Skip to content

P
Paddle

BaiXuePrincess / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
“effdae160415040bea351db3f111a7320d41d871”上不存在“python/paddle/git@gitcode.net:s920243400/PaddleDetection.git”
未验证 提交 26d9b9e5 编写于 作者: C Chen Weihang 提交者： GitHub
浏览文件
操作

Polish English APIs' doc of several Optimizers (#20166) (#20333) 

test=release/1.6 test=document_fix
上级 9612ee60
隐藏空白更改
内联 并排
Showing with 311 addition and 161 deletion
paddle/fluid/API.spec
浏览文件 @ 26d9b9e5
...@@ -912,116 +912,116 @@ paddle.fluid.optimizer.SGDOptimizer ('paddle.fluid.optimizer.SGDOptimizer', ('do ...@@ -912,116 +912,116 @@ paddle.fluid.optimizer.SGDOptimizer ('paddle.fluid.optimizer.SGDOptimizer', ('do
paddle.fluid.optimizer.SGDOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'regularization', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.SGDOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'regularization', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.SGDOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610')) paddle.fluid.optimizer.SGDOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610'))
paddle.fluid.optimizer.SGDOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.SGDOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.SGDOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.SGDOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'd2a59fb4c678a2feb231fc5b1adcc9b4'))
paddle.fluid.optimizer.SGDOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.SGDOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.SGDOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) paddle.fluid.optimizer.SGDOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.SGDOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) paddle.fluid.optimizer.SGDOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '8387af01322a6defc92c1832faccd304'))
paddle.fluid.optimizer.MomentumOptimizer ('paddle.fluid.optimizer.MomentumOptimizer', ('document', 'a72bd02e5459e64596897d190413d449')) paddle.fluid.optimizer.MomentumOptimizer ('paddle.fluid.optimizer.MomentumOptimizer', ('document', 'a72bd02e5459e64596897d190413d449'))
paddle.fluid.optimizer.MomentumOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'momentum', 'use_nesterov', 'regularization', 'name'], varargs=None, keywords=None, defaults=(False, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.MomentumOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'momentum', 'use_nesterov', 'regularization', 'name'], varargs=None, keywords=None, defaults=(False, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.MomentumOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610')) paddle.fluid.optimizer.MomentumOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610'))
paddle.fluid.optimizer.MomentumOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.MomentumOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.MomentumOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.MomentumOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'd2a59fb4c678a2feb231fc5b1adcc9b4'))
paddle.fluid.optimizer.MomentumOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.MomentumOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.MomentumOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) paddle.fluid.optimizer.MomentumOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.MomentumOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) paddle.fluid.optimizer.MomentumOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '8387af01322a6defc92c1832faccd304'))
paddle.fluid.optimizer.AdagradOptimizer ('paddle.fluid.optimizer.AdagradOptimizer', ('document', 'a1d4f0682cde43ad34432b1338aadf04')) paddle.fluid.optimizer.AdagradOptimizer ('paddle.fluid.optimizer.AdagradOptimizer', ('document', 'b6508a25326275d44e658dd73bcd5593'))
paddle.fluid.optimizer.AdagradOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'epsilon', 'regularization', 'name', 'initial_accumulator_value'], varargs=None, keywords=None, defaults=(1e-06, None, None, 0.0)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.AdagradOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'epsilon', 'regularization', 'name', 'initial_accumulator_value'], varargs=None, keywords=None, defaults=(1e-06, None, None, 0.0)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.AdagradOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610')) paddle.fluid.optimizer.AdagradOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610'))
paddle.fluid.optimizer.AdagradOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.AdagradOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.AdagradOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.AdagradOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'd2a59fb4c678a2feb231fc5b1adcc9b4'))
paddle.fluid.optimizer.AdagradOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.AdagradOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.AdagradOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) paddle.fluid.optimizer.AdagradOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.AdagradOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) paddle.fluid.optimizer.AdagradOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '8387af01322a6defc92c1832faccd304'))
paddle.fluid.optimizer.AdamOptimizer ('paddle.fluid.optimizer.AdamOptimizer', ('document', '6fe871b955cab6e267422d5af666dafa')) paddle.fluid.optimizer.AdamOptimizer ('paddle.fluid.optimizer.AdamOptimizer', ('document', '34e694895a702ba18a7b5ae618458217'))
paddle.fluid.optimizer.AdamOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name', 'lazy_mode'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.AdamOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name', 'lazy_mode'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.AdamOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610')) paddle.fluid.optimizer.AdamOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610'))
paddle.fluid.optimizer.AdamOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.AdamOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.AdamOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.AdamOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'd2a59fb4c678a2feb231fc5b1adcc9b4'))
paddle.fluid.optimizer.AdamOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.AdamOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.AdamOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) paddle.fluid.optimizer.AdamOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.AdamOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) paddle.fluid.optimizer.AdamOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '8387af01322a6defc92c1832faccd304'))
paddle.fluid.optimizer.AdamaxOptimizer ('paddle.fluid.optimizer.AdamaxOptimizer', ('document', '883fc4541214e8343d3a89711936e15d')) paddle.fluid.optimizer.AdamaxOptimizer ('paddle.fluid.optimizer.AdamaxOptimizer', ('document', '515bae60aa82e7fbd1046f59e56549bb'))
paddle.fluid.optimizer.AdamaxOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.AdamaxOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.AdamaxOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610')) paddle.fluid.optimizer.AdamaxOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610'))
