diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 0ac18686..870dec4f 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -70,10 +70,10 @@ def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):
         # Set input according to defined shapes and make arrays single and
         # C-contiguous as Caffe expects.
         for in_, blob in kwargs.iteritems():
-            if blob.shape[0] != self.blobs[in_].num:
-                raise Exception('Input is not batch sized')
             if blob.ndim != 4:
                 raise Exception('{} blob is not 4-d'.format(in_))
+            if blob.shape[0] != self.blobs[in_].num:
+                raise Exception('Input is not batch sized')
             self.blobs[in_].data[...] = blob
 
     self._forward(start_ind, end_ind)
@@ -117,10 +117,10 @@ def _Net_backward(self, diffs=None, start=None, end=None, **kwargs):
         # Set top diffs according to defined shapes and make arrays single and
         # C-contiguous as Caffe expects.
         for top, diff in kwargs.iteritems():
-            if diff.shape[0] != self.blobs[top].num:
-                raise Exception('Diff is not batch sized')
             if diff.ndim != 4:
                 raise Exception('{} diff is not 4-d'.format(top))
+            if diff.shape[0] != self.blobs[top].num:
+                raise Exception('Diff is not batch sized')
             self.blobs[top].diff[...] = diff
 
     self._backward(start_ind, end_ind)
@@ -284,17 +284,16 @@ def _Net_preprocess(self, input_name, input_):
     caffe_in = input_.astype(np.float32)
     input_scale = self.input_scale.get(input_name)
     channel_order = self.channel_swap.get(input_name)
-    mean = self.mean.get(input_name)
     in_size = self.blobs[input_name].data.shape[2:]
     if caffe_in.shape[:2] != in_size:
         caffe_in = caffe.io.resize_image(caffe_in, in_size)
-    if input_scale:
+    if input_scale is not None:
         caffe_in *= input_scale
-    if channel_order:
+    if channel_order is not None:
         caffe_in = caffe_in[:, :, channel_order]
     caffe_in = caffe_in.transpose((2, 0, 1))
-    if mean is not None:
-        caffe_in -= mean
+    if hasattr(self, 'mean'):
+        caffe_in -= self.mean.get(input_name, 0)
     return caffe_in
 
 
@@ -305,15 +304,14 @@ def _Net_deprocess(self, input_name, input_):
     decaf_in = input_.copy().squeeze()
     input_scale = self.input_scale.get(input_name)
     channel_order = self.channel_swap.get(input_name)
-    mean = self.mean.get(input_name)
-    if mean is not None:
-        decaf_in += mean
+    if hasattr(self, 'mean'):
+        decaf_in += self.mean.get(input_name, 0)
     decaf_in = decaf_in.transpose((1,2,0))
-    if channel_order:
+    if channel_order is not None:
         channel_order_inverse = [channel_order.index(i)
                                  for i in range(decaf_in.shape[2])]
         decaf_in = decaf_in[:, :, channel_order_inverse]
-    if input_scale:
+    if input_scale is not None:
         decaf_in /= input_scale
     return decaf_in