Merge pull request #3095 from harlouci/flake8_v4

theano/tensor/nnet/Conv3D.py

 from __future__ import print_function
+
+import numpy as N
 from six.moves import xrange
+
 import theano
 from theano.tensor import basic as T
-import numpy as N
-#from util import strutil
+# from util import strutil
 from theano.tensor.blas_headers import blas_header_text, blas_header_version
 from theano.tensor.blas import ldflags
 from theano.misc import strutil
@@ -72,26 +74,28 @@ class Conv3D(theano.Op):
 
     def grad(self, inputs, output_gradients):
         V, W, b, d = inputs
-        dCdH , = output_gradients
+        dCdH, = output_gradients
         # make all of these ops support broadcasting of scalar b to vector b and eplace the zeros_like in all their grads
         # print dCdH.broadcastable
         # print "dCdH.broadcastable"
         # quit(-1)
-        #dCdH = printing.Print("dCdH = ",["shape"])
+        # dCdH = printing.Print("dCdH = ",["shape"])
 
         # Make sure the broadcasting pattern of the gradient is the the same
         # as the initial variable
-        dCdV = ConvTransp3D.convTransp3D(W, T.zeros_like(V[0, 0, 0, 0, :]), d, dCdH, V.shape[1:4])
+        dCdV = theano.tensor.nnet.convTransp3D(
+            W, T.zeros_like(V[0, 0, 0, 0, :]), d, dCdH, V.shape[1:4])
         dCdV = T.patternbroadcast(dCdV, V.broadcastable)
         WShape = W.shape
-        dCdW = ConvGrad3D.convGrad3D(V, d, WShape, dCdH)
+        dCdW = theano.tensor.nnet.convGrad3D(V, d, WShape, dCdH)
         dCdW = T.patternbroadcast(dCdW, W.broadcastable)
         dCdb = T.sum(dCdH, axis=(0, 1, 2, 3))
         dCdb = T.patternbroadcast(dCdb, b.broadcastable)
-        dCdd = grad_undefined(self, 3, inputs[3],
-                "The gradient of Conv3D with respect to the convolution" +\
-                " stride is undefined because Conv3D is only defined for" +\
-                " integer strides.")
+        dCdd = grad_undefined(
+            self, 3, inputs[3],
+            "The gradient of Conv3D with respect to the convolution"
+            " stride is undefined because Conv3D is only defined for"
+            " integer strides.")
 
         if 'name' in dir(dCdH) and dCdH.name is not None:
             dCdH_name = dCdH.name
@@ -113,11 +117,13 @@ class Conv3D(theano.Op):
         else:
             b_name = 'anon_b'
 
-        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+')'
+        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 ]
+        return [dCdV, dCdW, dCdb, dCdd]
 
     def perform(self, node, inputs, output_storage):
         V, W, b, d = inputs
@@ -144,7 +150,7 @@ class Conv3D(theano.Op):
         output_width = T.floor((vidWidth - filterWidth) // dc) + 1
         output_dur = T.floor((vidDur - filterDur) // dt) + 1
 
-        rval = (batch_size,  output_height, output_width, output_dur, output_channels )
+        rval = (batch_size, output_height, output_width, output_dur, output_channels)
 
         return [rval]
 
@@ -155,7 +161,7 @@ class Conv3D(theano.Op):
         return ldflags()
 
     def c_compile_args(self):
-        flags =  ldflags(libs=False, flags=True)
+        flags = ldflags(libs=False, flags=True)
         return flags
 
     def c_lib_dirs(self):
@@ -170,7 +176,7 @@ class Conv3D(theano.Op):
 
         H = outputs[0]
 
-        codeSource =  """
+        codeSource = """
             ///////////// < code generated by Conv3D >
 
             //printf("\t\t\t\tConv3D c code\\n");
@@ -320,13 +326,13 @@ class Conv3D(theano.Op):
         VV, WV, bv, dv = node.inputs
         HV = node.outputs[0]
         if (theano.config.blas.ldflags and
-            VV.dtype == WV.dtype and HV.dtype == VV.dtype):
+                VV.dtype == WV.dtype and HV.dtype == VV.dtype):
             if VV.dtype == 'float64':
                 gemv = 'dgemv_'
             elif VV.dtype == 'float32':
                 gemv = 'sgemv_'
             else:
-                raise Exception('Unrecognized dtype for convolution '+V.value.dtype)
+                raise Exception('Unrecognized dtype for convolution ' + V.value.dtype)
 
             codeSource += """
             if (inputChannels > 20 && outputChannels > 20 && ws4 == sizeof(ELEM_AT(%(W)s,0)))
@@ -571,7 +577,7 @@ def computeH(V, W, b, d):
     outputChannels = W.shape[0]
     inputChannels = V.shape[4]
     if W.shape[4] != inputChannels:
-        raise Exception("W.shape[4] = "+str(W.shape[4])+" but inputChannels = "+str(inputChannels))
+        raise Exception("W.shape[4] = " + str(W.shape[4]) + " but inputChannels = " + str(inputChannels))
     filterHeight = W.shape[1]
     filterWidth = W.shape[2]
     filterDur = W.shape[3]
@@ -586,12 +592,12 @@ def computeH(V, W, b, d):
     assert dy > 0
     assert dt > 0
 
