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

test=release/1.6 test=document_fix

paddle/fluid/API.spec

@@ -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.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.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.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.__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_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.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.AdagradOptimizer ('paddle.fluid.optimizer.AdagradOptimizer', ('document', 'a1d4f0682cde43ad34432b1338aadf04'))
+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', '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.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.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.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.AdamOptimizer ('paddle.fluid.optimizer.AdamOptimizer', ('document', '6fe871b955cab6e267422d5af666dafa'))
+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', '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.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.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.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.AdamaxOptimizer ('paddle.fluid.optimizer.AdamaxOptimizer', ('document', '883fc4541214e8343d3a89711936e15d'))
+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', '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.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.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.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.__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_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.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.DecayedAdagradOptimizer ('paddle.fluid.optimizer.DecayedAdagradOptimizer', ('document', 'e76838a8586bf2e58e6b5cdd2f67f780'))
+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', '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.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.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.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.__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_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.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.__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_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.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.__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_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.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.ModelAverage ('paddle.fluid.optimizer.ModelAverage', ('document', '0a0adcd60230630e21fe1ef46362dbc0'))
+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', '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.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_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.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.restore (ArgSpec(args=['self', 'executor'], varargs=None, keywords=None, defaults=None), ('document', '5f14ea4adda2791e1c3b37ff327f6a83'))
+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', '7917cbe4d3ed7954ae73360fbccc39f6'))
 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.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.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.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.__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_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.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.__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_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.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.__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'))

python/paddle/fluid/optimizer.py

@@ -449,23 +449,28 @@ class Optimizer(object):
                  no_grad_set=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.
 
         Args:
-            loss (Variable): loss variable to run optimizations.
-            startup_program (Program): startup_program for initializing parameters
-                in `parameter_list`.
-            parameter_list (list): list of Variables to update.
-            no_grad_set (set|None): set of Variables should be ignored.
-            callbacks (list|None): list of callables to run when appending backward
-                operator for one parameter.
+            loss (Variable): ``loss`` variable to run optimizations.
+            startup_program (Program, optional): :ref:`api_fluid_Program` for
+                initializing parameters in ``parameter_list``. The default value
+                is None, at this time :ref:`api_fluid_default_startup_program` will be used.
+            parameter_list (list, optional): List of ``Variable`` names to update
+                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.
+            callbacks (list, optional): list of callable objects to run when appending backward
+                operator for one parameter. The default value is None.
 
         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:
-            See examples in `apply_gradients`.
+            See examples in ``apply_gradients``.
         """
         no_grad_set = self._get_no_grad_set(loss, no_grad_set)
 
@@ -597,22 +602,30 @@ class Optimizer(object):
                  no_grad_set=None,
                  grad_clip=None):
         """
-        Add operations to minimize `loss` by updating `parameter_list`.
-
-        This method combines interface `backward()` and
-        `apply_gradients()` into one.
+        Add operations to minimize ``loss`` by updating ``parameter_list``.
 
         Args:
-            loss (Variable): loss variable to run optimizations.
-            startup_program (Program): startup_program for initializing parameters
-                in `parameter_list`.
-            parameter_list (list): list of Variables to update.
-            no_grad_set (set|None): set of Variables should be ignored.
-            grad_clip (GradClipBase|None) : Gradient clip strategy
+            loss (Variable): A ``Variable`` containing the value to minimize.
+            startup_program (Program, optional): :ref:`api_fluid_Program` for
+                initializing parameters in ``parameter_list``. The default value
+                is None, at this time :ref:`api_fluid_default_startup_program` will be used.
+            parameter_list (list, optional): List of ``Variable`` names to update
+                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:
-            tuple: (optimize_ops, params_grads) which are, list of operators appended;
-            and list of (param, grad) Variables pair for optimization.
+            tuple: tuple (optimize_ops, params_grads), A list of operators appended
+            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."
         params_grads = self.backward(
@@ -1173,9 +1186,10 @@ class LarsMomentumOptimizer(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::
 
@@ -1183,32 +1197,38 @@ class AdagradOptimizer(Optimizer):
 
         param\_out &= param - \\frac{learning\_rate * grad}{\sqrt{moment\_out} + \epsilon}
 
-    The original paper(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
-    does not have the epsilon attribute. It is added here in our implementation
-    as also proposed here: http://cs231n.github.io/neural-networks-3/#ada
+    Related paper: `Adaptive Subgradient Methods for Online Learning and
+    Stochastic Optimization <http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf>`_.
+
+    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.
 
