Python-深度学习入门记录#1

提示：注重Python中class对象的使用方法
问题：训练集文件加载

class RULE:
    def __init__(self):
        self.mask=None
    def forward(self,input): #函数运算
        self.mask=(input<=0)
        out=input.copy()
        out[self.mask]=0
        return out
    def backward(self,dout): #函数求导
        dout[self.mask]=0
        doutput=dout
        return doutput

class Sigmoid:
    def __init__(self):
        self.out=None
    def forward(self,input):
        out=1/(1+np.exp(-input))
        self.out=out
        return out
    def backward(self,dout):
        doutput=dout*(1.0-self.out)*self.out
        return doutput

提示：包含矩阵表示，矩阵运算，Y=WX +b

class Affine:
    def __init__(self,W,b):
        self.W=W
        self.b=b
        self.x=None
        self.dW=None
        self.db=None
    def forward(self,x):
        self.x=x
        out=np.dot(x,self.W)+self.b
        return out
    def backward(self,dout):
        dx=np.dot(x,self.W.T) # 转置
        self.dW=np.dot(self.x.T,dout) # 转置
        self.db=np.sum(dout,axis=0)
        return dx

class SoftmaxWithLoss:
    def __init__(self):
        self.loss=None
        self.y=None
        self.t=None
    def forward(self,x,t):
        self.t=t
        self.y=softmax(x)
        self.loss=cross_entropy_errot(self.y,self.t)
        return self.loss
    def backward(self,dout=1):
        bacth_size=self.t.shape[0]
        dx=(self.y-self.t)/bacth_size
        return dx

def softmax(input):
    exp_input=np.exp(input)
    sum_exp=np.sum(exp_input)
    output=exp_input/sum_exp
    return output

y是输出，t是监督数据

def cross_entropy_error(y, t):
    if y.ndim == 1:
        t = t.reshape(1, t.size)
        y = y.reshape(1, y.size)
    # 监督数据是one-hot-vector的情况下，转换为正确解标签的索引
    if t.size == y.size:
        t = t.argmax(axis=1)
    batch_size = y.shape[0]
    return -np.sum(np.log(y[np.arange(batch_size), t] + 1e-7)) / batch_size

def softmax_loss(input, t):
    y = softmax(input)
    return cross_entropy_error(y, t)