diff --git a/FilterEvaluator/Evaluator.py b/FilterEvaluator/Evaluator.py
index a93c1c8..5f82c15 100644
--- a/FilterEvaluator/Evaluator.py
+++ b/FilterEvaluator/Evaluator.py
@@ -28,10 +28,8 @@ from tools import utils, Train, Loader, WebVisual
 import EvaluatorUnsuper
 
 
-
 batchsize = 128
 
-
 # model = utils.SetDevice(Model.Net5Grad35())
 # model = utils.SetDevice(Model.Net31535())
 model = utils.SetDevice(Model.Net3Grad335())
@@ -44,8 +42,6 @@ layer = 0
 
 
 
-
-
 # traindata, testdata = Loader.MNIST(batchsize)
 # traindata, testdata = Loader.RandomMnist(batchsize, style="Vertical")
 # traindata, testdata = Loader.RandomMnist(batchsize, style="Horizontal")
@@ -59,24 +55,27 @@ traindata, testdata = Loader.Cifar10Mono(batchsize, num_workers=0, shuffle=True)
 
 # weight = EvaluatorUnsuper.UnsuperLearnSearchWeight(model, layer, traindata, NumSearch=100000, SearchChannelRatio=8, Interation=10)
 # np.save("WeightSearch.npy", weight)
-
-# weight = EvaluatorUnsuper.UnsuperLearnTrainWeight(model, layer, traindata, NumTrain=5000)
-# np.save("WeightTrain.npy", weight)
-
-
-
-
 # weight = np.load(CurrentPath+"WeightSearch.npy")
 # bestweight,index = EvaluatorUnsuper.UnsuperLearnFindBestWeight(model,layer,weight,traindata,128,100000)
 # np.save(CurrentPath+"bestweightSearch.npy", bestweight)
+# bestweight = np.load(CurrentPath+"bestweightSearch.npy")
 # utils.NumpyToImage(bestweight, CurrentPath+"image")
+# EvaluatorUnsuper.SetModelConvWeight(model,layer,bestweight)
+# utils.SaveModel(model,CurrentPath+"/checkpointSearch.pkl")
 
 
-# weight = np.load(CurrentPath+"WeightTrain.npy")
-# bestweight, index = EvaluatorUnsuper.UnsuperLearnFindBestWeight(
-#     model, layer, weight, traindata, databatchs=16, interation=1000000)
-# np.save(CurrentPath+"bestweightTrain.npy", bestweight)
-# utils.NumpyToImage(bestweight, CurrentPath+"image")
+
+
+# weight = EvaluatorUnsuper.UnsuperLearnTrainWeight(model, layer, traindata, NumTrain=5000)
+# np.save("WeightTrain.npy", weight)
+weight = np.load(CurrentPath+"WeightTrain.npy")
+bestweight, index = EvaluatorUnsuper.UnsuperLearnFindBestWeight(model, layer, weight, traindata, databatchs=128, interation=1000000)
+np.save(CurrentPath+"bestweightTrain.npy", bestweight)
+bestweight = np.load(CurrentPath+"bestweightTrain.npy")
+utils.NumpyToImage(bestweight, CurrentPath+"image")
+# EvaluatorUnsuper.SetModelConvWeight(model,layer,bestweight)
+# utils.SaveModel(model,CurrentPath+"/checkpointTrain.pkl")
+
 
 
 
@@ -95,22 +94,6 @@ traindata, testdata = Loader.Cifar10Mono(batchsize, num_workers=0, shuffle=True)
 
 
 
-
-# weight = np.load(CurrentPath+"bestweightSearch.npy")
-# EvaluatorUnsuper.SetModelConvWeight(model,layer,weight)
-# utils.SaveModel(model,CurrentPath+"/checkpointSearch.pkl")
-
-
-weight = np.load(CurrentPath+"bestweightTrain.npy")
-EvaluatorUnsuper.SetModelConvWeight(model,layer,weight)
-utils.SaveModel(model,CurrentPath+"/checkpointTrain.pkl")
-
-
-
-
-
-
-
 # utils.NumpyToImage(weight, CurrentPath+"image")
 # utils.SaveModel(model,CurrentPath+"/checkpoint.pkl")
 print("save model sucess")
diff --git a/FilterEvaluator/image/0-1024.png b/FilterEvaluator/image/0-1024.png
index 73aede3..be95532 100644
Binary files a/FilterEvaluator/image/0-1024.png and b/FilterEvaluator/image/0-1024.png differ
diff --git a/FilterEvaluator/visdom.server.log b/FilterEvaluator/visdom.server.log
index 8457fc6..950625b 100644
Binary files a/FilterEvaluator/visdom.server.log and b/FilterEvaluator/visdom.server.log differ
diff --git a/tools/WebVisual.py b/tools/WebVisual.py
index 1e00b4b..79a6bcd 100644
--- a/tools/WebVisual.py
+++ b/tools/WebVisual.py
@@ -36,11 +36,8 @@ class Line():
         self.size = 0
         self.win = windows
 
-    def AppendData(self, y):
-        self.win.AppendData(self, y)
-
-    def AppendData(self, x, y):
-        self.win.AppendData(self, x, y)
+    def AppendData(self, y, x=None):
+        self.win.AppendData(self, y, x)
 
 
 class LineWin():
@@ -70,26 +67,16 @@ class LineWin():
         )
         return Line(linename, len(y))
 
-    def AppendData(self, line, y):
-        if not isinstance(y, list):
-            y = [y]
-        self.viz.line(
-            X=np.array(range(line.size, line.size+len(y))),
-            Y=np.array(y),
-            win=self.win,
-            update="append",
-            name=line.name
-        )
-        line.size = line.size + len(y)
-
-    def AppendData(self, line, x, y):
-        if not isinstance(y, list) and not isinstance(y, np.ndarray):
-            y = [y]
-        if not isinstance(x, list) and not isinstance(x, np.ndarray):
+    def AppendData(self, line, y, x=None):
+        y = [y]
+        Y = np.array(y).reshape((-1))
+        X = []
+        if x:
             x = [x]
-        X = np.array(x)
-        Y = np.array(y)
-        X = X[0:len(Y)]
+            X = np.array(X).reshape((-1))
+        else:
+            X = np.array(range(line.size, line.size+len(Y)))
+
         self.viz.line(
             Y=Y,
             X=X,
diff --git a/tools/utils.py b/tools/utils.py
index 88c62d8..de090e1 100755
--- a/tools/utils.py
+++ b/tools/utils.py
@@ -175,7 +175,7 @@ def NumpyToImage(numpydate, foldname, title="", maxImageWidth=128, maxImageHeigh
     data = numpydate.reshape((-1, numpydate.shape[-2], numpydate.shape[-1]))
     datashape = data.shape
 
-    newdata = np.ones((datashape[0],datashape[1]+1,datashape[2]+1))*numpydatemin
+    newdata = np.zeros((datashape[0],datashape[1]+1,datashape[2]+1))
     newdata[:, 0:datashape[1], 0:datashape[2]]=data
     datashape = newdata.shape