You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
236 lines
9.0 KiB
236 lines
9.0 KiB
# 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
|
|
from functools import partial
|
|
import contextlib
|
|
import numpy as np
|
|
import paddle
|
|
import paddle.fluid.core as core
|
|
import paddle.fluid as fluid
|
|
import paddle.fluid.framework as framework
|
|
import paddle.fluid.optimizer as optimizer
|
|
import paddle.fluid.regularizer as regularizer
|
|
from paddle.fluid.backward import append_backward
|
|
|
|
|
|
class TestL2DecayRegularizer(unittest.TestCase):
|
|
def test_l2decay_regularizer(self):
|
|
program = framework.Program()
|
|
block = program.global_block()
|
|
mul_x = block.create_parameter(
|
|
dtype="float32",
|
|
shape=[5, 10],
|
|
lod_level=0,
|
|
name="mul.x",
|
|
regularizer=regularizer.L2DecayRegularizer(0.5))
|
|
self.assertTrue(mul_x.regularizer is not None)
|
|
self.assertTrue(
|
|
isinstance(mul_x.regularizer, regularizer.L2DecayRegularizer))
|
|
mul_y = block.create_var(
|
|
dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
|
|
mul_out = block.create_var(
|
|
dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
|
|
block.append_op(
|
|
type="mul",
|
|
inputs={"X": mul_x,
|
|
"Y": mul_y},
|
|
outputs={"Out": mul_out},
|
|
attrs={"x_num_col_dims": 1})
|
|
mean_out = block.create_var(
|
|
dtype="float32", shape=[1], lod_level=0, name="mean.out")
|
|
block.append_op(
|
|
type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out})
|
|
params_grads = append_backward(mean_out)
|
|
self.assertEqual(len(params_grads), 1)
|
|
count_ops = len(block.ops)
|
|
params_grads = optimizer.append_regularization_ops(params_grads)
|
|
self.assertEqual(len(params_grads), 1)
|
|
self.assertEqual(len(block.ops), count_ops + 2)
|
|
self.assertEqual(block.ops[-1].type, 'sum')
|
|
self.assertEqual(block.ops[-2].type, 'scale')
|
|
|
|
|
|
class TestL1DecayRegularizer(unittest.TestCase):
|
|
def test_l2decay_regularizer(self):
|
|
program = framework.Program()
|
|
block = program.global_block()
|
|
mul_x = block.create_parameter(
|
|
dtype="float32",
|
|
shape=[5, 10],
|
|
lod_level=0,
|
|
name="mul.x",
|
|
regularizer=regularizer.L1DecayRegularizer(0.5))
|
|
self.assertTrue(mul_x.regularizer is not None)
|
|
self.assertTrue(
|
|
isinstance(mul_x.regularizer, regularizer.L1DecayRegularizer))
|
|
mul_y = block.create_var(
|
|
dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
|
|
mul_out = block.create_var(
|
|
dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
|
|
block.append_op(
|
|
type="mul",
|
|
inputs={"X": mul_x,
|
|
"Y": mul_y},
|
|
outputs={"Out": mul_out},
|
|
attrs={"x_num_col_dims": 1})
|
|
mean_out = block.create_var(
|
|
dtype="float32", shape=[1], lod_level=0, name="mean.out")
|
|
block.append_op(
|
|
type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out})
|
|
params_grads = append_backward(mean_out)
|
|
self.assertEqual(len(params_grads), 1)
|
|
count_ops = len(block.ops)
|
|
params_grads = optimizer.append_regularization_ops(params_grads)
|
|
self.assertEqual(len(params_grads), 1)
|
|
self.assertEqual(len(block.ops), count_ops + 3)
|
|
self.assertEqual(block.ops[-1].type, 'sum')
|
|
self.assertEqual(block.ops[-2].type, 'scale')
|
|
self.assertEqual(block.ops[-3].type, 'sign')
|
|
|
|
|
|
def bow_net(data,
|
|
label,
|
|
dict_dim,
|
|
is_sparse=False,
|
|
emb_dim=128,
|
|
hid_dim=128,
|
|
hid_dim2=96,
|
|
class_dim=2):
|
|
"""
|
|
BOW net
|
|
This model is from https://github.com/PaddlePaddle/models:
|
|
fluid/PaddleNLP/text_classification/nets.py
|
|
"""
|
|
emb = fluid.layers.embedding(
|
|
input=data, is_sparse=is_sparse, size=[dict_dim, emb_dim])
|
|
bow = fluid.layers.sequence_pool(input=emb, pool_type='sum')
|
|
bow_tanh = fluid.layers.tanh(bow)
|
|
fc_1 = fluid.layers.fc(input=bow_tanh, size=hid_dim, act="tanh")
|
|