paddle.fluid.optimizer.AdamaxOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.AdamaxOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.AdamaxOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.AdamaxOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'd2a59fb4c678a2feb231fc5b1adcc9b4'))
paddle.fluid.optimizer.AdamaxOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.AdamaxOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.AdamaxOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) paddle.fluid.optimizer.AdamaxOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.AdamaxOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) paddle.fluid.optimizer.AdamaxOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '8387af01322a6defc92c1832faccd304'))
paddle.fluid.optimizer.DpsgdOptimizer ('paddle.fluid.optimizer.DpsgdOptimizer', ('document', '71113c30b66c0f4035b10ebd8af8c5ad')) paddle.fluid.optimizer.DpsgdOptimizer ('paddle.fluid.optimizer.DpsgdOptimizer', ('document', '71113c30b66c0f4035b10ebd8af8c5ad'))
paddle.fluid.optimizer.DpsgdOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'clip', 'batch_size', 'sigma'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.DpsgdOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'clip', 'batch_size', 'sigma'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.DpsgdOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610')) paddle.fluid.optimizer.DpsgdOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610'))
paddle.fluid.optimizer.DpsgdOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.DpsgdOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.DpsgdOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.DpsgdOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'd2a59fb4c678a2feb231fc5b1adcc9b4'))
paddle.fluid.optimizer.DpsgdOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.DpsgdOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.DpsgdOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) paddle.fluid.optimizer.DpsgdOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.DpsgdOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) paddle.fluid.optimizer.DpsgdOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '8387af01322a6defc92c1832faccd304'))
paddle.fluid.optimizer.DecayedAdagradOptimizer ('paddle.fluid.optimizer.DecayedAdagradOptimizer', ('document', 'e76838a8586bf2e58e6b5cdd2f67f780')) paddle.fluid.optimizer.DecayedAdagradOptimizer ('paddle.fluid.optimizer.DecayedAdagradOptimizer', ('document', '6f5adb9f881a3b182236e344033dbd44'))
paddle.fluid.optimizer.DecayedAdagradOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'decay', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.95, 1e-06, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.DecayedAdagradOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'decay', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.95, 1e-06, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.DecayedAdagradOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610')) paddle.fluid.optimizer.DecayedAdagradOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610'))
paddle.fluid.optimizer.DecayedAdagradOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.DecayedAdagradOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.DecayedAdagradOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.DecayedAdagradOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'd2a59fb4c678a2feb231fc5b1adcc9b4'))
paddle.fluid.optimizer.DecayedAdagradOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.DecayedAdagradOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.DecayedAdagradOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) paddle.fluid.optimizer.DecayedAdagradOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.DecayedAdagradOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) paddle.fluid.optimizer.DecayedAdagradOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '8387af01322a6defc92c1832faccd304'))
paddle.fluid.optimizer.FtrlOptimizer ('paddle.fluid.optimizer.FtrlOptimizer', ('document', 'cba8aae0a267b9a4d8833ae79a00fc55')) paddle.fluid.optimizer.FtrlOptimizer ('paddle.fluid.optimizer.FtrlOptimizer', ('document', 'cba8aae0a267b9a4d8833ae79a00fc55'))
paddle.fluid.optimizer.FtrlOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'l1', 'l2', 'lr_power', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.0, 0.0, -0.5, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.FtrlOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'l1', 'l2', 'lr_power', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.0, 0.0, -0.5, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.FtrlOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610')) paddle.fluid.optimizer.FtrlOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610'))
paddle.fluid.optimizer.FtrlOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.FtrlOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.FtrlOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.FtrlOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'd2a59fb4c678a2feb231fc5b1adcc9b4'))
paddle.fluid.optimizer.FtrlOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.FtrlOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.FtrlOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) paddle.fluid.optimizer.FtrlOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.FtrlOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) paddle.fluid.optimizer.FtrlOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '8387af01322a6defc92c1832faccd304'))
paddle.fluid.optimizer.RMSPropOptimizer ('paddle.fluid.optimizer.RMSPropOptimizer', ('document', '5217bc4fc399010021d6b70541005780')) paddle.fluid.optimizer.RMSPropOptimizer ('paddle.fluid.optimizer.RMSPropOptimizer', ('document', '5217bc4fc399010021d6b70541005780'))
paddle.fluid.optimizer.RMSPropOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'rho', 'epsilon', 'momentum', 'centered', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.95, 1e-06, 0.0, False, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.RMSPropOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'rho', 'epsilon', 'momentum', 'centered', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.95, 1e-06, 0.0, False, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.RMSPropOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610')) paddle.fluid.optimizer.RMSPropOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610'))
paddle.fluid.optimizer.RMSPropOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.RMSPropOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.RMSPropOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.RMSPropOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'd2a59fb4c678a2feb231fc5b1adcc9b4'))
paddle.fluid.optimizer.RMSPropOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.RMSPropOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.RMSPropOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) paddle.fluid.optimizer.RMSPropOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.RMSPropOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) paddle.fluid.optimizer.RMSPropOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '8387af01322a6defc92c1832faccd304'))
