import paddle
import numpy as np

# train on default dygraph mode
paddle.disable_static()
x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
linear = paddle.nn.Linear(10, 10)
scheduler = paddle.optimizer.LinearLrWarmup(
        learning_rate=0.5, warmup_steps=20, start_lr=0, end_lr=0.5, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameter_list=linear.parameters())
for epoch in range(20):
    for batch_id in range(2):
        x = paddle.to_tensor(x)
        out = linear(x)
        loss = paddle.reduce_mean(out)
        loss.backward()
        sgd.minimize(loss)
        linear.clear_gradients()
    scheduler.step()

# train on static mode
paddle.enable_static()
main_prog = paddle.static.Program()
start_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, start_prog):
    x = paddle.static.data(name='x', shape=[None, 4, 5])
    y = paddle.static.data(name='y', shape=[None, 4, 5])
    z = paddle.static.nn.fc(x, 100)
    loss = paddle.mean(z)
    scheduler = paddle.optimizer.lr_scheduler.LinearLrWarmup(
        learning_rate=0.5, warmup_steps=20, start_lr=0, end_lr=0.5, verbose=True)
    sgd = paddle.optimizer.SGD(learning_rate=scheduler)
    sgd.minimize(loss)

exe = paddle.static.Executor()
exe.run(start_prog)
for epoch in range(20):
    for batch_id in range(2):
        out = exe.run(
            main_prog,
            feed={
                'x': np.random.randn(3, 4, 5).astype('float32'),
                'y': np.random.randn(3, 4, 5).astype('float32')
            },
            fetch_list=loss.name)
    scheduler.step()