update doc

docs/user/config.rst

@@ -38,26 +38,61 @@ The default ``.emmental-config.yaml`` configuration file is shown below::
         train_split: train # the split for training, accepts str or list of strs
         valid_split: valid # the split for validation, accepts str or list of strs
         test_split: test # the split for testing, accepts str or list of strs
-        ignore_index: 0 # the ignore index, uses for masking samples
+        ignore_index: # the ignore index, uses for masking samples
         optimizer_config:
             optimizer: adam # [sgd, adam, adamax, bert_adam]
             lr: 0.001 # Learing rate
             l2: 0.0 # l2 regularization
-            grad_clip: 1.0 # gradient clipping
-            sgd_config:
-                momentum: 0.9
+            grad_clip: # gradient clipping
+            asgd_config:
+                lambd: 0.0001
+                alpha: 0.75
+                t0: 1000000.0
+            adadelta_config:
+                rho: 0.9
+                eps: 0.000001
+            adagrad_config:
+                lr_decay: 0
+                initial_accumulator_value: 0
+                eps: 0.0000000001
             adam_config:
                 betas: !!python/tuple [0.9, 0.999]
                 eps: 0.00000001
                 amsgrad: False
+            adamw_config:
+                betas: !!python/tuple [0.9, 0.999]
+                eps: 0.00000001
+                amsgrad: False
             adamax_config:
                 betas: !!python/tuple [0.9, 0.999]
                 eps: 0.00000001
+            lbfgs_config:
+                max_iter: 20
+                max_eval:
+                tolerance_grad: 0.0000001
+                tolerance_change: 0.000000001
+                history_size: 100
+                line_search_fn:
+            rms_prop_config:
+                alpha: 0.99
+                eps: 0.00000001
+                momentum: 0
+                centered: False
+            r_prop_config:
+                etas: !!python/tuple [0.5, 1.2]
+                step_sizes: !!python/tuple [0.000001, 50]
+            sgd_config:
+                momentum: 0
+                dampening: 0
+                nesterov: False
+            sparse_adam_config:
+                betas: !!python/tuple [0.9, 0.999]
+                eps: 0.00000001
             bert_adam_config:
                 betas: !!python/tuple [0.9, 0.999]
                 eps: 0.00000001
         lr_scheduler_config:
-            lr_scheduler: # [linear, exponential, reduce_on_plateau]
+            lr_scheduler: # [linear, exponential, reduce_on_plateau, cosine_annealing]
             warmup_steps: # warm up steps
             warmup_unit: batch # [epoch, batch]
             warmup_percentage: # warm up percentage
@@ -79,6 +114,8 @@ The default ``.emmental-config.yaml`` configuration file is shown below::
                     - 1000
                 gamma: 0.1
                 last_epoch: -1
+            cosine_annealing_config:
+                last_epoch: -1
         task_scheduler_config:
             task_scheduler: round_robin # [sequential, round_robin, mixed]
             sequential_scheduler_config:
@@ -92,7 +129,7 @@ The default ``.emmental-config.yaml`` configuration file is shown below::
     # Logging configuration
     logging_config:
         counter_unit: epoch # [epoch, batch]
-        evaluation_freq: 2
+        evaluation_freq: 1
         writer_config:
             writer: tensorboard # [json, tensorboard]
             verbose: True

docs/user/learning.rst

@@ -28,26 +28,61 @@ The learning parameters of the model are described below::
         train_split: train # the split for training, accepts str or list of strs
         valid_split: valid # the split for validation, accepts str or list of strs
         test_split: test # the split for testing, accepts str or list of strs
-        ignore_index: 0 # the ignore index, uses for masking samples
+        ignore_index: # the ignore index, uses for masking samples
         optimizer_config:
             optimizer: adam # [sgd, adam, adamax, bert_adam]
             lr: 0.001 # Learing rate
             l2: 0.0 # l2 regularization
-            grad_clip: 1.0 # gradient clipping
-            sgd_config:
-                momentum: 0.9
+            grad_clip: # gradient clipping
+            asgd_config:
+                lambd: 0.0001
+                alpha: 0.75
+                t0: 1000000.0
+            adadelta_config:
+                rho: 0.9
+                eps: 0.000001
+            adagrad_config:
+                lr_decay: 0
+                initial_accumulator_value: 0
+                eps: 0.0000000001
             adam_config:
                 betas: !!python/tuple [0.9, 0.999]
                 eps: 0.00000001
                 amsgrad: False
+            adamw_config:
+                betas: !!python/tuple [0.9, 0.999]
+                eps: 0.00000001
+                amsgrad: False
             adamax_config:
                 betas: !!python/tuple [0.9, 0.999]
                 eps: 0.00000001
+            lbfgs_config:
+                max_iter: 20
+                max_eval:
+                tolerance_grad: 0.0000001
+                tolerance_change: 0.000000001
+                history_size: 100
+                line_search_fn:
+            rms_prop_config:
+                alpha: 0.99
+                eps: 0.00000001
+                momentum: 0
+                centered: False
+            r_prop_config:
+                etas: !!python/tuple [0.5, 1.2]
+                step_sizes: !!python/tuple [0.000001, 50]
+            sgd_config:
+                momentum: 0
+                dampening: 0
+                nesterov: False
+            sparse_adam_config:
+                betas: !!python/tuple [0.9, 0.999]
+                eps: 0.00000001
             bert_adam_config:
                 betas: !!python/tuple [0.9, 0.999]
                 eps: 0.00000001
         lr_scheduler_config:
-            lr_scheduler: # [linear, exponential, reduce_on_plateau]
+            lr_scheduler: # [linear, exponential, reduce_on_plateau, cosine_annealing]
             warmup_steps: # warm up steps
             warmup_unit: batch # [epoch, batch]
             warmup_percentage: # warm up percentage
@@ -69,6 +104,8 @@ The learning parameters of the model are described below::
                     - 1000
                 gamma: 0.1
                 last_epoch: -1
+            cosine_annealing_config:
+                last_epoch: -1
         task_scheduler_config:
             task_scheduler: round_robin # [sequential, round_robin, mixed]
             sequential_scheduler_config:

docs/user/logging.rst

@@ -25,7 +25,7 @@ The logging parameters of Emmental are described below::
     # Logging configuration
     logging_config:
         counter_unit: epoch # [epoch, batch]
-        evaluation_freq: 2
+        evaluation_freq: 1
         writer_config:
             writer: tensorboard # [json, tensorboard]
             verbose: True