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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第 6章 与学习相关的技巧
196
训练数据用于参数（权重和偏置）的学习，验证数据用于超参数的性
能评估。为了确认泛化能力，要在最后使用（比较理想的是只用一次）
测试数据。
根据不同的数据集，有的会事先分成训练数据、验证数据、测试数据三
部分，有的只分成训练数据和测试数据两部分，有的则不进行分割。在这种
情况下，用户需要自行进行分割。如果是
MNIST
数据集，获得验证数据的
最简单的方法就是从训练数据中事先分割 20
%
作为验证数据，代码如下所示。
(x_train, t_train), (x_test, t_test) = load_mnist()
#
打乱训练数据
x_train, t_train = shuffle_dataset(x_train, t_train)
#
分割验证数据
validation_rate = 0.20
validation_num = int(x_train.shape[0] * validation_rate)
x_val = x_train[:validation_num]
t_val = t_train[:validation_num]
x_train = x_train[validation_num:]
t_train = t_train[validation_num:]
这里，分割训练数据前，先打乱了输入数据和教师标签。这是因为数据
集的数据可能存在偏向（比如，数据从“0”到“ 10”按顺序排列等）。这里使
用的
shuffle_dataset
函数利用了
np.random.shuffle ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.