def calculate_loss(self, x, y):
output = Softmax()
layers = self.forward_propagation(x)
loss = 0.0
for i, layer in enumerate(layers):
loss += output.loss(layer.mulv, y[i])
return loss / float(len(y))
def calculate_loss(self, X, y):
mulGate = MultiplyGate()
addGate = AddGate()
layer = Tanh()
softmaxOutput = Softmax()
input = X
for i in range(len(self.W)):
mul = mulGate.forward(self.W[i], input)
add = addGate.forward(mul, self.b[i])
input = layer.forward(add)
return softmaxOutput.loss(input, y)