Where should i define derivative from custom activation function in Keras

I am beginner in python, deep learning and neural network. I had made custom activation function. What i want to know when i am making custom activation function that root from sigmoid, where should i define the derivative for my custom activation function?

I've tried reading about automatic differentation. but i am not sure does keras automatically derivative my custom sigmoid?

my custom activation function in keras/activation.py

def tempsigmoid(x, temp=1.0):
    return K.sigmoid(x/temp)

my model

def baseline_model():
    # create model
    model = Sequential()
    model.add(Conv2D(101, (5, 5), input_shape=(1, 28, 28), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.2))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dense(num_classes, activation='tempsigmoid'))
    # Compile model
    model.compile(loss='mse', optimizer='adam', metrics=['accuracy'])
    return model