrote some code for the project
3. arranged an appointment for the reflash
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAEPCAYAAABsj5JaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0A)
*over time, my project will be able to more accurately recognize objects and perform processing on them.
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