diff --git a/TCN/tcn.py b/TCN/tcn.py
index 37dfd43..3a332c2 100644
--- a/TCN/tcn.py
+++ b/TCN/tcn.py
@@ -15,27 +15,25 @@ def forward(self, x):
 class TemporalBlock(nn.Module):
     def __init__(self, n_inputs, n_outputs, kernel_size, stride, dilation, padding, dropout=0.2):
         super(TemporalBlock, self).__init__()
-        self.conv1 = weight_norm(nn.Conv1d(n_inputs, n_outputs, kernel_size,
-                                           stride=stride, padding=padding, dilation=dilation))
-        self.chomp1 = Chomp1d(padding)
-        self.relu1 = nn.ReLU()
-        self.dropout1 = nn.Dropout(dropout)
-
-        self.conv2 = weight_norm(nn.Conv1d(n_outputs, n_outputs, kernel_size,
-                                           stride=stride, padding=padding, dilation=dilation))
-        self.chomp2 = Chomp1d(padding)
-        self.relu2 = nn.ReLU()
-        self.dropout2 = nn.Dropout(dropout)
-
-        self.net = nn.Sequential(self.conv1, self.chomp1, self.relu1, self.dropout1,
-                                 self.conv2, self.chomp2, self.relu2, self.dropout2)
+        conv1 = weight_norm(nn.Conv1d(n_inputs, n_outputs, kernel_size,
+                                      stride=stride, padding=padding, dilation=dilation))
+        chomp1 = Chomp1d(padding)
+        relu1 = nn.ReLU()
+        dropout1 = nn.Dropout(dropout)
+
+        conv2 = weight_norm(nn.Conv1d(n_outputs, n_outputs, kernel_size,
+                                      stride=stride, padding=padding, dilation=dilation))
+        chomp2 = Chomp1d(padding)
+        relu2 = nn.ReLU()
+        dropout2 = nn.Dropout(dropout)
+
+        self.net = nn.Sequential(conv1, chomp1, relu1, dropout1,
+                                 conv2, chomp2, relu2, dropout2)
         self.downsample = nn.Conv1d(n_inputs, n_outputs, 1) if n_inputs != n_outputs else None
         self.relu = nn.ReLU()
-        self.init_weights()
 
-    def init_weights(self):
-        self.conv1.weight.data.normal_(0, 0.01)
-        self.conv2.weight.data.normal_(0, 0.01)
+        conv1.weight.data.normal_(0, 0.01)
+        conv2.weight.data.normal_(0, 0.01)
         if self.downsample is not None:
             self.downsample.weight.data.normal_(0, 0.01)