Softmax Regression
(多标签分类)将多输入的分类值转化为[0,1]的概率分布，进而进行逻辑回归算法
softmax能将差距大的数值距离拉得更大，但是数值可能会溢出
Softmax Function
数学表达式
\[a_j = \frac{e^{z_j}}{ \sum_{k=1}^{N}{e^{z_k} }} \]代码
def my_softmax(z):
ez = np.exp(z) #element-wise exponenial
sm = ez/np.sum(ez)
return(sm)
cost function
数学表达式
loss:
\[\begin{equation} L(\mathbf{a},y)=\begin{cases} -log(a_1), & \text{if $y=1$}.\\ &\vdots\\ -log(a_N), & \text{if $y=N$} \end{cases} = -log\left(\frac{e^{z_2}}{ \sum_{i=1}^{N}{e^{z_i} }}\right) \end{equation} \]化简
\[L(\mathbf{z})= -\left[z_2 - log( \sum_{i=1}^{N}{e^{z_i} })\right] = \underbrace{log \sum_{i=1}^{N}{e^{z_i} }}_\text{logsumexp()} -z_2 = C+ log( \sum_{i=1}^{N}{e^{z_i-C} }) -z_2 \;\;\;\text{where } C=max_j(\mathbf{z}) \]cost function
\[\begin{align} J(\mathbf{w},b) = - \left[ \sum_{i=1}^{m} \sum_{j=1}^{N} 1\left\{y^{(i)} == j\right\} \log \frac{e^{z^{(i)}_j}}{\sum_{k=1}^N e^{z^{(i)}_k} }\right] \end{align} \]代码
# compute f_x
model = Sequential(
[
Dense(25, activation = 'relu'),
Dense(15, activation = 'relu'),
Dense(4, activation = 'softmax') # < softmax activation here
]
)
# compute loss
model.compile(
loss=tf.keras.losses.SparseCategoricalCrossentropy(),
optimizer=tf.keras.optimizers.Adam(0.001),
)
# gradient descent
model.fit(
X_train,y_train,
epochs=10
)
Numerical Stability
该模型使用指数函数，因而大数容易溢出，需要处理
数学原理
\[\begin{align} a_j &= \frac{e^{z_j}}{ \sum_{i=1}^{N}{e^{z_i} }} \frac{e^{-max_j(\mathbf{z})}}{ {e^{-max_j(\mathbf{z})}}} \\ &= \frac{e^{z_j-max_j(\mathbf{z})}}{ \sum_{i=1}^{N}{e^{z_i-max_j(\mathbf{z})} }} \end{align} \]化简
\[a_j = \frac{e^{z_j-C}}{ \sum_{i=1}^{N}{e^{z_i-C} }} \quad\quad\text{where}\quad C=max_j(\mathbf{z}) \]代码
def my_softmax_ns(z):
"""numerically stablility improved"""
bigz = np.max(z)
ez = np.exp(z-bigz) # minimize exponent
sm = ez/np.sum(ez)
return(sm)
多类分类和多标签分类不一样
- 多标签：无关物品分类
- 多类：有概率关系的特征分类