July 2018
Intermediate to advanced
310 pages
8h 21m
Chinese
Content preview from 深度学习入门 : 基于Python的理论与实现
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3.6 手写数字识别
75
执行上面的代码后，会显示“
Accuracy:
0.9352”。这表示有93.52
%
的数
据被正确分类了。目前我们的目标是运行学习到的神经网络，所以不讨论识
别精度本身，不过以后我们会花精力在神经网络的结构和学习方法上，思考
如何进一步提高这个精度。实际上，我们打算把精度提高到99
%
以上。
另外，在这个例子中，我们把
load_mnist
函数的参数
normalize
设置成了
True
。将
normalize
设置成
True
后，函数内部会进行转换，将图像的各个像
素值除以255，使得数据的值在0.0
～
1.0 的范围内。像这样把数据限定到某
个范围内的处理称为正规化（
normalization
）。此外，对神经网络的输入数据
进行某种既定的转换称为预处理（
pre-processing
）。这里，作为对输入图像的
一种预处理，我们进行了正规化。
预处理在神经网络（深度学习）中非常实用，其有效性已在提高识别
性能和学习的效率等众多实验中得到证明。在刚才的例子中，作为
一种预处理，我们将各个像素值除以255，进行了简单的正规化。
实际上，很多预处理都会考虑到数据的整体分布。比如，利用数据
整体的均值或标准差，移动数据，使数据整体以0为中心分布，或
者进行正规化，把数据的延展控制在一定范围内。除此之外，还有
将数据整体的分布形状均匀化的方法，即数据白化（whitening）等。
3.6.3
批处理
以上就是处理
MNIST
数据集的神经网络的实现，现在我们来关注输入
数据和权重参数的“形状”。再看一下刚才的代码实现。 ...
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