1. 首先准备一下数据
python
if __name__ == "__main__":
data = np.array([[2, 1, 0],
[2, 2, 0],
[5, 4, 1],
[4, 5, 1],
[2, 3, 0],
[3, 2, 0],
[6, 5, 1],
[4, 1, 0],
[6, 3, 1],
[7, 4, 1]])
x = data[:, :-1]
y = data[:, -1]
for epoch in range(1000):
...2. 实现Softmax+CrossEntropy层
单独求softmax层有点麻烦, 将softmax+entropy一起求导更方便。
假设对于输入向量 ( x 1 , x 2 , x 3 ) (x_1, x_2, x_3) (x1,x2,x3), 则对应的Loss为:
L = − ∑ i = 1 C y i ln p i = − ( y 1 ln p 1 + y 2 ln p 2 + y 3 ln p 3 ) \begin{align*} L&=-\sum_{i=1}^Cy_i \ln p^i \\ &=-(y_1\ln p_1+y_2\ln p_2+y_3\ln p_3) \end{align*} L=−i=1∑Cyilnpi=−(y1lnp1+y2lnp2+y3lnp3)
其中 y i y_i yi为ground truth, 为one-hot vector. p i p_i pi为输出概率。
p 1 = e x 1 e x 1 + e x 2 + e x 3 p 2 = e x 2 e x 1 + e x 2 + e x 3 p 3 = e x 3 e x 1 + e x 2 + e x 3 p_1=\frac{e^{x_1}}{e^{x_1}+e^{x_2}+e^{x_3}}\\ p_2=\frac{e^{x_2}}{e^{x_1}+e^{x_2}+e^{x_3}}\\ p_3=\frac{e^{x_3}}{e^{x_1}+e^{x_2}+e^{x_3}}\\ p1=ex1+ex2+ex3ex1p2=ex1+ex2+ex3ex2p3=ex1+ex2+ex3ex3
则偏导为
∂ L ∂ x 1 = − y 1 1 p 1 ∗ ∂ p 1 ∂ x 1 − y 2 1 p 2 ∗ ∂ p 2 ∂ x 1 − y 3 1 p 3 ∗ ∂ p 3 ∂ x 1 = − y 1 1 p 1 ∗ e x 1 ∗ ( e x 1 + e x 2 + e x 3 ) − e x 1 ∗ e x 1 ( e x 1 + e x 2 + e x 3 ) 2 − y 2 1 p 2 ∗ − e x 2 ∗ e x 1 ( e x 1 + e x 2 + e x 3 ) 2 − y 3 1 p 3 ∗ − e x 3 ∗ e x 1 ( e x 1 + e x 2 + e x 3 ) 2 = − y 1 1 p 1 ( p 1 ∗ p 2 + p 1 ∗ p 3 ) − y 2 1 p 2 ( − p 1 ∗ p 2 ) − y 3 1 p 3 ( − p 1 ∗ p 3 ) = − y 1 ( p 2 + p 3 ) + y 2 ∗ p 2 + y 3 ∗ p 3 = − y 1 ( 1 − p 1 ) + y 2 ∗ p 1 + y 3 ∗ p 1 = y 1 ( p 1 − 1 ) + y 2 ∗ p 1 + y 3 ∗ p 1 \begin{align*} \frac{\partial L}{\partial x_1} &= -y_1\frac{1}{p_1}*\frac{\partial p_1}{\partial x_1} - y_2\frac{1}{p_2}*\frac{\partial p_2}{\partial x_1} - y_3\frac{1}{p_3}*\frac{\partial p_3}{\partial x_1} \\ &= -y_1\frac{1}{p_1} * \frac{e^{x_1} * (e^{x_1}+e^{x_2}+e^{x_3})-e^{x_1}*e^{x_1}}{(e^{x_1}+e^{x_2}+e^{x_3})^2} \\ &\quad\quad-y_2\frac{1}{p_2}*\frac{-e^{x_2}*e^{x_1}}{{(e^{x_1}+e^{x_2}+e^{x_3})^2}}\\ &\quad\quad-y_3\frac{1}{p_3}*\frac{-e^{x_3}*e^{x_1}}{{(e^{x_1}+e^{x_2}+e^{x_3})^2}}\\ &=-y_1\frac{1}{p_1}(p_1*p_2+p_1*p_3)\\ &\quad\quad -y_2\frac{1}{p_2}(-p_1*p_2)\\ &\quad\quad -y_3\frac{1}{p_3}(-p_1*p_3)\\ &=-y1(p_2+p_3)+y_2*p_2+y_3*p_3\\ &=-y_1(1-p_1)+y_2*p_1+y_3*p_1\\ &=y_1(p_1-1)+y_2*p_1+y_3*p_1 \end{align*} ∂x1∂L=−y1p11∗∂x1∂p1−y2p21∗∂x1∂p2−y3p31∗∂x1∂p3=−y1p11∗(ex1+ex2+ex3)2ex1∗(ex1+ex2+ex3)−ex1∗ex1−y2p21∗(ex1+ex2+ex3)2−ex2∗ex1−y3p31∗(ex1+ex2+ex3)2−ex3∗ex1=−y1p11(p1∗p2+p1∗p3)−y2p21(−p1∗p2)−y3p31(−p1∗p3)=−y1(p2+p3)+y2∗p2+y3∗p3=−y1(1−p1)+y2∗p1+y3∗p1=y1(p1−1)+y2∗p1+y3∗p1