paddle.fluid.optimizer.AdadeltaOptimizer ('paddle.fluid.optimizer.AdadeltaOptimizer', ('document', 'f4354aef5e3b9134fa68919b75a3a097')) paddle.fluid.optimizer.AdadeltaOptimizer ('paddle.fluid.optimizer.AdadeltaOptimizer', ('document', 'f4354aef5e3b9134fa68919b75a3a097'))
paddle.fluid.optimizer.AdadeltaOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'epsilon', 'rho', 'regularization', 'name'], varargs=None, keywords=None, defaults=(1e-06, 0.95, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.AdadeltaOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'epsilon', 'rho', 'regularization', 'name'], varargs=None, keywords=None, defaults=(1e-06, 0.95, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.AdadeltaOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610')) paddle.fluid.optimizer.AdadeltaOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610'))
paddle.fluid.optimizer.AdadeltaOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.AdadeltaOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.AdadeltaOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.AdadeltaOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'd2a59fb4c678a2feb231fc5b1adcc9b4'))
paddle.fluid.optimizer.AdadeltaOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.AdadeltaOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.AdadeltaOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) paddle.fluid.optimizer.AdadeltaOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.AdadeltaOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) paddle.fluid.optimizer.AdadeltaOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '8387af01322a6defc92c1832faccd304'))
paddle.fluid.optimizer.ModelAverage ('paddle.fluid.optimizer.ModelAverage', ('document', '0a0adcd60230630e21fe1ef46362dbc0')) paddle.fluid.optimizer.ModelAverage ('paddle.fluid.optimizer.ModelAverage', ('document', 'e039a4b422ce5b360b4d777481d64975'))
paddle.fluid.optimizer.ModelAverage.__init__ (ArgSpec(args=['self', 'average_window_rate', 'min_average_window', 'max_average_window', 'regularization', 'name'], varargs=None, keywords=None, defaults=(10000, 10000, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.ModelAverage.__init__ (ArgSpec(args=['self', 'average_window_rate', 'min_average_window', 'max_average_window', 'regularization', 'name'], varargs=None, keywords=None, defaults=(10000, 10000, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.ModelAverage.apply (ArgSpec(args=['self', 'executor', 'need_restore'], varargs=None, keywords=None, defaults=(True,)), ('document', '648010d0ac1fa707dac0b89f74b0e35c')) paddle.fluid.optimizer.ModelAverage.apply (ArgSpec(args=['self', 'executor', 'need_restore'], varargs=None, keywords=None, defaults=(True,)), ('document', '582c279ec4792edf2d95a3064578da7b'))
paddle.fluid.optimizer.ModelAverage.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610')) paddle.fluid.optimizer.ModelAverage.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610'))
paddle.fluid.optimizer.ModelAverage.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.ModelAverage.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.ModelAverage.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.ModelAverage.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'd2a59fb4c678a2feb231fc5b1adcc9b4'))
paddle.fluid.optimizer.ModelAverage.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.ModelAverage.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.ModelAverage.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) paddle.fluid.optimizer.ModelAverage.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.ModelAverage.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) paddle.fluid.optimizer.ModelAverage.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '8387af01322a6defc92c1832faccd304'))
paddle.fluid.optimizer.ModelAverage.restore (ArgSpec(args=['self', 'executor'], varargs=None, keywords=None, defaults=None), ('document', '5f14ea4adda2791e1c3b37ff327f6a83')) paddle.fluid.optimizer.ModelAverage.restore (ArgSpec(args=['self', 'executor'], varargs=None, keywords=None, defaults=None), ('document', '7917cbe4d3ed7954ae73360fbccc39f6'))
paddle.fluid.optimizer.LarsMomentumOptimizer ('paddle.fluid.optimizer.LarsMomentumOptimizer', ('document', '030b9092a96a409b1bf5446bf45d0659')) paddle.fluid.optimizer.LarsMomentumOptimizer ('paddle.fluid.optimizer.LarsMomentumOptimizer', ('document', '030b9092a96a409b1bf5446bf45d0659'))
paddle.fluid.optimizer.LarsMomentumOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'momentum', 'lars_coeff', 'lars_weight_decay', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.0005, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.LarsMomentumOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'momentum', 'lars_coeff', 'lars_weight_decay', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.0005, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.LarsMomentumOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610')) paddle.fluid.optimizer.LarsMomentumOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610'))
paddle.fluid.optimizer.LarsMomentumOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.LarsMomentumOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.LarsMomentumOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.LarsMomentumOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'd2a59fb4c678a2feb231fc5b1adcc9b4'))
paddle.fluid.optimizer.LarsMomentumOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.LarsMomentumOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.LarsMomentumOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) paddle.fluid.optimizer.LarsMomentumOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.LarsMomentumOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) paddle.fluid.optimizer.LarsMomentumOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '8387af01322a6defc92c1832faccd304'))
paddle.fluid.optimizer.DGCMomentumOptimizer ('paddle.fluid.optimizer.DGCMomentumOptimizer', ('document', 'facdbef1b4871d0cf74c736ff2e94720')) paddle.fluid.optimizer.DGCMomentumOptimizer ('paddle.fluid.optimizer.DGCMomentumOptimizer', ('document', 'facdbef1b4871d0cf74c736ff2e94720'))
paddle.fluid.optimizer.DGCMomentumOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'momentum', 'rampup_begin_step', 'rampup_step', 'sparsity', 'use_nesterov', 'local_grad_clip_norm', 'num_trainers', 'regularization', 'name'], varargs=None, keywords=None, defaults=(1, [0.999], False, None, None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.DGCMomentumOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'momentum', 'rampup_begin_step', 'rampup_step', 'sparsity', 'use_nesterov', 'local_grad_clip_norm', 'num_trainers', 'regularization', 'name'], varargs=None, keywords=None, defaults=(1, [0.999], False, None, None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.DGCMomentumOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610')) paddle.fluid.optimizer.DGCMomentumOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610'))
paddle.fluid.optimizer.DGCMomentumOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.DGCMomentumOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.DGCMomentumOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.DGCMomentumOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'd2a59fb4c678a2feb231fc5b1adcc9b4'))
