further refining the comments

theano/tensor/nnet/abstract_conv.py

@@ -191,29 +191,29 @@ def conv2d_grad_wrt_inputs(output_grad,
     used by the convolution, such that the output_grad is upsampled
     to the input shape.
 
-    :type output_grad: symbolic 4D tensor as the output gradient
-        of the convolution. This is the tensor that will be upsampled
-        or whose gradient will be taken with respect to the input of
-        the convolution.
+    :type output_grad: symbolic 4D tensor.
     :param output_grad: mini-batch of feature map stacks, of shape
         (batch size, input channels, input rows, input columns).
-        See the optional parameter ``input_shape``.
+        This is the tensor that will be upsampled or the output
+        gradient of the convolution whose gradient will be taken
+        with respect to the input of the convolution.
+        See the optional parameter ``output_grad_shape``.
 
-    :type filters: symbolic 4D tensor
+    :type filters: symbolic 4D tensor.
     :param filters: set of filters used in CNN layer of shape
         (output channels, input channels, filter rows, filter columns).
         See the optional parameter ``filter_shape``.
 
     :type output_grad_shape: None, tuple/list of len 4 of int or
-        Constant variable indicating the shape of the output_grad.
+        Constant variable.
     :param output_grad_shape: The shape of the output_grad parameter.
         Optional, possibly used to choose an optimal implementation.
         You can give ``None`` for any element of the list to specify that this
         element is not known at compile time.
 
-    :type input_shape: tuple/list of len 2 of int or Constant variable
-        indicating the row and column size of the input (upsampled) features.
-    :param input_shape: The shape of the input parameter.
+    :type input_shape: tuple/list of len 2 of int or Constant variable.
+    :param input_shape: The shape (row and column size) of the
+        input (upsampled) parameter.
         Not Optional, since given the output_grad_shape and the subsample values,
         multiple input_shape may be plausible.
 
@@ -250,7 +250,7 @@ def conv2d_grad_wrt_inputs(output_grad,
         are not flipped and the operation is referred to as a
         cross-correlation.
 
-    :rtype: symbolic 4D tensor
+    :rtype: symbolic 4D tensor.
     :return: set of feature maps generated by convolutional layer. Tensor is
         of shape (batch size, output channels, output rows, output columns)
 
@@ -283,35 +283,37 @@ def conv2d_grad_wrt_weights(input,
     """This function will build the symbolic graph for getting the
     gradient of the output of a convolution (output_grad) w.r.t its wights.
 
-    :type input: symbolic 4D tensor as the input of the convolution
-         in the forward pass
+    :type input: symbolic 4D tensor.
     :param input: mini-batch of feature map stacks, of shape
         (batch size, input channels, input rows, input columns).
+        This is the input of the convolution in the forward pass.
 
-    :type output_grad: symbolic 4D tensor as the gradient output
-        of the convolution
+    :type output_grad: symbolic 4D tensor.
     :param output_grad: mini-batch of feature map stacks, of shape
         (batch size, input channels, input rows, input columns).
+        This is the gradient of the output of convolution.
 
-    :type filters: symbolic 4D tensor
+    :type filters: symbolic 4D tensor.
     :param filters: set of filters used in CNN layer of shape
         (output channels, input channels, filter rows, filter columns).
         See the optional parameter ``filter_shape``.
 
-    :type output_grad_shape: None, tuple/list of len 4 of int or Constant variable
+    :type output_grad_shape: None, tuple/list of len 4 of int
+        or Constant variable.
     :param output_grad_shape: The shape of the input parameter.
         Optional, possibly used to choose an optimal implementation.
         You can give ``None`` for any element of the list to specify that this
         element is not known at compile time.
 
-    :type input_shape: tuple/list of len 2 of int or Constant variable
-        indicating the row and column size of the input in the forward pass.
+    :type input_shape: tuple/list of len 2 of int or Constant variable.
     :param input_shape: The shape of the input parameter.
+        This parameter indicates the row and column size of the input
+        in the forward pass.
         Optional, possibly used to choose an optimal implementation.
         You can give ``None`` for any element of the list to specify that this
         element is not known at compile time.
 
-    :type filter_shape: None, tuple/list of len 4 of int or Constant variable
+    :type filter_shape: None, tuple/list of len 4 of int or Constant variable.
     :param filter_shape: The shape of the filters parameter.
         Not Optional, since given the output_grad_shape and the input_shape,
         multiple filter_shape may be plausible.
@@ -343,7 +345,7 @@ def conv2d_grad_wrt_weights(input,
         are not flipped and the operation is referred to as a
         cross-correlation.
 
-    :rtype: symbolic 4D tensor
+    :rtype: symbolic 4D tensor.
     :return: set of feature maps generated by convolutional layer. Tensor is
         of shape (batch size, output channels, output rows, output columns)