diff --git a/mindspore/nn/layer/conv.py b/mindspore/nn/layer/conv.py
index 77c6ace75d..158d5cb93a 100644
--- a/mindspore/nn/layer/conv.py
+++ b/mindspore/nn/layer/conv.py
@@ -13,10 +13,13 @@
 # limitations under the License.
 # ============================================================================
 """conv"""
+import numpy as np
 from mindspore import log as logger
 from mindspore.ops import operations as P
+from mindspore.ops.primitive import constexpr
 from mindspore.common.parameter import Parameter
 from mindspore.common.initializer import initializer
+from mindspore.common.tensor import Tensor
 from mindspore._checkparam import ParamValidator as validator, Rel
 from mindspore._checkparam import Validator
 from mindspore._checkparam import check_bool, twice, check_int_positive
@@ -254,6 +257,11 @@ class Conv2d(_Conv):
         return s
 
 
+@constexpr
+def _check_input_3d(input_shape):
+    if len(input_shape) != 3:
+        raise ValueError(f"Input should be 3d, but got shape {input_shape}")
+
 class Conv1d(_Conv):
     r"""
     1D convolution layer.
@@ -359,6 +367,15 @@ class Conv1d(_Conv):
         kernel_size = (1, kernel_size)
         stride = (1, stride)
         dilation = (1, dilation)
+        get_shape = P.Shape()
+        get_dtype = P.DType()
+        if isinstance(weight_init, Tensor):
+            weight_init_shape = get_shape(weight_init)
+            Validator.check_integer('weight_init_shape', len(weight_init_shape), 3, Rel.EQ, self.cls_name)
+            weight_init_dtype = get_dtype(weight_init)
+            weight_init_value = weight_init.asnumpy()
+            weight_init_value = np.expand_dims(weight_init_value, 2)
+            weight_init = Tensor(weight_init_value, weight_init_dtype)
 
         super(Conv1d, self).__init__(
             in_channels,
@@ -391,13 +408,13 @@ class Conv1d(_Conv):
 
     def construct(self, x):
         x_shape = self.shape(x)
-        if len(x_shape) == 3:
-            x = self.expand_dims(x, 2)
+        _check_input_3d(x_shape)
+        x = self.expand_dims(x, 2)
         output = self.conv2d(x, self.weight)
         if self.has_bias:
             output = self.bias_add(output, self.bias)
-        if len(x_shape) == 3:
-            output = self.squeeze(output)
+
+        output = self.squeeze(output)
         return output
 
     def extend_repr(self):
@@ -669,6 +686,15 @@ class Conv1dTranspose(_Conv):
         kernel_size = (1, kernel_size)
         stride = (1, stride)
         dilation = (1, dilation)
+        get_shape = P.Shape()
+        get_dtype = P.DType()
+        if isinstance(weight_init, Tensor):
+            weight_init_shape = get_shape(weight_init)
+            Validator.check_integer('weight_init_shape', len(weight_init_shape), 3, Rel.EQ, self.cls_name)
+            weight_init_dtype = get_dtype(weight_init)
+            weight_init_value = weight_init.asnumpy()
+            weight_init_value = np.expand_dims(weight_init_value, 2)
+            weight_init = Tensor(weight_init_value, weight_init_dtype)
         # out_channels and in_channels swap.
         # cause Conv2DBackpropInput's out_channel refers to Conv2D's out_channel,
         # then Conv1dTranspose's out_channel refers to Conv2DBackpropInput's in_channel.
@@ -733,8 +759,8 @@ class Conv1dTranspose(_Conv):
 
     def construct(self, x):
         x_shape = self.shape(x)
-        if len(x_shape) == 3:
-            x = self.expand_dims(x, 2)
+        _check_input_3d(x_shape)
+        x = self.expand_dims(x, 2)
 
         n, _, h, w = self.shape(x)
 
@@ -746,8 +772,7 @@ class Conv1dTranspose(_Conv):
         if self.has_bias:
             output = self.bias_add(output, self.bias)
 
-        if len(x_shape) == 3:
-            output = self.squeeze(output)
+        output = self.squeeze(output)
         return output
 
     def extend_repr(self):
diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index 061c97cc7b..3b854037cf 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -1690,7 +1690,9 @@ class L2Loss(PrimitiveWithInfer):
     Set `input_x` as x and output as loss.
 
     .. math::
-        loss = sum(x ** 2) / 2
+        loss = sum(x ** 2) / nelement(x)
+
+    :math:`nelement(x)` represents the number of `input_x`.
 
     Inputs:
         - **input_x** (Tensor) - A input Tensor.