diff --git a/mindspore/nn/layer/conv.py b/mindspore/nn/layer/conv.py
index fbf6ad2a0c..730b5e3398 100644
--- a/mindspore/nn/layer/conv.py
+++ b/mindspore/nn/layer/conv.py
@@ -37,7 +37,8 @@ class _Conv(Cell):
                  group,
                  has_bias,
                  weight_init,
-                 bias_init):
+                 bias_init,
+                 transposed=False):
         super(_Conv, self).__init__()
         self.in_channels = check_int_positive(in_channels)
         self.out_channels = check_int_positive(out_channels)
@@ -65,9 +66,11 @@ class _Conv(Cell):
         if out_channels % group != 0:
             raise ValueError("Attr 'out_channels' of 'Conv2D' Op must be divisible by "
                              "attr 'group' of 'Conv2D' Op.")
-
-        self.weight = Parameter(initializer(weight_init, [out_channels, in_channels // group, *kernel_size]),
-                                name='weight')
+        if transposed:
+            shape = [in_channels, out_channels // group, *kernel_size]
+        else:
+            shape = [out_channels, in_channels // group, *kernel_size]
+        self.weight = Parameter(initializer(weight_init, shape), name='weight')
 
         if check_bool(has_bias):
             self.bias = Parameter(initializer(bias_init, [out_channels]), name='bias')
@@ -312,8 +315,8 @@ class Conv2dTranspose(_Conv):
         # cause Conv2DBackpropInput's out_channel refers to Conv2D's out_channel,
         # then Conv2dTranspose's out_channel refers to Conv2DBackpropInput's in_channel.
         super(Conv2dTranspose, self).__init__(
-            out_channels,
             in_channels,
+            out_channels,
             kernel_size,
             stride,
             pad_mode,
@@ -322,10 +325,11 @@ class Conv2dTranspose(_Conv):
             group,
             has_bias,
             weight_init,
-            bias_init)
+            bias_init,
+            transposed=True)
 
-        self.out_channels = out_channels
         self.in_channels = in_channels
+        self.out_channels = out_channels
         self.shape = P.Shape()
         if pad_mode not in ('valid', 'same', 'pad'):
             raise ValueError('Attr \'pad_mode\' of \'Conv2dTranspose\' Op passed '
diff --git a/tests/ut/python/nn/test_conv.py b/tests/ut/python/nn/test_conv.py
index 821c224a75..a76c789a79 100644
--- a/tests/ut/python/nn/test_conv.py
+++ b/tests/ut/python/nn/test_conv.py
@@ -20,7 +20,6 @@ import mindspore.nn as nn
 from mindspore import Tensor
 from ..ut_filter import non_graph_engine
 
-
 weight = Tensor(np.ones([2, 2]))
 in_channels = 3
 out_channels = 64
@@ -28,6 +27,7 @@ out_channels = 64
 
 class Net(nn.Cell):
     """ Net definition """
+
     def __init__(self,
                  cin,
                  cout,
@@ -93,12 +93,14 @@ def test_compile_pad_pad():
     input_data = Tensor(np.ones([1, 3, 16, 50], dtype=np.float32))
     net(input_data)
 
+
 def test_conv_group_error():
     with pytest.raises(ValueError):
         nn.Conv2d(6, 8, 3, group=3)
     with pytest.raises(ValueError):
         nn.Conv2d(6, 9, 3, group=2)
 
+
 def test_conv_check():
     """ test_conv_check """
     with pytest.raises(ValueError):
@@ -139,15 +141,15 @@ class NetConv2dTranspose(nn.Cell):
         super(NetConv2dTranspose, self).__init__()
         self.conv = nn.Conv2dTranspose(cin,
                                        cout,
-                              kernel_size,
-                              stride,
-                              pad_mode,
-                              padding,
-                              dilation,
-                              group,
-                              has_bias,
-                              weight_init,
-                              bias_init)
+                                       kernel_size,
+                                       stride,
+                                       pad_mode,
+                                       padding,
+                                       dilation,
+                                       group,
+                                       has_bias,
+                                       weight_init,
+                                       bias_init)
 
     def construct(self, input_x):
         return self.conv(input_x)
@@ -165,6 +167,13 @@ def test_compile_transpose_bias():
     net(input_data)
 
 
+def test_compile_transpose_bias_init():
+    bias = Tensor(np.random.randn(64).astype(np.float32))
+    net = NetConv2dTranspose(3, 64, 4, has_bias=True, weight_init='normal', bias_init=bias)
+    input_data = Tensor(np.ones([1, 3, 16, 50], dtype=np.float32))
+    net(input_data)
+
+
 def test_compile_transpose_valid():
     net = NetConv2dTranspose(3, 64, 4, pad_mode='valid', weight_init='normal')
     input_data = Tensor(np.ones([1, 3, 16, 50], dtype=np.float32))