paddle.fluid.optimizer.DGCMomentumOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.DGCMomentumOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.DGCMomentumOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) paddle.fluid.optimizer.DGCMomentumOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.DGCMomentumOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) paddle.fluid.optimizer.DGCMomentumOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '8387af01322a6defc92c1832faccd304'))
paddle.fluid.optimizer.LambOptimizer ('paddle.fluid.optimizer.LambOptimizer', ('document', '7dd8b270156a52f1f6b4663336960893')) paddle.fluid.optimizer.LambOptimizer ('paddle.fluid.optimizer.LambOptimizer', ('document', '7dd8b270156a52f1f6b4663336960893'))
paddle.fluid.optimizer.LambOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'lamb_weight_decay', 'beta1', 'beta2', 'epsilon', 'regularization', 'exclude_from_weight_decay_fn', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.01, 0.9, 0.999, 1e-06, None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.LambOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'lamb_weight_decay', 'beta1', 'beta2', 'epsilon', 'regularization', 'exclude_from_weight_decay_fn', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.01, 0.9, 0.999, 1e-06, None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.LambOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610')) paddle.fluid.optimizer.LambOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610'))
paddle.fluid.optimizer.LambOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.LambOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.LambOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.LambOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'd2a59fb4c678a2feb231fc5b1adcc9b4'))
paddle.fluid.optimizer.LambOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.LambOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.LambOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) paddle.fluid.optimizer.LambOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.LambOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) paddle.fluid.optimizer.LambOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '8387af01322a6defc92c1832faccd304'))
paddle.fluid.optimizer.ExponentialMovingAverage ('paddle.fluid.optimizer.ExponentialMovingAverage', ('document', 'a38b7d5b9f17a295ed15d4c1b9ab4cd0')) paddle.fluid.optimizer.ExponentialMovingAverage ('paddle.fluid.optimizer.ExponentialMovingAverage', ('document', 'a38b7d5b9f17a295ed15d4c1b9ab4cd0'))
paddle.fluid.optimizer.ExponentialMovingAverage.__init__ (ArgSpec(args=['self', 'decay', 'thres_steps', 'name'], varargs=None, keywords=None, defaults=(0.999, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.ExponentialMovingAverage.__init__ (ArgSpec(args=['self', 'decay', 'thres_steps', 'name'], varargs=None, keywords=None, defaults=(0.999, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.ExponentialMovingAverage.apply (ArgSpec(args=['self', 'executor', 'need_restore'], varargs=None, keywords=None, defaults=(True,)), ('document', '30f494752ac8921dc5835a63637f453a')) paddle.fluid.optimizer.ExponentialMovingAverage.apply (ArgSpec(args=['self', 'executor', 'need_restore'], varargs=None, keywords=None, defaults=(True,)), ('document', '30f494752ac8921dc5835a63637f453a'))
......
python/paddle/fluid/optimizer.py
浏览文件 @ 26d9b9e5
...@@ -449,23 +449,28 @@ class Optimizer(object): ...@@ -449,23 +449,28 @@ class Optimizer(object):
no_grad_set=None, no_grad_set=None,
callbacks=None): callbacks=None):
""" """
First part of `minimize`, do auto-diff to append backward ops for The first part of ``minimize``, do auto-diff to append backward operations for
the current program. the current program.
Args: Args:
loss (Variable): loss variable to run optimizations. loss (Variable): ``loss`` variable to run optimizations.
startup_program (Program): startup_program for initializing parameters startup_program (Program, optional): :ref:`api_fluid_Program` for
in `parameter_list`. initializing parameters in ``parameter_list``. The default value
parameter_list (list): list of Variables to update. is None, at this time :ref:`api_fluid_default_startup_program` will be used.
no_grad_set (set|None): set of Variables should be ignored. parameter_list (list, optional): List of ``Variable`` names to update
callbacks (list|None): list of callables to run when appending backward to minimize ``loss``. The default value is None, at this time all parameters
operator for one parameter. will be updated.
no_grad_set (set, optional): Set of ``Variable`` objects that don't need
to be updated. The default value is None.
callbacks (list, optional): list of callable objects to run when appending backward
operator for one parameter. The default value is None.
Return: Return:
list: list of (param, grad) pair, grad is the output of backward. list: list of (param, grad) variable pairs, param is ``Parameter``,
grad is the gradient value corresponding to the parameter.
Examples: Examples:
See examples in `apply_gradients`. See examples in ``apply_gradients``.
""" """
no_grad_set = self._get_no_grad_set(loss, no_grad_set) no_grad_set = self._get_no_grad_set(loss, no_grad_set)
...@@ -597,22 +602,30 @@ class Optimizer(object): ...@@ -597,22 +602,30 @@ class Optimizer(object):
no_grad_set=None, no_grad_set=None,
grad_clip=None): grad_clip=None):
""" """
Add operations to minimize `loss` by updating `parameter_list`. Add operations to minimize ``loss`` by updating ``parameter_list``.
This method combines interface `backward()` and
`apply_gradients()` into one.
Args: Args:
loss (Variable): loss variable to run optimizations. loss (Variable): A ``Variable`` containing the value to minimize.
startup_program (Program): startup_program for initializing parameters startup_program (Program, optional): :ref:`api_fluid_Program` for
in `parameter_list`. initializing parameters in ``parameter_list``. The default value
parameter_list (list): list of Variables to update. is None, at this time :ref:`api_fluid_default_startup_program` will be used.
no_grad_set (set|None): set of Variables should be ignored. parameter_list (list, optional): List of ``Variable`` names to update
grad_clip (GradClipBase|None) : Gradient clip strategy to minimize ``loss``. The default value is None, at this time all parameters
will be updated.
no_grad_set (set, optional): Set of ``Variable`` objects that don't need
to be updated. The default value is None.
grad_clip (GradClipBase, optional) : Gradient clipping strategy, static
graph mode does not need to use this argument. Currently, this argument
only supports gradient clipping in dygraph mode. In the future, this
argument my be adjusted. The default value is None.
Returns: Returns:
tuple: (optimize_ops, params_grads) which are, list of operators appended; tuple: tuple (optimize_ops, params_grads), A list of operators appended
and list of (param, grad) Variables pair for optimization. by minimize and a list of (param, grad) variable pairs, param is
``Parameter``, grad is the gradient value corresponding to the parameter.
Examples:
Please refer to the example of current Optimizer.
