Removed tabs from doc files

doc/tutorial/examples.txt

@@ -416,9 +416,9 @@ The preceding elements are featured in this more realistic example.  It will be
   prediction = p_1 > 0.5                    # The prediction thresholded
   xent = -y*T.log(p_1) - (1-y)*T.log(1-p_1) # Cross-entropy loss function
   cost = xent.mean() + 0.01*(w**2).sum()    # The cost to minimize
-  gw,gb = T.grad(cost, [w,b])		  # Compute the gradient of the cost
-					  # (we shall return to this in a 
-					  # following section of this tutorial)
+  gw,gb = T.grad(cost, [w,b])               # Compute the gradient of the cost
+                                            # (we shall return to this in a
+                                            # following section of this tutorial)
 
   # Compile
   train = theano.function(

doc/tutorial/extending_theano.txt

@@ -68,8 +68,8 @@ Op Contract
             pass
 
         # C implementation: [see theano web site for other functions]
-	def c_code(...):
-	# ...
+        def c_code(...):
+            # ...
             pass
 
         # others implementation (pycuda, ...):
@@ -83,7 +83,7 @@ Op Contract
         def grad(self, inputs, g):
             pass
 
-	def R_op(self, inputs, eval_points):
+        def R_op(self, inputs, eval_points):
             pass
 
         def infer_shape(node, (i0_shapes, ...))
@@ -157,7 +157,7 @@ Op Example
         def grad(self, inputs, output_grads):
             return [output_grads[0] * 2]
 
-	def R_op(self, inputs, eval_points):
+        def R_op(self, inputs, eval_points):
             # R_op can receive None as eval_points.
             # That mean there is no diferientiable path through that input
             # If this imply that you cannot compute some outputs,

doc/tutorial/gpu_data_convert.txt

@@ -69,7 +69,7 @@ CudaNdarrays. Here is an example from the file ``theano/misc/tests/test_pycuda_t
       gb = cuda_ndarray.CudaNdarray(b)
       dest = cuda_ndarray.CudaNdarray.zeros(a.shape)
       multiply_them(dest, ga, gb,
-		    block=(400, 1, 1), grid=(1, 1))
+                    block=(400, 1, 1), grid=(1, 1))
       assert (numpy.asarray(dest) == a * b).all()
 
 

doc/tutorial/index.txt

@@ -29,7 +29,7 @@ you out.
     adding
     examples
     symbolic_graphs
-    printing_drawing	
+    printing_drawing
     gradients
     modes
     loading_and_saving

doc/tutorial/loop.txt

@@ -61,7 +61,7 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
   import numpy
   import theano
   import theano.tensor as T
-  theano.config.warn.subtensor_merge_bug = False	
+  theano.config.warn.subtensor_merge_bug = False
 
   coefficients = theano.tensor.vector("coefficients")
   x = T.scalar("x")

doc/tutorial/modes.txt

@@ -83,14 +83,14 @@ Consider the logistic regression:
                 name = "predict")
 
     if any([x.op.__class__.__name__ in ['Gemv', 'CGemv', 'Gemm', 'CGemm'] for x in
-    train.maker.fgraph.toposort()]):
-	print 'Used the cpu'
+            train.maker.fgraph.toposort()]):
+        print 'Used the cpu'
     elif any([x.op.__class__.__name__ in ['GpuGemm', 'GpuGemv'] for x in
-    train.maker.fgraph.toposort()]):
-	print 'Used the gpu'
+              train.maker.fgraph.toposort()]):
+        print 'Used the gpu'
     else:
-	print 'ERROR, not able to tell if theano used the cpu or the gpu'
-	print train.maker.fgraph.toposort()
+        print 'ERROR, not able to tell if theano used the cpu or the gpu'
+        print train.maker.fgraph.toposort()
 
     for i in range(training_steps):
         pred, err = train(D[0], D[1])

doc/tutorial/using_gpu.txt

@@ -44,7 +44,7 @@ file and run it.
     rng = numpy.random.RandomState(22)
     x = shared(numpy.asarray(rng.rand(vlen), config.floatX))
     f = function([], T.exp(x))
-    print f.maker.fgraph.toposort()	
+    print f.maker.fgraph.toposort()
     t0 = time.time()
     for i in xrange(iters):
         r = f()
@@ -339,14 +339,14 @@ Consider again the logistic regression:
                 name = "predict")
 
     if any([x.op.__class__.__name__ in ['Gemv', 'CGemv', 'Gemm', 'CGemm'] for x in
-    train.maker.fgraph.toposort()]):
-	print 'Used the cpu'
+            train.maker.fgraph.toposort()]):
+        print 'Used the cpu'
     elif any([x.op.__class__.__name__ in ['GpuGemm', 'GpuGemv'] for x in
-    train.maker.fgraph.toposort()]):
-	print 'Used the gpu'
+              train.maker.fgraph.toposort()]):
+        print 'Used the gpu'
     else:
-	print 'ERROR, not able to tell if theano used the cpu or the gpu'
-	print train.maker.fgraph.toposort()
+        print 'ERROR, not able to tell if theano used the cpu or the gpu'
+        print train.maker.fgraph.toposort()
 
     for i in range(training_steps):
         pred, err = train(D[0], D[1])