-    outputHeight = int( (vidHeight - filterHeight) / dx )+1
-    outputWidth = int( (vidWidth - filterWidth) / dy )+1
-    outputDur = int( (vidDur - filterDur) / dt ) + 1
+    outputHeight = int((vidHeight - filterHeight) / dx) + 1
+    outputWidth = int((vidWidth - filterWidth) / dy) + 1
+    outputDur = int((vidDur - filterDur) / dt) + 1
 
-    H =  N.zeros( (batchSize,  outputHeight,
-        outputWidth, outputDur, outputChannels ), dtype=V.dtype )
+    H = N.zeros((batchSize, outputHeight,
+                outputWidth, outputDur, outputChannels), dtype=V.dtype)
 
     # H[i,j,x,y,t] = b_j + sum_k sum_l sum_m sum_z W[j,z,k,l,m] V[i,z, dx*x+k,dy*y+l,dt*t+m]
     for i in xrange(0, H.shape[0]):
@@ -610,12 +616,8 @@ def computeH(V, W, b, d):
                                         # if (i,j,x,y,t) == (0,0,0,0,0):
                                         #    print (( W[j,z,k,l,m] , V[i,z,d[0]*x+k,d[1]*y+l,d[2]*t+m] ), (k,l,m) )
                                         w = W[j, k, l, m, z]
-                                        v = V[i, d[0]*x+k, d[1]*y+l, d[2]*t+m, z]
+                                        v = V[i, d[0] * x + k, d[1] * y + l, d[2] * t + m, z]
                                         # if i == 0 and x == 0 and y == 0 and t == 0 and j == 0:
                                         #    print 'setting H[0] += '+str(w*v)+'   W['+str((j,z,k,l,m))+']='+str(w)+'   V['+str((i,d[0]*x+k,d[1]*y+l,d[2]*t+m,z))+']='+str(v)
                                         H[i, x, y, t, j] += w * v
     return H
-
-
-from . import ConvGrad3D
-from . import ConvTransp3D

theano/tensor/nnet/ConvGrad3D.py

+from six.moves import xrange
+
+import numpy as N
+
 import theano
 from theano.tensor import basic as T
 from theano.misc import strutil
-import numpy as N
-from six.moves import xrange
 from theano.gradient import grad_undefined
 from theano.gradient import DisconnectedType
 
@@ -23,11 +25,15 @@ class ConvGrad3D(theano.Op):
         WShape_ = T.as_tensor_variable(WShape)
         dCdH_ = T.as_tensor_variable(dCdH)
 
-        return theano.Apply(self, inputs=[V_, d_, WShape_, dCdH_], outputs=[ T.TensorType(V_.dtype, (False, False, False, False, False))() ] )
+        return theano.Apply(self,
+                            inputs=[V_, d_, WShape_, dCdH_],
+                            outputs=[T.TensorType(
+                                V_.dtype,
+                                (False, False, False, False, False))()])
 
     def infer_shape(self, node, input_shapes):
         V, d, W_shape, dCdH = node.inputs
-        return [ ( W_shape[0], W_shape[1], W_shape[2], W_shape[3], W_shape[4] ) ]
+        return [(W_shape[0], W_shape[1], W_shape[2], W_shape[3], W_shape[4])]
 
     def connection_pattern(self, node):
 
@@ -38,12 +44,12 @@ class ConvGrad3D(theano.Op):
         dLdA, = output_gradients
 
         z = T.zeros_like(C[0, 0, 0, 0, :])
-        dLdC = convTransp3D(dLdA, z, d, B, C.shape[1:4])
+        dLdC = theano.tensor.nnet.convTransp3D(dLdA, z, d, B, C.shape[1:4])
         # d actually does affect the outputs, so it's not disconnected
         dLdd = grad_undefined(self, 1, d)
         # The shape of the weights doesn't affect the output elements
         dLdWShape = DisconnectedType()()
-        dLdB = conv3D(C, dLdA, T.zeros_like(B[0, 0, 0, 0, :]), d)
+        dLdB = theano.tensor.nnet.conv3D(C, dLdA, T.zeros_like(B[0, 0, 0, 0, :]), d)
 
         return [dLdC, dLdd, dLdWShape, dLdB]
 
