loss.py

from module import Layers
import numpy as np

def Softmax(input):
    vec_max = np.max(input,axis=1,keepdims=True)  
    input -= vec_max
    exp = np.exp(input) 
    softmax_pro = exp/np.sum(exp,axis=1,keepdims=True)
    return softmax_pro

class CrossEntropyLoss():
    def __init__(self, reduce='mean'):
        super(CrossEntropyLoss,self).__init__()
        self.reduce = reduce

    def __call__(self,pred,label):
        # self.softmax_p = Softmax(pred)
        self.softmax_p = pred
        self.real = label

        loss = 0
        for i in range(label.shape[0]):
            loss += -np.log( self.softmax_p[i,label[i]] )
        
        if self.reduce == 'mean':
            loss /= label.shape[0]

        grad = self.grad()
        return loss,grad
        
    def grad(self):
        grad = self.softmax_p.copy()
        for i in range(self.real.shape[0]):
            grad[i,self.real[i]] -= 1
        return grad 


class MSELoss():
    def __init__(self, reduce='mean'):
        super(MSELoss,self).__init__()
        self.reduce = reduce

    def __call__(self,pred,label):
        assert pred.shape == label.shape, 'pred and gt shape must be same'
        loss = np.sum(np.square((pred-label)),axis=-1)
        
        if self.reduce == 'mean':
            loss = np.mean(loss)
        else:
            loss = np.sum(loss)
        
        grad = (pred-label)
        
        return loss,grad


if __name__ == '__main__':
    mseloss = MSELoss()

    x = np.random.randn(10,10)
    y = x+1

    loss,grad = mseloss(x,y)