Machine Learning--week4 神经网络的基本概念

之前的学习成果并不能解决复杂的非线性问题

Neural Networks

Sigmoid(logistic) activation function: activation function is another term for \(g(z) = \frac{1}{1+e^{-z}}\)

activation: the value that's computed by and as output by a specific

weights = parameters = \(\theta\)

input units: \(x_1,x_2, x_3,\dots, x_n\)

bias unit/ bias neuron: \(x_0\) 与 \(a_0^{(j)}\)

input units 和 hypothesis 之间的layer 由activation 构成

input wire/ output wire：input wire是指指向目标neuron的箭头，output wire是指从目标neuron指出的箭头

\(a_i^{(j)}\): "activation" of neuron \(i\) or of unit \(i\) in layer \(j\)

\(\Theta^{(j)}\): matrix of weights controlling the function mapping form layer \(j\) to layer \(j+1\)

（注意\(\Theta\)是大写的，因为它需要用到矩阵的形式了）

layer 1 == input layer

layer n == output layer (the last layer)

layer 2 ~ layer n-1 == hidden layer

for example:

\[\begin{align} \text{output of layer 1(a hidden lyer)}&\begin{cases}a_1^{(2)} &= g(\Theta_{10}^{(1)}x_0 + \Theta_{11}^{(1)}x_1 + \Theta_{12}^{(1)}x_2 + \Theta_{13}^{(1)}x_3)\\ a_2^{(2)} &= g(\Theta_{20}^{(1)}x_0 + \Theta_{21}^{(1)}x_1 + \Theta_{22}^{(1)}x_2 + \Theta_{23}^{(1)}x_3)\\ a_3^{(2)} &= g(\Theta_{30}^{(1)}x_0 + \Theta_{31}^{(1)}x_1 + \Theta_{32}^{(1)}x_2 + \Theta_{33}^{(1)}x_3)\end{cases}\\ \text{output layer}&\begin{cases}h_\Theta(x) = a_1^{(3)} = g(\Theta_{10}^{(2)}a_0^{(2)} + \Theta_{11}^{(2)}a_1^{(2)} +\Theta_{12}^{(2)}a_2^{(2)} + \Theta_{13}^{(2)}a_3^{(2)})\end{cases} \end{align} \]

直观点就是：

\[\begin{align} \text{output of layer 1(a hidden lyer)} &\begin{cases} a_1^{(2)} &= g(\Theta_{1}^{(1)}a^{(1)})\\ a_2^{(2)} &= g(\Theta_{2}^{(1)}a^{(1)})\\ a_3^{(2)} &= g(\Theta_{3}^{(1)}a^{(1)}) \end{cases}\\ \text{output layer} &\begin{cases} h_\Theta(x) = a_1^{(3)} = g(\Theta_{1}^{(2)}a^{(2)}) \end{cases} \end{align} \]

)generally, \(\Theta^{(j)}\) will be of dimension \(s_{j+1} \times (s_j+1)\), if network has \(s_j\) units in layer \(j\) and \(s_{j+1}\) units in layer \(j+1\). (\(s_j+1\)中的\(+1\) comes from the addition in \(\Theta^{(j)}\) of the "bias nodes," \(x_0\) and \(\Theta_0^{(j)}\) . In other words the output nodes will not include the bias nodes while the inputs will. )

定义 \(a^{(1)} = x\)

\(z^{j+1} = \Theta^{(j)}a^{(j)}\)

\(x_k^{(j+1)} = \Theta_{k,0}^{(j)}a_0^{(j)} + \Theta_{k,1}^{(j)}a_1^{(j)} + \dots + \Theta_{k,n^{(j)}}^{(j)}a_{n^{(j)}}^{(j)}\quad ,(n^{(j)} \text{ means layer j has } n^{(j)} \text{ activation})\)

\(a^{(j)} = g(z^{(j)}) = g(\Theta^{(j-1)}a^{(j-1)})\quad(j\ge2)\)

设有 \(n\) 个 layers, then the last matrix \(\Theta^{(n)}\) will have only one row which is multiplied by one column \(a^{(j)}\) so that our result is a single number:

​ \(h_\Theta(x) = a^{(n+1)}=g(z^{(n+1)})\)

Add \(a_0^{(j)}=1\)

Forward Propagation：向前传播

Neural Networks 实际上是使用\(a^{(n-1)}\)layer作为训练logistic regression的特征的，而非input layer，在\(\Theta^{(1)}\)中选择不同的参数可能得到一些复杂的特征，从而的到更好的hypothesis，这样做比直接用\(x_1,x_2,\dots ,x_n\)作为训练特征更好

architecture(架构)：the way that neural networks are connected

逻辑表达式对应的\(\theta\)：

• \({\rm AND} = (x_1 \bigwedge x_2)\):
  • \(\Theta = \begin{bmatrix}-30 &20& 20 \end{bmatrix}\)
• \({\rm NOR} = (\lnot x_1 \bigwedge \lnot x_2)\):
  • \(\Theta = \begin{bmatrix}10 & -20& -20 \end{bmatrix}\)
• \({\rm OR} = (x_1 \bigvee x_2)\):
  • \(\Theta = \begin{bmatrix}-10 &20& 20 \end{bmatrix}\)
• \({\rm NOT} = (\lnot x)\):
  • \(\Theta = \begin{bmatrix}-10 & 20\end{bmatrix}\)
• \({\rm XNOR} = (\lnot x_1 \bigwedge \lnot x_2) \bigvee ( x_1 \bigwedge x_2)\)
  • 需要一个hidden layer: \(a_1^{(2)} == (\lnot x_1 \bigwedge \lnot x_2),\quad a_2^{(2)} == (x_1 \bigwedge x_2)\)
  • output layer: \(a^{(3)} == (a_1^{(2)} \bigvee a_2^{(2)})\)

逻辑表达式的实现：

​ 令\(x=\begin{bmatrix}1 \\ x_1\\x_2 \end{bmatrix}\), 则 \(a_i = g(\Theta_ix)\)就得到\(\Theta_i\)对应的逻辑运算符运算\(x_1,x_2\)的结果了

​ 比如 \(\Theta_i = \begin{bmatrix}-10 &20& 20 \end{bmatrix}\)那么\(a_i == x_1 \bigvee x_2\)

​ 像\({\rm XNOR}\)这种复杂的逻辑表达式需要借助hidden layer才能算出来

对于 multiclass Classification:

​ 用\(y = \begin{bmatrix}1\\0\\0\\0 \end{bmatrix}, \begin{bmatrix}0\\1\\0\\0 \end{bmatrix}, \begin{bmatrix}0\\0\\1\\0 \end{bmatrix}, \begin{bmatrix}0\\0\\0\\1 \end{bmatrix},\begin{bmatrix}0\\0\\0\\0 \end{bmatrix}\)来表示不同的class，