def loss(self, x, t): #손실함수 z = self.predict(x) y = softmax(z) loss = cross_entropy_error(y, t) return loss
def get_loss(self, x, t): y = softmax(self.predict(x)) return cross_entropy_error(y, t)
def loss(self, x, t): y = self.predict(x) return cross_entropy_error(y, t)
def loss(self, inputParam, teacher): outputParam = self.predict(inputParam) return cee.cross_entropy_error(outputParam, teacher)
def forward(self, x, t): self.t = t self.y = softmax(x) self.loss = cross_entropy_error(self.y, self.t) return self.loss