""" """
assert isinstance(loss, Variable), "The loss should be an Variable." assert isinstance(loss, Variable), "The loss should be an Variable."
params_grads = self.backward( params_grads = self.backward(
...@@ -1173,9 +1186,10 @@ class LarsMomentumOptimizer(Optimizer): ...@@ -1173,9 +1186,10 @@ class LarsMomentumOptimizer(Optimizer):
class AdagradOptimizer(Optimizer): class AdagradOptimizer(Optimizer):
""" """
**Adaptive Gradient Algorithm (Adagrad)** The Adaptive Gradient optimizer (Adagrad for short) can adaptively assign
different learning rates to individual parameters.
The update is done as follows: The parameter ``param_out`` update rule with gradient ``grad``:
.. math:: .. math::
...@@ -1183,32 +1197,38 @@ class AdagradOptimizer(Optimizer): ...@@ -1183,32 +1197,38 @@ class AdagradOptimizer(Optimizer):
param\_out &= param - \\frac{learning\_rate * grad}{\sqrt{moment\_out} + \epsilon} param\_out &= param - \\frac{learning\_rate * grad}{\sqrt{moment\_out} + \epsilon}
The original paper(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf) Related paper: `Adaptive Subgradient Methods for Online Learning and
does not have the epsilon attribute. It is added here in our implementation Stochastic Optimization <http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf>`_.
as also proposed here: http://cs231n.github.io/neural-networks-3/#ada
The original paper does not have the ``epsilon`` attribute. It is added here
in our implementation as also proposed `Per-parameter adaptive learning rate
methods <http://cs231n.github.io/neural-networks-3/#ada>`_
for numerical stability to avoid the division by zero error. for numerical stability to avoid the division by zero error.
Args: Args:
learning_rate (float|Variable): the learning rate used to update parameters. \ learning_rate (float|Variable): The learning rate used to update ``Parameter``.
Can be a float value or a Variable with one float value as data element. It can be a float value or a ``Variable`` with a float type.
epsilon (float): a small float value for numerical stability. epsilon (float, optional): A small float value for numerical stability.
regularization: A Regularizer, such as The default value is 1e-06.
fluid.regularizer.L2DecayRegularizer. regularization (WeightDecayRegularizer, optional): A ``Regularizer``, such as
name: A optional name prefix. :ref:`api_fluid_regularizer_L2DecayRegularizer`. The default value is None.
initial_accumulator_value (float): Initial value for moment accumulator. name (str, optional): Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name`.
The default value is None.
initial_accumulator_value (float, optional): Initial value for moment accumulator.
The default value is 0.0.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import numpy as np import numpy as np
import paddle.fluid as fluid
np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32) np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
inp = fluid.layers.data( inp = fluid.data(name="inp", shape=[2, 2])
name="inp", shape=[2, 2], append_batch_size=False)
out = fluid.layers.fc(inp, size=3) out = fluid.layers.fc(inp, size=3)
out = fluid.layers.reduce_sum(out) out = fluid.layers.reduce_sum(out)
optimizer = fluid.optimizer.Adagrad(learning_rate=0.2) optimizer = fluid.optimizer.AdagradOptimizer(learning_rate=0.2)
optimizer.minimize(out) optimizer.minimize(out)
exe = fluid.Executor(fluid.CPUPlace()) exe = fluid.Executor(fluid.CPUPlace())
...@@ -1276,12 +1296,12 @@ class AdagradOptimizer(Optimizer): ...@@ -1276,12 +1296,12 @@ class AdagradOptimizer(Optimizer):
class AdamOptimizer(Optimizer): class AdamOptimizer(Optimizer):
""" """
This implements the Adam optimizer from Section 2 of the Adam The Adam optimzier uses an optimization described at the end
paper : https://arxiv.org/abs/1412.6980. of section 2 of `Adam paper <https://arxiv.org/abs/1412.6980>`_ ,
Adam is a first-order gradient-based optimization method based on it can dynamically adjusts the learning rate of each parameter using
adaptive estimates of lower-order moments. the 1st moment estimates and the 2nd moment estimates of the gradient.
Adam updates: The parameter ``param_out`` update rule with gradient ``grad``:
.. math:: .. math::
...@@ -1296,20 +1316,29 @@ class AdamOptimizer(Optimizer): ...@@ -1296,20 +1316,29 @@ class AdamOptimizer(Optimizer):
param\_out & = param - learning\_rate * \\frac{moment\_1}{\sqrt{moment\_2} + \epsilon} param\_out & = param - learning\_rate * \\frac{moment\_1}{\sqrt{moment\_2} + \epsilon}
Related paper: `Adam: A Method for Stochastic Optimization <https://arxiv.org/abs/1412.6980>`_
Args: Args:
learning_rate (float|Variable): the learning rate used to update parameters. \ learning_rate (float|Variable, optional): The learning rate used to update ``Parameter``.
Can be a float value or a Variable with one float value as data element. It can be a float value or a ``Variable`` with a float type. The default value is 0.001.
beta1 (float): The exponential decay rate for the 1st moment estimates. beta1 (float, optional): The exponential decay rate for the 1st moment estimates.
beta2 (float): The exponential decay rate for the 2nd moment estimates. The default value is 0.9.
epsilon (float): a small float value for numerical stability. beta2 (float, optional): The exponential decay rate for the 2nd moment estimates.
regularization: A Regularizer, such as fluid.regularizer.L2DecayRegularizer. The default value is 0.999.
name: A optional name prefix. epsilon (float, optional): A small float value for numerical stability.
lazy_mode(bool: false): The official Adam algorithm has two moving-average accumulators The default value is 1e-08.
the accumulators are updated at every step. Every element of the two moving-average is updated regularization (WeightDecayRegularizer, optional): A ``Regularizer``, such as
in both dense mode and sparse mode. If the size of parameter is very large, then the update :ref:`api_fluid_regularizer_L2DecayRegularizer`. The default value is None.
may be very slow. The lazy mode only update the element that has gradient is the current name (str, optional): Normally there is no need for user to set this property.
mini-batch, so it will be much more faster. But this mode has different semantics with the For more information, please refer to :ref:`api_guide_Name`.
original Adam algorithm and may lead to different result. The default value is None.
lazy_mode (bool, optional): The official Adam algorithm has two moving-average accumulators.