     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.
-        epsilon (float): a small float value for numerical stability.
-        regularization: A Regularizer, such as
-                        fluid.regularizer.L2DecayRegularizer.
-        name: A optional name prefix.
-        initial_accumulator_value (float): Initial value for moment accumulator.
+        learning_rate (float|Variable): The learning rate used to update ``Parameter``.
+            It can be a float value or a ``Variable`` with a float type.
+        epsilon (float, optional): A small float value for numerical stability.
+            The default value is 1e-06.
+        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.
+        initial_accumulator_value (float, optional): Initial value for moment accumulator.
+            The default value is 0.0.
 
     Examples:
         .. code-block:: python
 
-            import paddle.fluid as fluid
             import numpy as np
+            import paddle.fluid as fluid
 
             np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
-            inp = fluid.layers.data(
-                name="inp", shape=[2, 2], append_batch_size=False)
+            inp = fluid.data(name="inp", shape=[2, 2])
             out = fluid.layers.fc(inp, size=3)
             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)
 
             exe = fluid.Executor(fluid.CPUPlace())
@@ -1276,12 +1296,12 @@ class AdagradOptimizer(Optimizer):
 
 class AdamOptimizer(Optimizer):
     """
-    This implements the Adam optimizer from Section 2 of the Adam
-    paper : https://arxiv.org/abs/1412.6980.
-    Adam is a first-order gradient-based optimization method based on
-    adaptive estimates of lower-order moments.
-
-    Adam updates:
+    The Adam optimzier uses an optimization described at the end
+    of section 2 of `Adam paper <https://arxiv.org/abs/1412.6980>`_ ,
+    it can dynamically adjusts the learning rate of each parameter using
+    the 1st moment estimates and the 2nd moment estimates of the gradient.
+    
+    The parameter ``param_out`` update rule with gradient ``grad``:
 
     .. math::
 
@@ -1296,20 +1316,29 @@ class AdamOptimizer(Optimizer):
 
         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:
-        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.
-        lazy_mode(bool: false): 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 is the 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.
+        learning_rate (float|Variable, optional): The learning rate used to update ``Parameter``.
+            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.
+        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:
         .. code-block:: python
@@ -1320,8 +1349,8 @@ class AdamOptimizer(Optimizer):
             place = fluid.CPUPlace()
             main = fluid.Program()
             with fluid.program_guard(main):
-                x = fluid.layers.data(name='x', shape=[13], dtype='float32')
-                y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+                x = fluid.data(name='x', shape=[None, 13], dtype='float32')
+                y = fluid.data(name='y', shape=[None, 1], dtype='float32')
                 y_predict = fluid.layers.fc(input=x, size=1, act=None)
                 cost = fluid.layers.square_error_cost(input=y_predict, label=y)
                 avg_cost = fluid.layers.mean(cost)
@@ -1457,11 +1486,12 @@ class AdamOptimizer(Optimizer):
 
 class AdamaxOptimizer(Optimizer):
     """
-    We implement the Adamax optimizer from Section 7 of the Adam
-    paper: https://arxiv.org/abs/1412.6980. Adamax is a variant of the
-    Adam algorithm based on the infinity norm.
+    The Adamax optimizer is implemented based on the Adamax Optimization 
+    in Section 7 of `Adam paper <https://arxiv.org/abs/1412.6980>`_.
+    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::
 
@@ -1475,10 +1505,28 @@ class AdamaxOptimizer(Optimizer):
 
         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.
-    However, it is added here for numerical stability to prevent the
-    division by 0 error.
+    Args:
+        learning_rate (float|Variable, optional): The learning rate used to update ``Parameter``.
+            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:
         .. code-block:: python
@@ -1493,10 +1541,10 @@ class AdamaxOptimizer(Optimizer):
           train_program = fluid.Program()
           startup_program = fluid.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)
               loss = fluid.layers.mean(hidden)
-              adam = fluid.optimizer.Adamax(learning_rate=0.2)
+              adam = fluid.optimizer.AdamaxOptimizer(learning_rate=0.2)
               adam.minimize(loss)
 
           # Run the startup program once and only once.
@@ -1506,19 +1554,6 @@ class AdamaxOptimizer(Optimizer):
           outs = exe.run(program=train_program,
                         feed={'X': x},
                          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"
     _inf_norm_acc_str = "inf_norm"
@@ -1690,11 +1725,11 @@ class DpsgdOptimizer(Optimizer):
 
 class DecayedAdagradOptimizer(Optimizer):
     """
-    **Decayed Adagrad Optimizer**
-
-    The original paper(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
+    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
+    during model training when using the AdagradOptimizer.
 
