# 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 contextlib import unittest import numpy as np import six import paddle import paddle.fluid as fluid from paddle.fluid import core from paddle.fluid.optimizer import SGDOptimizer, Adam from paddle.fluid.imperative.nn import FC from paddle.fluid.imperative.base import to_variable from test_imperative_base import new_program_scope class MLP(fluid.imperative.Layer): def __init__(self, name_scope, param_attr=None, bias_attr=None): super(MLP, self).__init__(name_scope) self._fc1 = FC(self.full_name(), 10) self._fc2 = FC(self.full_name(), 10) def forward(self, inputs): y = self._fc1(inputs) y = self._fc2(y) return y class TestImperativeOptimizerBase(unittest.TestCase): def setUp(self): self.batch_num = 10 def get_optimizer(self): raise NotImplementedError() def _check_mlp(self): seed = 90 with fluid.imperative.guard(): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed mlp = MLP('mlp') optimizer = self.get_optimizer() train_reader = paddle.batch( paddle.dataset.mnist.train(), batch_size=128, drop_last=True) dy_param_init_value = {} for batch_id, data in enumerate(train_reader()): if batch_id >= self.batch_num: break dy_x_data = np.array( [x[0].reshape(1, 28, 28) for x in data]).astype('float32') y_data = np.array([x[1] for x in data]).astype('int64').reshape( 128, 1) img = to_variable(dy_x_data) label = to_variable(y_data) label._stop_gradient = True cost = mlp(img) avg_loss = fluid.layers.reduce_mean(cost) dy_out = avg_loss._numpy() if batch_id == 0: for param in mlp.parameters(): dy_param_init_value[param.name] = param._numpy() avg_loss._backward() optimizer.minimize(avg_loss) mlp.clear_gradients() dy_param_value = {} for param in mlp.parameters(): dy_param_value[param.name] = param._numpy() with new_program_scope(): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed exe = fluid.Executor(fluid.CPUPlace( ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0)) mlp = MLP('mlp') optimizer = self.get_optimizer() train_reader = paddle.batch( paddle.dataset.mnist.train(), batch_size=128, drop_last=True) img = fluid.layers.data( name='pixel', shape=[1, 28, 28], dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') cost = mlp(img) avg_loss = fluid.layers.reduce_mean(cost) optimizer.minimize(avg_loss) # initialize params and fetch them static_param_init_value = {} static_param_name_list = [] for param in mlp.parameters(): static_param_name_list.append(param.name) out = exe.run(fluid.default_startup_program(), fetch_list=static_param_name_list) for i in range(len(static_param_name_list)): static_param_init_value[static_param_name_list[i]] = out[i] for batch_id, data in enumerate(train_reader()): if batch_id >= self.batch_num: break static_x_data = np.array( [x[0].reshape(1, 28, 28) for x in data]).astype('float32') y_data = np.array([x[1] for x in data]).astype('int64').reshape( [128, 1]) fetch_list = [avg_loss.name] fetch_list.extend(static_param_name_list) out = exe.run(fluid.default_main_program(), feed={"pixel": static_x_data, "label": y_data}, fetch_list=fetch_list) static_param_value = {} static_out = out[0] for i in range(1, len(out)): static_param_value[static_param_name_list[i - 1]] = out[i] for key, value in six.iteritems(static_param_init_value): self.assertTrue(np.allclose(value, dy_param_init_value[key])) self.assertTrue(np.allclose(static_out, dy_out)) for key, value in six.iteritems(static_param_value): self.assertTrue(np.allclose(value, dy_param_value[key], atol=1e-5)) class TestImperativeOptimizerPiecewiseDecay(TestImperativeOptimizerBase): def get_optimizer(self): bd = [3, 6, 9] optimizer = SGDOptimizer(learning_rate=fluid.layers.piecewise_decay( boundaries=bd, values=[0.1 * (0.1**i) for i in range(len(bd) + 1)])) return optimizer def test_sgd(self): self._check_mlp() class TestImperativeOptimizerNaturalExpDecay(TestImperativeOptimizerBase): def get_optimizer(self): optimizer = SGDOptimizer(learning_rate=fluid.layers.natural_exp_decay( learning_rate=0.1, decay_steps=10000, decay_rate=0.5, staircase=True)) return optimizer def test_sgd(self): self._check_mlp() class TestImperativeOptimizerExponentialDecay(TestImperativeOptimizerBase): def get_optimizer(self): optimizer = SGDOptimizer(learning_rate=fluid.layers.exponential_decay( learning_rate=0.1, decay_steps=10000, decay_rate=0.5, staircase=True)) return optimizer def test_sgd(self): self._check_mlp() class TestImperativeOptimizerInverseTimeDecay(TestImperativeOptimizerBase): def get_optimizer(self): optimizer = Adam(learning_rate=fluid.layers.inverse_time_decay( learning_rate=0.1, decay_steps=10000, decay_rate=0.5, staircase=True)) return optimizer def test_adam(self): self._check_mlp() class TestImperativeOptimizerPolynomialDecay(TestImperativeOptimizerBase): def get_optimizer(self): optimizer = SGDOptimizer(learning_rate=fluid.layers.polynomial_decay( learning_rate=0.1, decay_steps=5, cycle=self.cycle)) return optimizer def test_sgd_cycle(self): self.cycle = True self._check_mlp() def test_sgd(self): self.cycle = False self._check_mlp() if __name__ == '__main__': unittest.main()