The accumulators are updated at every step. Every element of the two moving-average
is updated in both dense mode and sparse mode. If the size of parameter is very large,
then the update may be very slow. The lazy mode only update the element that has
gradient in current mini-batch, so it will be much more faster. But this mode has
different semantics with the original Adam algorithm and may lead to different result.
The default value is False.
Examples: Examples:
.. code-block:: python .. code-block:: python
...@@ -1320,8 +1349,8 @@ class AdamOptimizer(Optimizer): ...@@ -1320,8 +1349,8 @@ class AdamOptimizer(Optimizer):
place = fluid.CPUPlace() place = fluid.CPUPlace()
main = fluid.Program() main = fluid.Program()
with fluid.program_guard(main): with fluid.program_guard(main):
x = fluid.layers.data(name='x', shape=[13], dtype='float32') x = fluid.data(name='x', shape=[None, 13], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='float32') y = fluid.data(name='y', shape=[None, 1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None) y_predict = fluid.layers.fc(input=x, size=1, act=None)
cost = fluid.layers.square_error_cost(input=y_predict, label=y) cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost) avg_cost = fluid.layers.mean(cost)
...@@ -1457,11 +1486,12 @@ class AdamOptimizer(Optimizer): ...@@ -1457,11 +1486,12 @@ class AdamOptimizer(Optimizer):
class AdamaxOptimizer(Optimizer): class AdamaxOptimizer(Optimizer):
""" """
We implement the Adamax optimizer from Section 7 of the Adam The Adamax optimizer is implemented based on the Adamax Optimization
paper: https://arxiv.org/abs/1412.6980. Adamax is a variant of the in Section 7 of `Adam paper <https://arxiv.org/abs/1412.6980>`_.
Adam algorithm based on the infinity norm. The Adamax algorithm is a variant of the Adam algorithm based on the infinite norm,
which makes the learning rate update algorithm more stable and simple.
Adamax updates: The parameter ``param_out`` update rule with gradient ``grad``:
.. math:: .. math::
...@@ -1475,10 +1505,28 @@ class AdamaxOptimizer(Optimizer): ...@@ -1475,10 +1505,28 @@ class AdamaxOptimizer(Optimizer):
param\_out & = param - learning\_rate * \\frac{moment\_out}{inf\_norm\_out} param\_out & = param - learning\_rate * \\frac{moment\_out}{inf\_norm\_out}
Related paper: `Adam: A Method for Stochastic Optimization <https://arxiv.org/abs/1412.6980>`_
The original paper does not have an ``epsilon`` attribute,
it is added here for numerical stability to prevent the division by 0 error.
The original paper does not have an epsilon attribute. Args:
However, it is added here for numerical stability to prevent the learning_rate (float|Variable, optional): The learning rate used to update ``Parameter``.
division by 0 error. It can be a float value or a ``Variable`` with a float type. The default value is 0.001.
beta1 (float, optional): The exponential decay rate for the 1st moment estimates.
The default value is 0.9.
beta2 (float, optional): The exponential decay rate for the 2nd moment estimates.
The default value is 0.999.
epsilon (float, optional): A small float value for numerical stability.
The default value is 1e-08.
regularization (WeightDecayRegularizer, optional): A ``Regularizer``, such as
:ref:`api_fluid_regularizer_L2DecayRegularizer`. The default value is None.
name (str, optional): Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name`.
The default value is None.
**Notes**:
**Currently, AdamaxOptimizer doesn't support sparse parameter optimization.**
Examples: Examples:
.. code-block:: python .. code-block:: python
...@@ -1493,10 +1541,10 @@ class AdamaxOptimizer(Optimizer): ...@@ -1493,10 +1541,10 @@ class AdamaxOptimizer(Optimizer):
train_program = fluid.Program() train_program = fluid.Program()
startup_program = fluid.Program() startup_program = fluid.Program()
with fluid.program_guard(train_program, startup_program): with fluid.program_guard(train_program, startup_program):
data = fluid.layers.data(name='X', shape=[1], dtype='float32') data = fluid.data(name='X', shape=[None, 1], dtype='float32')
hidden = fluid.layers.fc(input=data, size=10) hidden = fluid.layers.fc(input=data, size=10)
loss = fluid.layers.mean(hidden) loss = fluid.layers.mean(hidden)
adam = fluid.optimizer.Adamax(learning_rate=0.2) adam = fluid.optimizer.AdamaxOptimizer(learning_rate=0.2)
adam.minimize(loss) adam.minimize(loss)
# Run the startup program once and only once. # Run the startup program once and only once.
...@@ -1506,19 +1554,6 @@ class AdamaxOptimizer(Optimizer): ...@@ -1506,19 +1554,6 @@ class AdamaxOptimizer(Optimizer):
outs = exe.run(program=train_program, outs = exe.run(program=train_program,
feed={'X': x}, feed={'X': x},
fetch_list=[loss.name]) fetch_list=[loss.name])
Args:
learning_rate (float|Variable): the learning rate used to update parameters. \
Can be a float value or a Variable with one float value as data element.
beta1 (float): The exponential decay rate for the 1st moment estimates.
beta2 (float): The exponential decay rate for the 2nd moment estimates.
epsilon (float): a small float value for numerical stability.
regularization: A Regularizer, such as
fluid.regularizer.L2DecayRegularizer.
name: A optional name prefix.