-    The update is done as follows:
+    The parameter ``param_out`` update rule with gradient ``grad``:
 
     .. math::
 
@@ -1702,34 +1737,37 @@ class DecayedAdagradOptimizer(Optimizer):
 
         param\_out & = param - \\frac{learning\_rate * grad}{\sqrt{moment\_out} + \epsilon}
 
-    The original paper(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
-    does not have an epsilon attribute. It is added here for numerical
+    Related paper: `Adaptive Subgradient Methods for Online Learning and Stochastic
+    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.
 
     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.
-        decay (float): decay rate.
-        epsilon (float): a small float value for numerical stability.
-        regularization: A Regularizer, such as
-                        fluid.regularizer.L2DecayRegularizer.
-        name: A optional name prefix.
+        learning_rate (float|Variable): The learning rate used to update ``Parameter``.
+            It can be a float value or a ``Variable`` with a float type.
+        decay (float, optional): The decay rate. The default value is 0.95.
+        epsilon (float, optional): A small float value for numerical stability.
+            The default value is 1e-06.
+        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, DecayedAdagradOptimizer doesn't support sparse parameter optimization.**
 
     Examples:
         .. code-block:: python
 
             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' )
-            trans = layers.fc( x, 100 )
-            cost = layers.reduce_mean( trans )
-            optimizer = fluid.optimizer.DecayedAdagrad(learning_rate=0.2)
+            x = fluid.data( name='x', shape=[None, 10], dtype='float32' )
+            trans = fluid.layers.fc( x, 100 )
+            cost = fluid.layers.reduce_mean( trans )
+            optimizer = fluid.optimizer.DecayedAdagradOptimizer(learning_rate=0.2)
             optimizer.minimize(cost)
-
-    Notes:
-       Currently, DecayedAdagradOptimizer doesn't support sparse parameter optimization.
     """
     _moment_acc_str = "moment"
 
@@ -2359,21 +2397,45 @@ Lamb = LambOptimizer
 
 
 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
-    parameter variables of current model by calling 'apply()' method. And the
-    'restore()' method is used to restore the parameter values of current model.
+    """
+    The ModelAverage optimizer accumulates specific continuous historical parameters
+    during training. The accumulated historical range can be controlled by the passed
+    ``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,
-    min_average_window, max_average_window and current update times.
+    When the cumulative times (num_accumulates) is greater than the specific window
+    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:
-        average_window_rate: The rate of average window.
-        min_average_window: The minimum size of average window.
-        max_average_window: The maximum size of average window.
-        regularization: A Regularizer, such as
-                        fluid.regularizer.L2DecayRegularizer.
-        name: A optional name prefix.
+        average_window_rate (float): The calculate ratio of the window length relative to ``Parameter`` update times.
+        min_average_window (int, optional): the minimum size of average window length. The default value is 10000.
+        max_average_window (int, optional): The maximum size of average window length. The default value is 10000.
+        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.
 
     Examples:
 
@@ -2390,7 +2452,7 @@ class ModelAverage(Optimizer):
         startup_program = fluid.Program()
         with fluid.program_guard(train_program, startup_program):
             # 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)
             loss = fluid.layers.mean(hidden)
             optimizer = fluid.optimizer.Momentum(learning_rate=0.2, momentum=0.1)
@@ -2399,13 +2461,14 @@ class ModelAverage(Optimizer):
             # build ModelAverage optimizer
             model_average = fluid.optimizer.ModelAverage(0.15,
                                                          min_average_window=10000,
-                                                         max_average_window=20000)
+                                                         max_average_window=12500)
 
             exe.run(startup_program)
-            x = numpy.random.random(size=(10, 1)).astype('float32')
-            outs = exe.run(program=train_program,
-                           feed={'X': x},
-                           fetch_list=[loss.name])
+            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):
@@ -2526,11 +2589,54 @@ class ModelAverage(Optimizer):
 
     @signature_safe_contextmanager
     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:
-            executor(fluid.Executor): current executor.
-            need_restore(bool): If you finally need to do restore, set it to True. Default is True.
+            executor(fluid.Executor): The current network executor.
+            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)
         try:
@@ -2540,10 +2646,54 @@ class ModelAverage(Optimizer):
                 self.restore(executor)
 
     def restore(self, executor):
-        """Restore parameter values of current model.
+        """
+        Restore ``Parameter`` values of current model.
         
         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)