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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5.7 误差反向传播法的实现
155
5.7.2
对应误差反向传播法的神经网络的实现
现在来进行神经网络的实现。这里我们要把2层神经网络实现为
TwoLayerNet
。
首先，将这个类的实例变量和方法整理成表5
-
1 和表 5
-
2。
表5-1 TwoLayerNet类的实例变量
实例变量 说明
params
保存神经网络的参数的字典型变量。
params['W1']
是第1 层的权重，
params['b1']
是第1 层的偏置。
params['W2']
是第2 层的权重，
params['b2']
是第2 层的偏置
layers
保存神经网络的层的有序字典型变量。
以
layers['Affine1']
、
layers['ReLu1']
、
layers['Affine2']
的形式，
通过有序字典保存各个层
lastLayer
神经网络的最后一层。
本例中为
SoftmaxWithLoss
层
表5-2 TwoLayerNet类的方法
方法 说明
__init__(self, input_size,
hidden_size, output_size,
weight_init_std)
进行初始化。
参数从头开始依次是输入层的神经元数、隐藏层的
神经元数、输出层的神经元数、初始化权重时的高
斯分布的规模
predict(self, x)
进行识别（推理）。
参数
x
是图像数据
loss(self, x, t)
计算损失函数的值。
参数
X
是图像数据、
t
是正确解标签
accuracy(self, x, t)
计算识别精度
numerical_gradient(self, x, ...
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