Notes:
Currently, AdamaxOptimizer doesn't support sparse parameter optimization.
""" """
_moment_acc_str = "moment" _moment_acc_str = "moment"
_inf_norm_acc_str = "inf_norm" _inf_norm_acc_str = "inf_norm"
...@@ -1690,11 +1725,11 @@ class DpsgdOptimizer(Optimizer): ...@@ -1690,11 +1725,11 @@ class DpsgdOptimizer(Optimizer):
class DecayedAdagradOptimizer(Optimizer): class DecayedAdagradOptimizer(Optimizer):
""" """
**Decayed Adagrad Optimizer** The Decayed Adagrad optimizer can be seen as an Adagrad algorithm that introduces
the decay rate to solve the problem of a sharp drop in the learning rate
The original paper(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf) during model training when using the AdagradOptimizer.
The update is done as follows: The parameter ``param_out`` update rule with gradient ``grad``:
.. math:: .. math::
...@@ -1702,34 +1737,37 @@ class DecayedAdagradOptimizer(Optimizer): ...@@ -1702,34 +1737,37 @@ class DecayedAdagradOptimizer(Optimizer):
param\_out & = param - \\frac{learning\_rate * grad}{\sqrt{moment\_out} + \epsilon} param\_out & = param - \\frac{learning\_rate * grad}{\sqrt{moment\_out} + \epsilon}
The original paper(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf) Related paper: `Adaptive Subgradient Methods for Online Learning and Stochastic
does not have an epsilon attribute. It is added here for numerical Optimization <http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf>`_.
The original paper does not have an ``epsilon`` attribute. It is added here for numerical
stability to avoid the division by zero error. stability to avoid the division by zero error.
Args: Args:
learning_rate (float|Variable): the learning rate used to update parameters. \ learning_rate (float|Variable): The learning rate used to update ``Parameter``.
Can be a float value or a Variable with one float value as data element. It can be a float value or a ``Variable`` with a float type.
decay (float): decay rate. decay (float, optional): The decay rate. The default value is 0.95.
epsilon (float): a small float value for numerical stability. epsilon (float, optional): A small float value for numerical stability.
regularization: A Regularizer, such as The default value is 1e-06.
fluid.regularizer.L2DecayRegularizer. regularization (WeightDecayRegularizer, optional): A ``Regularizer``, such as
name: A optional name prefix. :ref:`api_fluid_regularizer_L2DecayRegularizer`. The default value is None.
name (str, optional): Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name`.
The default value is None.
**Notes**:
**Currently, DecayedAdagradOptimizer doesn't support sparse parameter optimization.**
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
import paddle.fluid.layers as layers
from paddle.fluid.optimizer import DecayedAdagrad
x = layers.data( name='x', shape=[-1, 10], dtype='float32' ) x = fluid.data( name='x', shape=[None, 10], dtype='float32' )
trans = layers.fc( x, 100 ) trans = fluid.layers.fc( x, 100 )
cost = layers.reduce_mean( trans ) cost = fluid.layers.reduce_mean( trans )
optimizer = fluid.optimizer.DecayedAdagrad(learning_rate=0.2) optimizer = fluid.optimizer.DecayedAdagradOptimizer(learning_rate=0.2)
optimizer.minimize(cost) optimizer.minimize(cost)
Notes:
Currently, DecayedAdagradOptimizer doesn't support sparse parameter optimization.
""" """
_moment_acc_str = "moment" _moment_acc_str = "moment"
...@@ -2359,21 +2397,45 @@ Lamb = LambOptimizer ...@@ -2359,21 +2397,45 @@ Lamb = LambOptimizer
class ModelAverage(Optimizer): class ModelAverage(Optimizer):
"""Accumulate the average of parameters within sliding window. The average """
result will be saved in temporary variables which can be applied to The ModelAverage optimizer accumulates specific continuous historical parameters
parameter variables of current model by calling 'apply()' method. And the during training. The accumulated historical range can be controlled by the passed
'restore()' method is used to restore the parameter values of current model. ``average_window_rate`` argument. The averaged ``Parameter`` are used in the prediction,
which usually can improve the accuracy of the prediction.
Accumulate the average of the ``Parameter`` in the sliding window, the result will be saved
in a temporary variable, can be applied to the current model's ``Parameter`` by calling
the ``apply()`` method, and the current model ``Parameter`` can be restored by calling
the ``restore()`` method.
The window size for calculating the average is determined by ``average_window_rate``,
``min_average_window``, ``max_average_window`` and the current ``Parameter`` update times (num_updates).
The size of average window is determined by average_window_rate, When the cumulative times (num_accumulates) is greater than the specific window
min_average_window, max_average_window and current update times. threshold (average_window), the accumulated ``Parameter`` temporary variable is set to 0.0.
The following example will help to understand the role of these arguments:
::
if num_accumulates >= min_average_window and num_accumulates >= min(max_average_window, num_updates * average_window_rate):
num_accumulates = 0
In the above conditional judgment statement, ``num_accumulates`` indicates the current
accumulated number, which can be abstractly understood as the length of the cumulative window.
The length of the window must be at least the length set by the ``min_average_window`` argument,
and cannot exceed the length specified by the ``max_average_window`` argument or
``num_updates * average_window_rate``, where ``num_updates`` indicates the current ``Parameter``
update times, ``average_window_rate`` is a coefficient that calculates the length of the window.
