提交 5fd4bee2 编写于 作者: K kexinzhao 提交者: GitHub

Merge pull request #4977 from kexinzhao/python_adagrad

Adding interface for the adagrad optimizer
import paddle.v2.framework.framework as framework import paddle.v2.framework.framework as framework
from collections import defaultdict from collections import defaultdict
__all__ = ['SGDOptimizer', 'MomentumOptimizer'] __all__ = ['SGDOptimizer', 'MomentumOptimizer', 'AdagradOptimizer']
class Optimizer(object): class Optimizer(object):
...@@ -272,3 +272,60 @@ class MomentumOptimizer(Optimizer): ...@@ -272,3 +272,60 @@ class MomentumOptimizer(Optimizer):
attrs={"mu": self._momentum}) attrs={"mu": self._momentum})
return momentum_op return momentum_op
class AdagradOptimizer(Optimizer):
"""Simple Adagrad optimizer with moment state
"""
_moment_acc_str = "moment"
def __init__(self, learning_rate, epsilon=1.0e-6):
assert learning_rate is not None
assert epsilon is not None
super(AdagradOptimizer, self).__init__()
self.type = "adagrad"
self._learning_rate = learning_rate
self._epsilon = epsilon
def _initialize_tensors(self, block):
assert isinstance(block, framework.Block)
lr_shape = [1]
# create a variable for learning_rate
self._lr = block.create_var(
dtype="float32", shape=lr_shape, lod_level=0)
# create an op to init the learning_rate
# FIXME: Fix when Initialization design has been implemented
# https://github.com/PaddlePaddle/Paddle/pull/4852
block.append_op(
type="fill_constant",
outputs={"Out": self._lr},
attrs={"shape": lr_shape,
"value": self._learning_rate})
def _create_accumulators(self, block, parameters):
assert isinstance(block, framework.Block)
for p in parameters:
self._add_accumulator(block, self._moment_acc_str, p, 'float32')
def _append_optimize_op(self, block, param_and_grad):
assert isinstance(block, framework.Block)
moment_acc = self._get_accumulator(self._moment_acc_str,
param_and_grad[0])
# create the adagrad optimizer op
adagrad_op = block.append_op(
type=self.type,
inputs={
"Param": param_and_grad[0],
"Grad": param_and_grad[1],
"Moment": moment_acc,
"LearningRate": self._lr
},
outputs={"ParamOut": param_and_grad[0],
"MomentOut": moment_acc},
attrs={"epsilon": self._epsilon})
return adagrad_op
...@@ -69,5 +69,46 @@ class TestMomentumOptimizer(unittest.TestCase): ...@@ -69,5 +69,46 @@ class TestMomentumOptimizer(unittest.TestCase):
self.assertTrue(mul_x.name in velocity_acc) self.assertTrue(mul_x.name in velocity_acc)
class TestAdagradOptimizer(unittest.TestCase):
class MockAdagrad(optimizer.AdagradOptimizer):
def get_accumulators(self):
return self._accumulators
def get_moment_str(self):
return self._moment_acc_str
def test_adagrad_optimizer(self):
program = framework.Program()
block = program.global_block()
mul_x = block.create_parameter(
dtype="float32", shape=[5, 10], lod_level=0, name="mul.x")
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})
adagrad_optimizer = self.MockAdagrad(learning_rate=0.01, epsilon=1.0e-6)
params_grads = adagrad_optimizer.create_backward_pass(mul_out)
self.assertEqual(len(params_grads), 1)
self.assertEqual(len(adagrad_optimizer.get_accumulators()), 0)
opts = adagrad_optimizer.create_optimization_pass(params_grads, mul_out)
self.assertEqual(len(opts), 1)
adagrad_op = opts[0]
self.assertEqual(adagrad_op.type, "adagrad")
# check accumulators
accumulators = adagrad_optimizer.get_accumulators()
self.assertEqual(len(accumulators), 1)
self.assertTrue(adagrad_optimizer.get_moment_str() in accumulators)
moment_acc = accumulators[adagrad_optimizer.get_moment_str()]
self.assertEqual(len(moment_acc), 1)
self.assertTrue(mul_x.name in moment_acc)
if __name__ == '__main__': if __name__ == '__main__':
unittest.main() unittest.main()
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