class CosineDecayLR(LearningRateSchedule):
    def __init__(self, min_lr, max_lr, decay_steps):
        super(CosineDecayLR, self).__init__()
        self.min_lr = min_lr
        self.max_lr = max_lr
        self.decay_steps = decay_steps
        self.math_pi = math.pi
        self.delta = 0.5 * (max_lr - min_lr)
        self.cos = P.Cos()
        self.min = P.Minimum()
        self.cast = P.Cast()

    def construct(self, global_step):
        p = self.cast(self.min(global_step, self.decay_steps), mstype.float32)
        return self.min_lr + self.delta * (1.0 + self.cos(self.math_pi * p / self.decay_steps))

min_lr = 0.01
max_lr = 0.1
decay_steps = 4
global_steps = Tensor(2, mstype.int32)
cosine_decay_lr = CosineDecayLR(min_lr, max_lr, decay_steps)
result = cosine_decay_lr(global_steps)
print(result)
0.055

net = Net()
loss = nn.SoftmaxCrossEntropyWithLogits()
optim = nn.Momentum(net.trainable_params(), learning_rate=cosine_decay_lr)
model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)

dataset = create_custom_dataset()
model.train(1, dataset)