Whitespace cleanup.

theano/tensor/nnet/Conv3D.py

@@ -109,7 +109,7 @@ class Conv3D(theano.Op):
         dCdV.name = 'Conv3D_dCdV.dCdH='+dCdH_name+',V='+V_name
         dCdW.name = 'Conv3D_dCdW.dCdH='+dCdH_name+',V='+V_name+',W='+W_name
         dCdb.name = 'Conv3D_dCdb.dCdH='+dCdH_name+',V='+V_name+',W='+W_name+',b='+b_name
-        
+
 
 
         return [ dCdV, dCdW, dCdb, dCdd ]
@@ -140,7 +140,7 @@ class Conv3D(theano.Op):
         output_dur = T.floor( (vidDur - filterDur) / dt ) +1
 
         rval = (batch_size,  output_height, output_width, output_dur, output_channels )
-    
+
 
         return [ rval ]
 

theano/tensor/nnet/ConvTransp3D.py

@@ -73,7 +73,7 @@ class ConvTransp3D(theano.Op):
         else:
             b_name = 'anon'
 
-        
+
         dCdW.name = 'ConvTransp3D_dCdW.H='+H_name+',dCdR='+dCdR_name+',W='+W_name
         dCdb.name = 'ConvTransp3D_dCdb.H='+H_name+',dCdR='+dCdR_name+',W='+W_name+',b='+b_name
         dCdH.name = 'ConvTransp3D_dCdH.H='+H_name+',dCdR='+dCdR_name

theano/tensor/nnet/sigm.py

@@ -25,7 +25,7 @@ class ScalarSigmoid(scalar.UnaryScalarOp):
         if x < -30.0:
             return 0.0
         if x > 30.0:
-            return 1.0 
+            return 1.0
         return 1.0 / (1.0 + numpy.exp(-x))
     def impl(self, x):
         return ScalarSigmoid.st_impl(x)
@@ -125,7 +125,7 @@ def _is_1(expr):
         return False
 
 log1msigm_to_softplus = gof.PatternSub(
-    (tensor.log, 
+    (tensor.log,
         (tensor.sub,
             dict(pattern='y', constraint = _is_1),
             (sigmoid, 'x'))),
@@ -134,7 +134,7 @@ log1msigm_to_softplus = gof.PatternSub(
     skip_identities_fn=_skip_mul_1)
 
 log1pexp_to_softplus = gof.PatternSub(
-    (tensor.log1p, 
+    (tensor.log1p,
      (tensor.exp, 'x')),
     (softplus, 'x'),
     allow_multiple_clients = True)
@@ -289,7 +289,7 @@ def local_sigm_times_exp(node):
                 rval = tensor.mul(*terms)
             else:
                 rval = terms[0]
-            
+
             if neg:
                 return [-rval]
             else:
@@ -336,13 +336,13 @@ def local_1msigmoid(node):
                 return [sigmoid(-sub_r.owner.inputs[0])]
 
 register_local_1msigmoid = False
-# This is False because the Stabilize pattern above 
-# is looking for 1-sigm.  Also Canonizer turns neg into *(-1) and so 
+# This is False because the Stabilize pattern above
+# is looking for 1-sigm.  Also Canonizer turns neg into *(-1) and so
 # this optimization might set off an unwanted chain of things.
 # OTH - this transformation can be seen as pushing normal arithmetic either  below or above the
 # sigmoidal nonlinearity... so if the canonicalized form had anything to say about that then it
 # would be a consideration... anyway leaving False for now.
-              
+
 if register_local_1msigmoid:
     opt.register_canonicalize(local_1msigmoid)
 

theano/tensor/raw_random.py

@@ -155,7 +155,7 @@ class RandomFunction(gof.Op):
                 raise TypeError('r must be RandomStateType instance', r)
         # the following doesn't work because we want to ignore the broadcastable flags in
         # shape.type
-        # assert shape.type == tensor.lvector 
+        # assert shape.type == tensor.lvector
 
         # convert args to TensorType instances
         # and append enough None's to match the length of self.args

theano/tensor/signal/downsample.py

 """ Ops for downsampling images.
 
-Planned: 
+Planned:
 DownsampleFactorMax, DownsampleAvg, DownsampleSoftmax.
 
 """
@@ -21,7 +21,7 @@ def max_pool_2d(input, ds, ignore_border=False):
     the specified factor, by keeping only the maximum value of non-overlapping
     patches of size (ds[0],ds[1])
 
-    :type input: N-D theano tensor of input images. 
+    :type input: N-D theano tensor of input images.
     :param input: input images. Max pooling will be done over the 2 last dimensions.
     :type ds: tuple of length 2
     :param ds: factor by which to downscale. (2,2) will halve the image in each dimension.
@@ -39,7 +39,7 @@ def max_pool_2d(input, ds, ignore_border=False):
     batch_size = tensor.shape_padright(batch_size,1)
 
     # store as 4D tensor with shape: (batch_size,1,height,width)
-    new_shape = tensor.cast(tensor.join(0, batch_size, 
+    new_shape = tensor.cast(tensor.join(0, batch_size,
         tensor.as_tensor([1,]), img_shape), 'int64')
     input_4D = tensor.reshape(input, new_shape, ndim=4)
 
@@ -54,7 +54,7 @@ def max_pool_2d(input, ds, ignore_border=False):
 
 class DownsampleFactorMax(Op):
     """
-    For N-dimensional tensors, consider that the last two dimensions span images.  
+    For N-dimensional tensors, consider that the last two dimensions span images.
     This Op downsamples these images by a factor ds, by taking the max over non-
     overlapping rectangular regions.
     """
@@ -77,7 +77,7 @@ class DownsampleFactorMax(Op):
         :rtype: list
         :returns: the shape of the output from this op, for input of given shape.  This will
         have the same length as imgshape, but with last two elements reduced as per the
-        downsampling & ignore_border flags.  
+        downsampling & ignore_border flags.
         """
         if len(imgshape) < 2:
             raise TypeError('imgshape must have at least two elements (rows, cols)')
@@ -162,7 +162,7 @@ class DownsampleFactorMax(Op):
         int x_shp0_usable;
         int x_shp1_usable;
         int z_shp0, z_shp1;
-        if(%(x)s->nd!=4) 
+        if(%(x)s->nd!=4)
         {
             PyErr_SetString(PyExc_ValueError, "x must be a 4d ndarray");
             %(fail)s;
@@ -289,17 +289,17 @@ class DownsampleFactorMaxGrad(Op):
             PyErr_SetString(PyExc_ValueError, "input types must all match");
             %(fail)s;
         }
-        if(%(x)s->nd!=4) 
+        if(%(x)s->nd!=4)
         {
             PyErr_SetString(PyExc_ValueError, "x must be a 4d ndarray");
             %(fail)s;
         }
-        if(%(z)s->nd!=4) 
+        if(%(z)s->nd!=4)
         {
             PyErr_SetString(PyExc_ValueError, "z must be a 4d ndarray");
             %(fail)s;
         }
-        if(%(gz)s->nd!=4) 
+        if(%(gz)s->nd!=4)
         {
             PyErr_SetString(PyExc_ValueError, "gz must be a 4d ndarray");
             %(fail)s;