diff --git a/unsuper/Readme.md b/unsuper/Readme.md
new file mode 100644
index 0000000..3b5ef4e
--- /dev/null
+++ b/unsuper/Readme.md
@@ -0,0 +1,5 @@
+1. 3x3的时候会有重复
+  1. 重复的权重，虽然权重看起来都一样，但是有稍微的不同，不是完全一样
+  2. 3x3太小了导致了样本的信噪比太低，大部分的样本切出来都是0
+2. 5x5的时候会有网格状重复
+3. 7x7的时候边框区域问题
\ No newline at end of file
diff --git a/unsuper/minist.py b/unsuper/minist.py
index 73818a3..77b750c 100644
--- a/unsuper/minist.py
+++ b/unsuper/minist.py
@@ -30,7 +30,7 @@ test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, s
 class ConvNet(nn.Module):
     def __init__(self):
         super(ConvNet, self).__init__()
-        self.conv1 = nn.Conv2d(1, 8, 5, 1, 0)
+        self.conv1 = nn.Conv2d(1, 8, 3, 1, 0)
         self.pool = nn.MaxPool2d(2, 2)
         self.conv2 = nn.Conv2d(8, 1, 5, 1, 0)
         self.fc1 = nn.Linear(1 * 4 * 4, 10)
@@ -43,7 +43,7 @@ class ConvNet(nn.Module):
         x = self.fc1(x)
         return x
 
-    def forward_unsuper(self, x):
+    def normal_conv1_weight(self):
         weight = self.conv1.weight.reshape(self.conv1.weight.shape[0], -1)
         weight = weight.permute(1, 0)
         mean = torch.mean(weight, dim=0)
@@ -52,31 +52,31 @@ class ConvNet(nn.Module):
         weight = weight / sum
         weight = weight.permute(1, 0)
         weight = weight.reshape(self.conv1.weight.shape)
-        x = torch.conv2d(x, weight)
+        return weight
+
+    def forward_unsuper(self, x):
+        x = torch.conv2d(x, self.normal_conv1_weight(), stride=1)
         return x
 
     def printFector(self, x, label, dir=""):
         show.DumpTensorToImage(x.view(-1, x.shape[2], x.shape[3]), dir + "/input_image.png", Contrast=[0, 1.0])
         # show.DumpTensorToLog(x, "input_image.log")
-        x = self.conv1(x)
-        w = self.conv1.weight
-        show.DumpTensorToImage(w.view(-1, w.shape[2], w.shape[3]), dir + "/conv1_weight.png", Contrast=[-1.0, 1.0])
+
+        w = self.normal_conv1_weight()
+        x = torch.conv2d(x, w)
+        show.DumpTensorToImage(w.view(-1, w.shape[2], w.shape[3]), dir + "/conv1_weight.png")
         # show.DumpTensorToLog(w, "conv1_weight.log")
 
-        show.DumpTensorToImage(x.view(-1, x.shape[2], x.shape[3]), dir + "/conv1_output.png", Contrast=[-1.0, 1.0])
+        show.DumpTensorToImage(x.view(-1, x.shape[2], x.shape[3]), dir + "/conv1_output.png")
         # show.DumpTensorToLog(x, "conv1_output.png")
 
         x = self.pool(x)
         x = self.conv2(x)
         w = self.conv2.weight
-        show.DumpTensorToImage(
-            w.view(-1, w.shape[2], w.shape[3]).cpu(), dir + "/conv2_weight.png", Contrast=[-1.0, 1.0]
-        )
-        show.DumpTensorToImage(
-            x.view(-1, x.shape[2], x.shape[3]).cpu(), dir + "/conv2_output.png", Contrast=[-1.0, 1.0]
-        )
+        show.DumpTensorToImage(w.view(-1, w.shape[2], w.shape[3]).cpu(), dir + "/conv2_weight.png")
+        show.DumpTensorToImage(x.view(-1, x.shape[2], x.shape[3]).cpu(), dir + "/conv2_output.png")
         x = self.pool(x)
-        show.DumpTensorToImage(x.view(-1, x.shape[2], x.shape[3]).cpu(), dir + "/pool_output.png", Contrast=[-1.0, 1.0])
+        show.DumpTensorToImage(x.view(-1, x.shape[2], x.shape[3]).cpu(), dir + "/pool_output.png")
         pool_shape = x.shape
         x = x.view(x.shape[0], -1)
         x = self.fc1(x)
@@ -105,14 +105,27 @@ model = ConvNet().to(device)
 model.train()
 
 # Train the model unsuper
-epochs = 20
-model.conv1.weight.requires_grad = True
-model.conv2.weight.requires_grad = False
-model.fc1.weight.requires_grad = False
+epochs = 1
 n_total_steps = len(train_loader)
 for epoch in range(epochs):
     for i, (images, labels) in enumerate(train_loader):
         images = images.to(device)
+
+        # # images = images[:,:,12:15,12:15]
+        # kernel_size = 3
+        # mean_filter = torch.ones((1, 1, kernel_size, kernel_size), device=device) / (kernel_size * kernel_size)
+        # images = F.conv2d(images, mean_filter, padding=1)
+        # images = F.conv2d(images, mean_filter, padding=1)
+        # images = F.conv2d(images, mean_filter, padding=1)
+        # # images = F.conv2d(images, mean_filter, padding=1)
+        # # images = F.conv2d(images, mean_filter, padding=1)
+
+        # images = torch.rand(3, 3).to(device=device)
+        # # images[1, 1] = images[1, 1] * 2
+        # # images[0, 0] = images[1, 1] * 2
+        # # images[2, 2] = images[1, 1] * 2
+        # images = images.view(1, 1, 3, 3)
+
         outputs = model.forward_unsuper(images)
 
         outputs = outputs.permute(0, 2, 3, 1)  # 64 8 24 24 -> 64 24 24 8
@@ -134,15 +147,16 @@ for epoch in range(epochs):
         model.conv1.weight.grad = None
         loss.backward()
 
-        model.conv1.weight.data = model.conv1.weight.data - model.conv1.weight.grad * 10
+        model.conv1.weight.data = model.conv1.weight.data - model.conv1.weight.grad * 100
 
         if (i + 1) % 100 == 0:
             print(f"Epoch [{epoch+1}/{epochs}], Step [{i+1}/{n_total_steps}], Loss: {loss.item():.8f}")
 
-w = model.conv1.weight.grad
-show.DumpTensorToImage(w.view(-1, w.shape[2], w.shape[3]).cpu(), "conv1_weight_grad.png")
+show.DumpTensorToImage(images.view(-1, images.shape[2], images.shape[3]), "input_image.png", Contrast=[0, 1.0])
+g = model.conv1.weight.grad
+show.DumpTensorToImage(g.view(-1, g.shape[2], g.shape[3]).cpu(), "conv1_weight_grad.png")
 w = model.conv1.weight.data
-show.DumpTensorToImage(w.view(-1, w.shape[2], w.shape[3]), "conv1_weight_update.png", Contrast=[-1.0, 1.0])
+show.DumpTensorToImage(w.view(-1, w.shape[2], w.shape[3]), "conv1_weight_update.png")
 
 # loader = torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=False)
 # images, labels = next(iter(loader))