@@ -54,15 +60,10 @@ class ConvGrad3D(theano.Op):
         # partial C / partial W[j,z,k,l,m] = sum_i sum_p sum_q sum_r (partial C /partial H[i,j,p,q,r] ) *  V[i,z,dr*p+k,dc*q+l,dt*r+m]
 
         batchSize = dCdH.shape[0]
-        outputFilters = dCdH.shape[4]
         outputHeight = dCdH.shape[1]
         outputWidth = dCdH.shape[2]
         outputDur = dCdH.shape[3]
         assert V.shape[0] == batchSize
-        inputFilters = V.shape[4]
-        inputHeight = V.shape[1]
-        inputWidth = V.shape[2]
-        inputDur = V.shape[3]
         dr, dc, dt = d
 
         dCdW = N.zeros(WShape, dtype=V.dtype)
@@ -78,7 +79,10 @@ class ConvGrad3D(theano.Op):
                                 for r in xrange(0, outputDur):
                                     for j in xrange(0, WShape[0]):
                                         for z in xrange(0, WShape[4]):
-                                            dCdW[j, k, l, m, z] +=  dCdH[i, p, q, r, j] * V[i, dr*p+k, dc*q+l, dt*r+m, z]
+                                            dCdW[j, k, l, m, z] += (
+                                                dCdH[i, p, q, r, j] *
+                                                V[i, dr * p + k, dc * q + l,
+                                                  dt * r + m, z])
 
         output_storage[0][0] = dCdW
 
@@ -272,6 +276,3 @@ class ConvGrad3D(theano.Op):
 
 
 convGrad3D = ConvGrad3D()
-
-from theano.tensor.nnet.Conv3D import conv3D
-from theano.tensor.nnet.ConvTransp3D import convTransp3D

theano/tensor/nnet/ConvTransp3D.py

 from __future__ import print_function
+
 import numpy as N
 from six.moves import xrange
+
+import theano
 from theano.tensor import basic as T
 from theano.misc import strutil
-import theano
 from theano.gradient import grad_undefined
 from theano.gradient import DisconnectedType
 
@@ -31,12 +33,15 @@ class ConvTransp3D(theano.Op):
         else:
             RShape_ = T.as_tensor_variable([-1, -1, -1])
 
-        return theano.Apply(self, inputs=[W_, b_, d_, H_, RShape_], outputs=[ T.TensorType(H_.dtype, (False, False, False, False, False))() ] )
+        return theano.Apply(self,
+                            inputs=[W_, b_, d_, H_, RShape_],
+                            outputs=[T.TensorType(H_.dtype,
+                                     (False, False, False, False, False))()])
 
     def infer_shape(self, node, input_shapes):
         W, b, d, H, RShape = node.inputs
         W_shape, b_shape, d_shape, H_shape, RShape_shape = input_shapes
-        return [(H_shape[0],  RShape[0], RShape[1], RShape[2], W_shape[4])]
+        return [(H_shape[0], RShape[0], RShape[1], RShape[2], W_shape[4])]
 
     def connection_pattern(self, node):
         return [[True], [True], [True], [True], [False]]
@@ -44,9 +49,9 @@ class ConvTransp3D(theano.Op):
     def grad(self, inputs, output_gradients):
         W, b, d, H, RShape = inputs
         dCdR, = output_gradients
-        dCdH = conv3D(dCdR, W, T.zeros_like(H[0, 0, 0, 0, :]), d)
+        dCdH = theano.tensor.nnet.conv3D(dCdR, W, T.zeros_like(H[0, 0, 0, 0, :]), d)
         WShape = W.shape
-        dCdW = convGrad3D(dCdR, d, WShape, H)
+        dCdW = theano.tensor.nnet.convGrad3D(dCdR, d, WShape, H)
         dCdb = T.sum(dCdR, axis=(0, 1, 2, 3))
         # not differentiable, since d affects the output elements
         dCdd = grad_undefined(self, 2, d)
@@ -73,11 +78,13 @@ class ConvTransp3D(theano.Op):
         else:
             b_name = 'anon_b'
 
-        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
+        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
 
-        return [dCdW,  dCdb, dCdd, dCdH, dCdRShape]
+        return [dCdW, dCdb, dCdd, dCdH, dCdRShape]
 
     def perform(self, node, inputs, output_storage):
         W, b, d, H, RShape = inputs
@@ -335,7 +342,7 @@ def computeR(W, b, d, H, Rshape=None):
     assert len(b.shape) == 1
     assert len(d) == 3
 
-    outputChannels,  filterHeight, filterWidth, filterDur, \
+    outputChannels, filterHeight, filterWidth, filterDur, \
         inputChannels = W.shape
     batchSize, outputHeight, outputWidth, outputDur, \
         outputChannelsAgain = H.shape
@@ -367,7 +374,7 @@ def computeR(W, b, d, H, Rshape=None):
     # print "video size: "+str((videoHeight, videoWidth, videoDur))
 