fc_2 = fluid.layers.fc(input=fc_1, size=hid_dim2, act="tanh")
|
|
prediction = fluid.layers.fc(input=[fc_2], size=class_dim, act="softmax")
|
|
cost = fluid.layers.cross_entropy(input=prediction, label=label)
|
|
avg_cost = fluid.layers.mean(x=cost)
|
|
|
|
return avg_cost
|
|
|
|
|
|
class TestRegularizer(unittest.TestCase):
|
|
def setUp(self):
|
|
self.word_dict = paddle.dataset.imdb.word_dict()
|
|
reader = paddle.batch(
|
|
paddle.dataset.imdb.train(self.word_dict), batch_size=8)()
|
|
self.train_data = [next(reader) for _ in range(5)]
|
|
|
|
def get_places(self):
|
|
places = [core.CPUPlace()]
|
|
if core.is_compiled_with_cuda():
|
|
places.append(core.CUDAPlace(0))
|
|
return places
|
|
|
|
@contextlib.contextmanager
|
|
def scope_prog_guard(self, main_prog, startup_prog):
|
|
scope = fluid.core.Scope()
|
|
with fluid.unique_name.guard():
|
|
with fluid.scope_guard(scope):
|
|
with fluid.program_guard(main_prog, startup_prog):
|
|
yield
|
|
|
|
def run_program(self, place, feed_list):
|
|
exe = fluid.Executor(place)
|
|
feeder = fluid.DataFeeder(feed_list=feed_list, place=place)
|
|
exe.run(fluid.default_startup_program())
|
|
|
|
main_prog = fluid.default_main_program()
|
|
param_list = [var.name for var in main_prog.block(0).all_parameters()]
|
|
|
|
param_sum = []
|
|
for data in self.train_data:
|
|
out = exe.run(main_prog,
|
|
feed=feeder.feed(data),
|
|
fetch_list=param_list)
|
|
p_sum = 0
|
|
for v in out:
|
|
p_sum += np.sum(np.abs(v))
|
|
param_sum.append(p_sum)
|
|
return param_sum
|
|
|
|
def check_l2decay_regularizer(self, place, model):
|
|
main_prog = fluid.framework.Program()
|
|
startup_prog = fluid.framework.Program()
|
|
startup_prog.random_seed = 1
|
|
with self.scope_prog_guard(
|
|
main_prog=main_prog, startup_prog=startup_prog):
|
|
data = fluid.layers.data(
|
|
name="words", shape=[1], dtype="int64", lod_level=1)
|
|
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
|
|
|
|
avg_cost = model(data, label, len(self.word_dict))
|
|
|
|
optimizer = fluid.optimizer.Adagrad(
|
|
learning_rate=0.1,
|
|
regularization=fluid.regularizer.L2Decay(1.0))
|
|
optimizer.minimize(avg_cost)
|
|
param_sum = self.run_program(place, [data, label])
|
|
return param_sum
|
|
|
|
def check_l2decay(self, place, model):
|
|
main_prog = fluid.framework.Program()
|
|
startup_prog = fluid.framework.Program()
|
|
startup_prog.random_seed = 1
|
|
with self.scope_prog_guard(
|
|
main_prog=main_prog, startup_prog=startup_prog):
|
|
data = fluid.layers.data(
|
|
name="words", shape=[1], dtype="int64", lod_level=1)
|
|
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
|
|
|
|
avg_cost_l2 = model(data, label, len(self.word_dict))
|
|
|
|
param_list = fluid.default_main_program().block(0).all_parameters()
|
|
para_sum = []
|
|
for para in param_list:
|
|
para_mul = fluid.layers.square(x=para)
|
|
para_sum.append(fluid.layers.reduce_sum(input=para_mul))
|
|
avg_cost_l2 += fluid.layers.sums(para_sum) * .5
|
|
|
|
optimizer = fluid.optimizer.Adagrad(learning_rate=0.1)
|
|
optimizer.minimize(avg_cost_l2)
|
|
param_sum = self.run_program(place, [data, label])
|
|
return param_sum
|
|
|
|
def test_l2(self):
|
|
for place in self.get_places():
|
|
dense_sparse_p_sum = []
|
|
for sparse in [True, False]:
|
|
model = partial(bow_net, is_sparse=sparse)
|
|
framework_l2 = self.check_l2decay_regularizer(place, model)
|
|
l2 = self.check_l2decay(place, model)
|
|
assert len(l2) == len(framework_l2)
|
|
for i in range(len(l2)):
|
|
assert np.isclose(a=framework_l2[i], b=l2[i], rtol=5e-5)
|
|
dense_sparse_p_sum.append(framework_l2)
|
|
|
|
assert len(dense_sparse_p_sum[0]) == len(dense_sparse_p_sum[1])
|
|
for i in range(len(dense_sparse_p_sum[0])):
|
|
assert np.isclose(
|
|
a=dense_sparse_p_sum[0][i],
|
|
b=dense_sparse_p_sum[1][i],
|
|
rtol=5e-5)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|