同理:
∂ L ∂ x 2 = y 1 ∗ p 2 + y 2 ( p 2 − 1 ) + y 3 ∗ p 2 ∂ L ∂ x 3 = y 1 ∗ p 3 + y 2 p 3 + y 3 ∗ ( p 3 − 1 ) \frac{\partial L}{\partial x_2}=y_1*p_2+y_2(p_2-1)+y_3*p_2\\ \frac{\partial L}{\partial x_3}=y_1*p_3+y_2p_3+y_3*(p_3-1) ∂x2∂L=y1∗p2+y2(p2−1)+y3∗p2∂x3∂L=y1∗p3+y2p3+y3∗(p3−1)
当 y 1 = 1 y_1=1 y1=1时, 对应的导数为 ( p 1 − 1 , p 2 , p 3 ) (p1-1, p_2, p_3) (p1−1,p2,p3). 当 y 2 = 1 y_2=1 y2=1时,对应的导数为: ( p 1 , p 2 − 1 , p 3 ) (p_1, p2-1, p3) (p1,p2−1,p3).
例如求得概率为 ( 0.2 , 0.3 , 0.5 ) (0.2, 0.3, 0.5) (0.2,0.3,0.5), label为 ( 0 , 0 , 1 ) (0, 0, 1) (0,0,1), 则导数为 ( 0.2 , 0.3 , − 0.5 ) (0.2, 0.3, -0.5) (0.2,0.3,−0.5)
python代码为:
注意求softmax时需要np.exp(x-np.max(x, axis=1, keepdims=True))防止指数运算溢出。
python
class Softmax:
def __init__(self, n_classes):
self.n_classes = n_classes
def forward(self, x, y):
prob = np.exp(x-np.max(x, axis=1, keepdims=True))
prob /= np.sum(prob, axis=1, keepdims=True)
# 选出y==1位置的概率
loss = -np.sum(np.log(prob[np.arange(len(y), y])) / len(y)
self.grad = prob.copy()
self.grad[np.arange(len(y), y] -= 1
"""
因为后面求导数都是直接np.sum而不是np.mean, 因此这里mean一次就可以了
"""
self.grad /= len(y)
return prob, loss
def backward(self):
return self.grad3. 单独的CrossEntropy
python代码为:
python
class Entropy:
def __init__(self, n_classes):
self.n_classes = n_classes
self.grad = None
def forward(self, x, y):
# x: (b, c), y: (b)
b = y.shape[0]
one_hot_y = np.zeros((b, self.n_classes))
one_hot_y[range(len(y)), y] = 1
self.grad = one_hot_y * -1 / x
return np.mean(-one_hot_y * np.log(x), axis=0)
def backward(self):
return self.grad2. 单独的Softmax层
python
from einops import repeat, rearrange, einsum
class Softmax:
def __init__(self):
def forward(self, x):
# x: (b, c)
x_exp = np.exp(x)
self.output = x_xep / np.sum(x_exp, axis=1, keep_dims=True)
return self.output
def backward(self, prev_grad):
b, c = self.output.shape
o = repeat(self.output, 'b c -> b c r', r=c)
I = repeat(np.eye(x.shape[1]), 'c1 c2 -> b c1 c2', b=b)
self.grad = o * (I - rearrange(o, 'b c1 c2 -> b c2 c1'))
return einsum(self.grad, grad[..., None], 'b c c, b c m -> b c m')[..., 0] 3. Linear层
注意更新 w w w时用的 d w d_w dw, 但是往上一层传递的是 d x d_x dx。因为上一层需要 d L / d o u t dL/d_{out} dL/dout, 而本层的输入 x x x即是上一次层的输出 d L / d o u t = d L / d x dL/d_{out} = dL/dx dL/dout=dL/dx
python
class Linear:
def __init__(self, in_channels, out_channels, lr):
self.lr = lr
