diff --git a/infer.wav b/infer.wav
new file mode 100644
index 0000000..a836907
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diff --git a/infer2.wav b/infer2.wav
new file mode 100644
index 0000000..8657086
Binary files /dev/null and b/infer2.wav differ
diff --git a/net/__pycache__/model.cpython-37.pyc b/net/__pycache__/model.cpython-37.pyc
index 60d14b6..9cebb84 100644
Binary files a/net/__pycache__/model.cpython-37.pyc and b/net/__pycache__/model.cpython-37.pyc differ
diff --git a/net/model.py b/net/model.py
index 46e20df..9b3759e 100644
--- a/net/model.py
+++ b/net/model.py
@@ -32,43 +32,43 @@ def __init__(self):
             nn.Conv1d(128, 128, kernel_size=3, stride=1, padding=1),
             nn.BatchNorm1d(128),
             nn.ReLU(),
-            nn.AvgPool1d(3))
+            nn.MaxPool1d(3,stride=3))
         self.conv3 = nn.Sequential(
             nn.Conv1d(128, 128, kernel_size=3, stride=1, padding=1),
             nn.BatchNorm1d(128),
             nn.ReLU(),
-            nn.AvgPool1d(3))
+            nn.MaxPool1d(3,stride=3))
         self.conv4 = nn.Sequential(
             nn.Conv1d(128, 256, kernel_size=3, stride=1, padding=1),
             nn.BatchNorm1d(256),
             nn.ReLU(),
-            nn.AvgPool1d(3))
+            nn.MaxPool1d(3,stride=3))
         self.conv5 = nn.Sequential(
             nn.Conv1d(256, 256, kernel_size=3, stride=1, padding=1),
             nn.BatchNorm1d(256),
             nn.ReLU(),
-            nn.AvgPool1d(3))  
+            nn.MaxPool1d(3,stride=3))  
         self.conv6 = nn.Sequential(
-            nn.Conv1d(256, 512, kernel_size=3, stride=1, padding=1),
-            nn.BatchNorm1d(512),
+            nn.Conv1d(256, 256, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm1d(256),
             nn.ReLU(),
-            nn.AvgPool1d(3))
+            nn.MaxPool1d(3,stride=3))
         self.conv7 = nn.Sequential(
-            nn.Conv1d(512, 512, kernel_size=3, stride=1, padding=1),
+            nn.Conv1d(256, 512, kernel_size=3, stride=1, padding=1),
             nn.BatchNorm1d(512),
             nn.ReLU(),
-            nn.AvgPool1d(3))
+            nn.MaxPool1d(3,stride=3))
         self.conv8 = nn.Sequential(
             nn.Conv1d(512, 512, kernel_size=3, stride=1, padding=1),
             nn.BatchNorm1d(512),
             nn.ReLU(),
-            nn.AvgPool1d(2),
             nn.Dropout(0.5))
-            
-        self.fc1 = nn.Linear(512, 256)
-        self.fc2 = nn.Linear(256, 4)
+
+        self.fc = nn.Linear(1024, 4)
         
-        self.LSTM = nn.LSTM(input_size = 4, hidden_size = 4, bidirectional=True)
+        #self.LSTM = nn.LSTM(input_size = 4, hidden_size = 2, bidirectional=True)
+        #self.fc3 = nn.Linear(hidden_size*2,output_size)
+        #self.output = F.softmax(linear(output),1)
 
     
     def forward(self, x, batch):
@@ -94,18 +94,20 @@ def forward(self, x, batch):
         out = out.reshape(batch,out.size(1)* out.size(2))
         #print(out.shape)
 
-        out = self.fc1(out)
+        out = self.fc(out)
         #print(out.shape)
-        out = self.fc2(out)
+        #out = self.fc2(out)
         #print(out.shape)
 
-        #out = out.reshape(1,batch,out.size(1))
-        #print(out.shape)
-        #out, hidden = self.LSTM(out)
-        #print(out.shape)
-        #out = out.squeeze()
+        # out = out.reshape(1,batch,out.size(1))
+        # print(out.shape)
+        # out, hidden = self.LSTM(out)
+        # print(out.shape)
+        # out = out.squeeze()
+        # print(out.shape)
 
         return out
+        #return F.softmax(out,1)
 '''
 class VoltexLSTM
 
diff --git a/train.py b/train.py
index af9072f..2aaffd7 100644
--- a/train.py
+++ b/train.py
@@ -20,7 +20,7 @@
 
 import os
 
-def infer(model, filename) :
+def infer(model, device, batch, filename, savename) :
 
     # Training End, infer #
     input = KshDataset.music_load(filename + '/nofx.ogg')    
@@ -66,12 +66,12 @@ def infer(model, filename) :
 
     song = mp.Audio(filename = (filename + "nofx.ogg"),  note_timestamp = note_time_Stamp_output, fx_timestamp = fx_time_Stamp_output)
     song.synthesize(diff='ka')
-    song.save(filename = "infer.wav")
+    song.save(filename = savename)
 
 def main():
     
     model = voltexNet()
-    #model.load_state_dict(torch.load("./model/model_bestAcc_.pth"))
+    model.load_state_dict(torch.load("./model_99_.pth"))
     #print ("load model")
     
     device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
@@ -81,7 +81,7 @@ def main():
     #input = torch.rand(128,3,80,15)
 
     criterion = nn.CrossEntropyLoss()
-    optimizer = optim.SGD(model.parameters(), lr=0.2, momentum=0.9)
+    optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
     scheduler = lr_scheduler.StepLR(optimizer, step_size=200, gamma=0.1)
 
     # kshDataset에서는 (곡의 길이 * 25 = batch) * (sample rate * 0.04)의 tensor가 넘어옴
@@ -97,12 +97,12 @@ def main():
     filenames = os.listdir(dirname)
     valnames = os.listdir(valname)
 
-    batch = 512
+    batch = 256
     song_index = 0
     best_Acc = 0
 
     epoch_loss = 0.0
-
+    '''
     for epoch in range(0,800) :
         epoch_loss = 0.0
         train_loss = 0.0
@@ -213,8 +213,9 @@ def main():
             break
         
         torch.save(model.state_dict(), "./model/model_"+ str(epoch)+ "_.pth")
-    
-    infer(mode, "./data_test/songs/badapple_nomico_alreco")
+    '''
+    infer(model, device, batch, "./data_test/songs/smooooch_kn/","infer.wav")
+    infer(model, device, batch, "./data_test/songs/dynasty_yooh/","infer2.wav")