diff --git a/step/step_arch/graphwavenet/model.py b/step/step_arch/graphwavenet/model.py
index 4ca335c..f664d59 100644
--- a/step/step_arch/graphwavenet/model.py
+++ b/step/step_arch/graphwavenet/model.py
@@ -99,13 +99,13 @@ def __init__(self, num_nodes, support_len, dropout=0.3, gcn_bool=True, addaptadj
                 # dilated convolutions
                 self.filter_convs.append(nn.Conv2d(in_channels=residual_channels, out_channels=dilation_channels, kernel_size=(1,kernel_size),dilation=new_dilation))
 
-                self.gate_convs.append(nn.Conv1d(in_channels=residual_channels, out_channels=dilation_channels, kernel_size=(1, kernel_size), dilation=new_dilation))
+                self.gate_convs.append(nn.Conv2d(in_channels=residual_channels, out_channels=dilation_channels, kernel_size=(1, kernel_size), dilation=new_dilation))
 
                 # 1x1 convolution for residual connection
-                self.residual_convs.append(nn.Conv1d(in_channels=dilation_channels, out_channels=residual_channels, kernel_size=(1, 1)))
+                self.residual_convs.append(nn.Conv2d(in_channels=dilation_channels, out_channels=residual_channels, kernel_size=(1, 1)))
 
                 # 1x1 convolution for skip connection
-                self.skip_convs.append(nn.Conv1d(in_channels=dilation_channels, out_channels=skip_channels, kernel_size=(1, 1)))
+                self.skip_convs.append(nn.Conv2d(in_channels=dilation_channels, out_channels=skip_channels, kernel_size=(1, 1)))
                 self.bn.append(nn.BatchNorm2d(residual_channels))
                 new_dilation *= 2
                 receptive_field += additional_scope