configuration change

hyunwoongko · Dec 18, 2019 · aecd5e9 · aecd5e9
1 parent c1c22a3
commit aecd5e9
Show file tree

Hide file tree

Showing 6 changed files with 17 additions and 13 deletions.
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
diff --git a/__pycache__/conf.cpython-36.pyc b/__pycache__/conf.cpython-36.pyc
diff --git a/conf.py b/conf.py
@@ -20,9 +20,9 @@
 # optimizer parameter setting
 init_lr = 1e-4
 factor = 0.8
-patience = 5
-warmup = 3
-weight_decay = 5e-3
+patience = 10
+warmup = 5
+weight_decay = 5e-4
 epoch = 2000
 clip = 1
 inf = float('inf')
diff --git a/result/test.txt b/result/test.txt
@@ -1 +1 @@
-[5.091137647628784, 5.0348674058914185, 5.0215981006622314, 5.0442681312561035, 5.031394064426422, 4.974141061306, 4.9869683384895325, 5.003897190093994, 5.022670149803162, 5.014573931694031, 5.0173457860946655, 5.000527858734131]
+[4.984338045120239, 4.875711560249329, 4.780858218669891, 4.849339187145233, 4.910612761974335, 4.755706012248993, 4.562326073646545, 4.756882011890411,
diff --git a/result/train.txt b/result/train.txt
@@ -1 +1 @@
-[5.800987136521528, 5.191104743974324, 5.152123199160403, 5.133547501416984, 5.136521278498981, 5.121117488928303, 5.096929487152772, 5.088037919367988, 5.087618151425265, 5.086602942008804, 5.09127295279818, 5.10448893786527]
+[5.617368328413774, 5.063993916112421, 4.893223413811906, 4.7998555536312155, 4.7442694508556755, 4.707525814157226, 4.618371282905209, 4.527510960196609,
diff --git a/train.py b/train.py
@@ -10,7 +10,6 @@
 from torch.optim import Adam
 
 from data import *
-from graph import draw
 from models.model.transformer import Transformer
 from util.epoch_timer import epoch_time
 
@@ -39,9 +38,11 @@ def initialize_weights(m):
 
 print(f'The model has {count_parameters(model):,} trainable parameters')
 model.apply(initialize_weights)
-optimizer = Adam(model.parameters(), lr=init_lr, weight_decay=weight_decay)
+optimizer = Adam(params=model.parameters(),
+ lr=init_lr,
+ weight_decay=weight_decay)
 
-scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
+scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer=optimizer,
  verbose=True,
  factor=factor,
  patience=patience)