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w_conv1 = weight_variable([5, 5, 1, 32]) 这里这个shape最后一个为什么是32呢, 这个有没有啥说法,我理解是把一张图片分成32张,每张可能提取到不同的特征。 我把32换成1, 后面的64换成2,也能运行,而且训练速度更快,迭代到最后正确率差不多
The text was updated successfully, but these errors were encountered:
不好意思,之前匆匆跑去准备考研,就没再继续,根据官方文档描述,这个卷积的权重张量形状是[5, 5, 1, 32],前两个维度是patch的大小,接着是输入的通道数目,最后是输出的通道数目
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w_conv1 = weight_variable([5, 5, 1, 32])
这里这个shape最后一个为什么是32呢, 这个有没有啥说法,我理解是把一张图片分成32张,每张可能提取到不同的特征。 我把32换成1, 后面的64换成2,也能运行,而且训练速度更快,迭代到最后正确率差不多
The text was updated successfully, but these errors were encountered: