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