Args: Args:
average_window_rate: The rate of average window. average_window_rate (float): The calculate ratio of the window length relative to ``Parameter`` update times.
min_average_window: The minimum size of average window. min_average_window (int, optional): the minimum size of average window length. The default value is 10000.
max_average_window: The maximum size of average window. max_average_window (int, optional): The maximum size of average window length. The default value is 10000.
regularization: A Regularizer, such as regularization (WeightDecayRegularizer, optional): A ``Regularizer``, such as
fluid.regularizer.L2DecayRegularizer. :ref:`api_fluid_regularizer_L2DecayRegularizer`. The default value is None.
name: A optional name prefix. name (str, optional): Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name`.
The default value is None.
Examples: Examples:
...@@ -2390,7 +2452,7 @@ class ModelAverage(Optimizer): ...@@ -2390,7 +2452,7 @@ class ModelAverage(Optimizer):
startup_program = fluid.Program() startup_program = fluid.Program()
with fluid.program_guard(train_program, startup_program): with fluid.program_guard(train_program, startup_program):
# build net # build net
data = fluid.layers.data(name='X', shape=[1], dtype='float32') data = fluid.data(name='X', shape=[None, 1], dtype='float32')
hidden = fluid.layers.fc(input=data, size=10) hidden = fluid.layers.fc(input=data, size=10)
loss = fluid.layers.mean(hidden) loss = fluid.layers.mean(hidden)
optimizer = fluid.optimizer.Momentum(learning_rate=0.2, momentum=0.1) optimizer = fluid.optimizer.Momentum(learning_rate=0.2, momentum=0.1)
...@@ -2399,13 +2461,14 @@ class ModelAverage(Optimizer): ...@@ -2399,13 +2461,14 @@ class ModelAverage(Optimizer):
# build ModelAverage optimizer # build ModelAverage optimizer
model_average = fluid.optimizer.ModelAverage(0.15, model_average = fluid.optimizer.ModelAverage(0.15,
min_average_window=10000, min_average_window=10000,
max_average_window=20000) max_average_window=12500)
exe.run(startup_program) exe.run(startup_program)
x = numpy.random.random(size=(10, 1)).astype('float32') for i in range(12500):
outs = exe.run(program=train_program, x = numpy.random.random(size=(10, 1)).astype('float32')
feed={'X': x}, outs = exe.run(program=train_program,
fetch_list=[loss.name]) feed={'X': x},
fetch_list=[loss.name])
# apply ModelAverage # apply ModelAverage
with model_average.apply(exe): with model_average.apply(exe):
...@@ -2526,11 +2589,54 @@ class ModelAverage(Optimizer): ...@@ -2526,11 +2589,54 @@ class ModelAverage(Optimizer):
@signature_safe_contextmanager @signature_safe_contextmanager
def apply(self, executor, need_restore=True): def apply(self, executor, need_restore=True):
"""Apply average values to parameters of current model. """
Apply the average of the cumulative ``Parameter`` to the parameters of the current model.
Args: Args:
executor(fluid.Executor): current executor. executor(fluid.Executor): The current network executor.
need_restore(bool): If you finally need to do restore, set it to True. Default is True. need_restore(bool): Restore flag variable, if set to True, the network will restore
the parameters of the network to the default value, if set to False,
it will not be restored. The default value is True.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy
# First create the Executor.
place = fluid.CPUPlace() # fluid.CUDAPlace(0)
exe = fluid.Executor(place)
train_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(train_program, startup_program):
# build net
data = fluid.data(name='X', shape=[None, 1], dtype='float32')
hidden = fluid.layers.fc(input=data, size=10)
loss = fluid.layers.mean(hidden)
optimizer = fluid.optimizer.Momentum(learning_rate=0.2, momentum=0.1)
optimizer.minimize(loss)
# build ModelAverage optimizer
model_average = fluid.optimizer.ModelAverage(0.15,
min_average_window=10000,
max_average_window=12500)
exe.run(startup_program)
for i in range(12500):
x = numpy.random.random(size=(10, 1)).astype('float32')
outs = exe.run(program=train_program,
feed={'X': x},
fetch_list=[loss.name])
# apply ModelAverage
with model_average.apply(exe):
x = numpy.random.random(size=(10, 1)).astype('float32')
exe.run(program=train_program,
feed={'X': x},
fetch_list=[loss.name])
""" """
executor.run(self.apply_program) executor.run(self.apply_program)
try: try:
...@@ -2540,10 +2646,54 @@ class ModelAverage(Optimizer): ...@@ -2540,10 +2646,54 @@ class ModelAverage(Optimizer):
self.restore(executor) self.restore(executor)
def restore(self, executor): def restore(self, executor):
"""Restore parameter values of current model. """
Restore ``Parameter`` values of current model.
Args: Args:
executor(fluid.Executor): current executor. executor(fluid.Executor): The current network executor.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy
# First create the Executor.
place = fluid.CPUPlace() # fluid.CUDAPlace(0)
exe = fluid.Executor(place)
train_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(train_program, startup_program):
# build net
data = fluid.data(name='X', shape=[None, 1], dtype='float32')
hidden = fluid.layers.fc(input=data, size=10)
loss = fluid.layers.mean(hidden)
optimizer = fluid.optimizer.Momentum(learning_rate=0.2, momentum=0.1)
optimizer.minimize(loss)
# build ModelAverage optimizer
model_average = fluid.optimizer.ModelAverage(0.15,
min_average_window=10000,
max_average_window=12500)
exe.run(startup_program)
for i in range(12500):
x = numpy.random.random(size=(10, 1)).astype('float32')
outs = exe.run(program=train_program,
feed={'X': x},
fetch_list=[loss.name])
# apply ModelAverage
with model_average.apply(exe, False):
x = numpy.random.random(size=(10, 1)).astype('float32')
exe.run(program=train_program,
feed={'X': x},
fetch_list=[loss.name])
# restore Parameters
model_average.restore(exe)
""" """
executor.run(self.restore_program) executor.run(self.restore_program)
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或