diff --git a/tinygrad/nn/__init__.py b/tinygrad/nn/__init__.py
index edc23417ed..786fa52f64 100644
--- a/tinygrad/nn/__init__.py
+++ b/tinygrad/nn/__init__.py
@@ -43,7 +43,7 @@ class Conv2d:
   def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True):
     self.kernel_size = (kernel_size, kernel_size) if isinstance(kernel_size, int) else tuple(kernel_size)
     self.stride, self.padding, self.dilation, self.groups = stride, padding, dilation, groups
-    self.weight = Tensor.kaiming_uniform(out_channels, in_channels//groups, *self.kernel_size, a=math.sqrt(5))
+    self.weight = self.initialize_weight(out_channels, in_channels, groups)
     assert all_int(self.weight.shape), "does not support symbolic shape"
     bound = 1 / math.sqrt(prod(self.weight.shape[1:]))
     self.bias = Tensor.uniform(out_channels, low=-bound, high=bound) if bias else None
@@ -51,21 +51,21 @@ class Conv2d:
   def __call__(self, x):
     return x.conv2d(self.weight, self.bias, padding=self.padding, stride=self.stride, dilation=self.dilation, groups=self.groups)
 
+  def initialize_weight(self, out_channels, in_channels, groups): return Tensor.kaiming_uniform(out_channels, in_channels//groups, *self.kernel_size, a=math.sqrt(5))
+
 def ConvTranspose1d(in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, dilation=1, groups=1, bias=True):
   return ConvTranspose2d(in_channels, out_channels, (kernel_size,), stride, padding, output_padding, dilation, groups, bias)
 
-class ConvTranspose2d:
+class ConvTranspose2d(Conv2d):
   def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, dilation=1, groups=1, bias=True):
-    self.kernel_size = (kernel_size, kernel_size) if isinstance(kernel_size, int) else tuple(kernel_size)
-    self.stride, self.padding, self.output_padding, self.dilation, self.groups = stride, padding, output_padding, dilation, groups
-    self.weight = Tensor.kaiming_uniform(in_channels, out_channels//groups, *self.kernel_size, a=math.sqrt(5))
-    assert all_int(self.weight.shape), "does not support symbolic shape"
-    bound = 1 / math.sqrt(prod(self.weight.shape[1:]))
-    self.bias = Tensor.uniform(out_channels, low=-bound, high=bound) if bias else None
+    super().__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias)
+    self.output_padding = output_padding
 
   def __call__(self, x):
     return x.conv_transpose2d(self.weight, self.bias, padding=self.padding, output_padding=self.output_padding, stride=self.stride, dilation=self.dilation, groups=self.groups)
 
+  def initialize_weight(self, out_channels, in_channels, groups): return Tensor.kaiming_uniform(in_channels, out_channels//groups, *self.kernel_size, a=math.sqrt(5))
+
 class Linear:
   def __init__(self, in_features, out_features, bias=True):
     self.weight = Tensor.kaiming_uniform(out_features, in_features, a=math.sqrt(5))