remove lr schedulers

conf.py

@@ -18,10 +18,8 @@
 drop_prob = 0.1
 
 # optimizer parameter setting
-warmup = 5
-factor = 0.8
 init_lr = 1e-4
 weight_decay = 5e-4
-epoch = 100
+epoch = 300
 clip = 1
 inf = float('inf')

graph.py

@@ -0,0 +1,32 @@
+"""
+@author : Hyunwoong
+@when : 2019-12-18
+@homepage : https://github.com/gusdnd852
+"""
+
+import matplotlib.pyplot as plt
+import re
+
+
+def read(name):
+ f = open(name, 'r')
+ file = f.read()
+ file = re.sub('\\[', '', file)
+ file = re.sub('\\]', '', file)
+ f.close()
+
+ return [float(i) * 100.0 for i in file.split(',')]
+
+
+train = read('./result/train.txt')
+test = read('./result/test.txt')
+
+plt.plot(train, 'r', label='train')
+plt.plot(test, 'b', label='validation')
+
+plt.xlabel('epoch')
+plt.ylabel('loss')
+plt.title('training result')
+plt.grid(True, which='both', axis='both')
+plt.legend(loc='lower right')
+plt.show()

result/test.txt

@@ -0,0 +1 @@
+[5.107369244098663]

result/train.txt

@@ -0,0 +1 @@
+[5.809054460819597]

train.py

@@ -41,7 +41,6 @@ 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 = LRScheduler(d_model=d_model, factor=factor, warmup=warmup, optimizer=optimizer)
 criterion = nn.CrossEntropyLoss(ignore_index=src_pad_idx)