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.
86 lines
3.4 KiB
86 lines
3.4 KiB
import unittest
|
|
|
|
import paddle.v2.fluid.framework as framework
|
|
import paddle.v2.fluid.optimizer as optimizer
|
|
import paddle.v2.fluid.regularizer as regularizer
|
|
from paddle.v2.fluid.backward import append_backward_ops
|
|
|
|
|
|
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_ops(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, 'elementwise_add')
|
|
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_ops(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, 'elementwise_add')
|
|
self.assertEqual(block.ops[-2].type, 'scale')
|
|
self.assertEqual(block.ops[-3].type, 'sign')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|