# Copyright (c) 2019 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. import unittest import paddle from paddle import fluid def get_places(): places = [fluid.CPUPlace()] if fluid.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) return places def main_test_func(place, dtype): main = fluid.Program() startup = fluid.Program() with fluid.program_guard(main, startup): with fluid.scope_guard(fluid.Scope()): x = paddle.static.data(name='x', shape=[None, 13], dtype=dtype) y = paddle.static.data(name='y', shape=[None, 1], dtype=dtype) y_predict = paddle.static.nn.fc(x, size=1) cost = paddle.nn.functional.square_error_cost( input=y_predict, label=y ) avg_cost = paddle.mean(cost) adam_optimizer = paddle.optimizer.Adam(0.01) adam_optimizer.minimize(avg_cost) fetch_list = [avg_cost] train_reader = paddle.batch( paddle.dataset.uci_housing.train(), batch_size=1 ) feeder = fluid.DataFeeder(place=place, feed_list=[x, y]) exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) for data in train_reader(): exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list) class AdamFp32Test(unittest.TestCase): def setUp(self): self.dtype = 'float32' def test_main(self): for p in get_places(): main_test_func(p, self.dtype) class AdamFp64Test(AdamFp32Test): def setUp(self): self.dtype = 'float64' if __name__ == '__main__': unittest.main()