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 3章 神经网络
58
3.4.2
各层间信号传递的实现
现在看一下从输入层到第 1层的第 1 个神经元的信号传递过程，如图 3
-
17
所示。
1
x
1
x
2
y
1
y
2
图3-17 从输入层到第1 层的信号传递
图 3
-
17 中增加了表示偏置的神经元“1”。请注意，偏置的右下角的索引
号只有一个。这是因为前一层的偏置神经元（神经元“1”）只有一个
A
。
为了确认前面的内容，现在用数学式表示
。 通过加权信号和偏
置的和按如下方式进行计算。
（3.8）
A
任何前一层的偏置神经元“1”都只有一个。偏置权重的数量取决于后一层的神经元的数量（不包括
后一层的偏置神经元“1”）。——译者注
3.4 3层神经网络的实现
59
此外，如果使用矩阵的乘法运算，则可以将第1 层的加权和表示成下面
的 式（3.9）。
A
(
1
)
=
XW
(
1
)
+
B
(
1
)
（3.9）
其中，
A
(
1
)
、
X
、
B
(
1
)
、
W
(
1
)
如下所示。
下面我们用
NumPy
多维数组来实现式（3.9），这里将输入信号、权重、
偏置设置成任意值。
X = np.array([1.0, 0.5])
W1 = np.array([[0.1, 0.3, 0.5], [0.2, 0.4, 0.6]])
B1 = np.array([0.1, 0.2, 0.3])
print(W1.shape) ...
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