2D CNN's and 3D CNN's description update. (keras-team#20059)

* Update conv2d.py

Update Conv2D description

* Updated conv2D and Conv3D description

* Update Conv2D and Conv3D description

* Update conv2D and conv3D description_1

keras/src/layers/convolutional/conv2d.py

@@ -7,10 +7,10 @@ class Conv2D(BaseConv):
     """2D convolution layer.
 
     This layer creates a convolution kernel that is convolved with the layer
-    input over a single spatial (or temporal) dimension to produce a tensor of
-    outputs. If `use_bias` is True, a bias vector is created and added to the
-    outputs. Finally, if `activation` is not `None`, it is applied to the
-    outputs as well.
+    input over a 2D spatial (or temporal) dimension (height and width) to 
+    produce a tensor of outputs. If `use_bias` is True, a bias vector is created 
+    and added to the outputs. Finally, if `activation` is not `None`, it is 
+    applied to the outputs as well.
 
     Args:
         filters: int, the dimension of the output space (the number of filters

keras/src/layers/convolutional/conv3d.py

@@ -7,10 +7,10 @@ class Conv3D(BaseConv):
     """3D convolution layer.
 
     This layer creates a convolution kernel that is convolved with the layer
-    input over a single spatial (or temporal) dimension to produce a tensor of
-    outputs. If `use_bias` is True, a bias vector is created and added to the
-    outputs. Finally, if `activation` is not `None`, it is applied to the
-    outputs as well.
+    input over a 3D spatial (or temporal) dimension (width,height and depth) to 
+    produce a tensor of outputs. If `use_bias` is True, a bias vector is created 
+    and added to the outputs. Finally, if `activation` is not `None`, it is 
+    applied to the outputs as well.
 
     Args:
         filters: int, the dimension of the output space (the number of filters