#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import print_function

import unittest
import numpy as np
from op_test import OpTest
from paddle.fluid import core
from paddle.fluid.op import Operator
import paddle.fluid as fluid
import paddle


class TestLambOpV2(unittest.TestCase):
    def test_lamb_op(self):
        paddle.enable_static()
        place = fluid.CPUPlace()
        shape = [2, 3, 8, 8]
        exe = fluid.Executor(place)
        train_prog = fluid.Program()
        startup = fluid.Program()
        with fluid.program_guard(train_prog, startup):
            with fluid.unique_name.guard():
                data = fluid.data(name="data", shape=shape)
                conv = fluid.layers.conv2d(data, 8, 3)
                loss = fluid.layers.reduce_mean(conv)
                beta1 = 0.85
                beta2 = 0.95
                betas = [beta1, beta2]
                opt = paddle.optimizer.Lamb(
                    learning_rate=1e-5, beta1=beta1, beta2=beta2, epsilon=1e-8)
                opt.minimize(loss)

        exe.run(startup)
        data_np = np.random.random(shape).astype('float32')
        rets = exe.run(train_prog, feed={"data": data_np}, fetch_list=[loss])
        assert rets[0] is not None


if __name__ == "__main__":
    unittest.main()