self.w = np.random.rand(in_channels, out_channels)
self.b = np.random.rand(out_channels)
def forward(self, x):
self.x = x
return x@self.w + self.b
def backward(self, grad):
dx = einsum(prev_grad, rearrange(self.w, 'w1 w2 -> w2 w1'), 'c1 b, b c2 -> c1 c2')
dw = einsum(rearrange(self.x, 'b c -> c b'), prev_grad, 'c1 b, b c2 -> c1 c2')
db = np.sum(prev_grad, axis=0)
self.w -= self.lr * dw
self.b -= self.lr * db
"""
注意这里往上一层传递的是dx, 因为上一层需要dL/d_out, 而本层的输入x即是上一次层的输出
dL/d_out = dL/dx
"""
return dx5. 完整训练代码
python
from einops import *
import numpy as np
class Softmax:
def __init__(self, train=True):
self.grad = None
self.train = train
def forward(self, x, y):
prob = np.exp(x-np.max(x, axis=1, keepdims=True))
prob /= np.sum(prob, axis=1, keepdims=True)
if self.train:
loss = -np.sum(np.log(prob[range(len(y)), y]))/len(y)
self.grad = prob.copy()
self.grad[range(len(y)), y] -= 1
self.grad /= len(y)
return prob, loss
else:
return prob
def backward(self):
return self.grad
class Linear:
def __init__(self, in_channels, out_channels, lr):
self.w = np.random.rand(in_channels, out_channels)
self.b = np.random.rand(out_channels)
self.lr = lr
def forward(self, x):
self.x = x
output = einsum(x, self.w, 'b c1, c1 c2 -> b c2') + self.b
return output
def backward(self, prev_grad):
cur_grad = einsum(rearrange(self.x, 'b c -> c b'), prev_grad, 'c1 b, b c2 -> c1 c2')
self.w -= self.lr * cur_grad
self.b -= self.lr * np.sum(prev_grad, axis=0)
return cur_grad
class Network:
def __init__(self, in_channels, out_channels, n_classes, lr):
self.lr = lr
self.linear = Linear(in_channels, out_channels, lr)
self.softmax = Softmax()
def forward(self, x, y=None):
out = self.linear.forward(x)
out = self.softmax.forward(out, y)
return out
def backward(self):
grad = self.softmax.backward()
grad = self.linear.backward(grad)
return grad
if __name__ == "__main__":
data = np.array([[2, 1, 0],
[2, 2, 0],
[5, 4, 1],
[4, 5, 1],
[2, 3, 0],
[3, 2, 0],
[6, 5, 1],
[4, 1, 0],
[6, 3, 1],
[7, 4, 1]])
# x = np.concatenate([np.array([[1]] * data.shape[0]), data[:, :2]], axis=1)
x = data[:, :-1]
y = data[:, -1:].flatten()
net = Network(2, 2, 2, 0.1)
# loss_fn = CrossEntropy(n_classes=2)
for epoch in range(500):
prob, loss = net.forward(x, y)
# loss = loss_fn.forward(out, y)
# grad_ = loss_fn.backward()
grad = net.backward()
print(loss)
net.softmax.train = False
print(net.forward(np.array([[0, 0], [0, 4], [8, 6], [10, 10]])), y)