     R = N.zeros((batchSize, videoHeight,
-            videoWidth, videoDur, inputChannels), dtype=H.dtype)
+                videoWidth, videoDur, inputChannels), dtype=H.dtype)
 
     # R[i,j,r,c,t] = b_j + sum_{rc,rk | d \circ rc + rk = r} sum_{cc,ck | ...} sum_{tc,tk | ...} sum_k W[k, j, rk, ck, tk] * H[i,k,rc,cc,tc]
     for i in xrange(0, batchSize):
@@ -404,8 +411,8 @@ def computeR(W, b, d, H, Rshape=None):
                                     if tk < 0:
                                         break
 
-                                    R[
-                                        i, r, c, t, j] += N.dot(W[:, rk, ck, tk, j], H[i, rc, cc, tc, :] )
+                                    R[i, r, c, t, j] += N.dot(
+                                        W[:, rk, ck, tk, j], H[i, rc, cc, tc, :])
 
                                     tc += 1
                                 ""  # close loop over tc
@@ -421,7 +428,3 @@ def computeR(W, b, d, H, Rshape=None):
     ""  # close loop over i
 
     return R
-
-
-from theano.tensor.nnet.Conv3D import conv3D
-from theano.tensor.nnet.ConvGrad3D import convGrad3D

theano/tensor/nnet/conv3d2d.py

@@ -194,13 +194,13 @@ def conv3d(signals, filters,
         _signals_shape_5d[2],
         _signals_shape_5d[3],
         _signals_shape_5d[4],
-        )
+    )
     _filters_shape_4d = (
         _filters_shape_5d[0] * _filters_shape_5d[1],
         _filters_shape_5d[2],
         _filters_shape_5d[3],
         _filters_shape_5d[4],
-        )
+    )
 
     if border_mode[1] != border_mode[2]:
         raise NotImplementedError('height and width bordermodes must match')
@@ -228,7 +228,7 @@ def conv3d(signals, filters,
             _filters_shape_5d[1],  # Tf
             _signals_shape_5d[3] - _filters_shape_5d[3] + 1,
             _signals_shape_5d[4] - _filters_shape_5d[4] + 1,
-            ))
+        ))
     elif border_mode[1] == 'full':
         out_tmp = out_4d.reshape((
             _signals_shape_5d[0],  # Ns
@@ -237,7 +237,7 @@ def conv3d(signals, filters,
             _filters_shape_5d[1],  # Tf
             _signals_shape_5d[3] + _filters_shape_5d[3] - 1,
             _signals_shape_5d[4] + _filters_shape_5d[4] - 1,
-            ))
+        ))
     elif border_mode[1] == 'same':
         raise NotImplementedError()
     else:
@@ -246,15 +246,15 @@ def conv3d(signals, filters,
     # now sum out along the Tf to get the output
     # but we have to sum on a diagonal through the Tf and Ts submatrix.
     if border_mode[0] == 'valid':
-        if _filters_shape_5d[1]!=1:
-          out_5d = diagonal_subtensor(out_tmp, 1, 3).sum(axis=3)
-        else: # for Tf==1, no sum along Tf, the Ts-axis of the output is unchanged!
-          out_5d = out_tmp.reshape((
-            _signals_shape_5d[0],
-            _signals_shape_5d[1],
-            _filters_shape_5d[0],
-            _signals_shape_5d[3] - _filters_shape_5d[3] + 1,
-            _signals_shape_5d[4] - _filters_shape_5d[4] + 1,
+        if _filters_shape_5d[1] != 1:
+            out_5d = diagonal_subtensor(out_tmp, 1, 3).sum(axis=3)
+        else:  # for Tf==1, no sum along Tf, the Ts-axis of the output is unchanged!
+            out_5d = out_tmp.reshape((
+                _signals_shape_5d[0],
+                _signals_shape_5d[1],
+                _filters_shape_5d[0],
+                _signals_shape_5d[3] - _filters_shape_5d[3] + 1,
+                _signals_shape_5d[4] - _filters_shape_5d[4] + 1,
             ))
     elif border_mode[0] in ('full', 'same'):
         raise NotImplementedError('sequence border mode', border_mode[0])
@@ -316,7 +316,7 @@ if cuda.cuda_available:
 def local_inplace_DiagonalSubtensor(node):
     """ also work for IncDiagonalSubtensor """
     if (isinstance(node.op, (DiagonalSubtensor, IncDiagonalSubtensor)) and
-        not node.op.inplace):
+            not node.op.inplace):
         new_op = node.op.__class__(inplace=True)
         new_node = new_op(*node.inputs)
         return [new_node]

theano/tensor/nnet/neighbours.py

@@ -2,15 +2,15 @@
 TODO: implement Images2Neibs.infer_shape() methods
 
 """
-from six.moves import xrange
+
+import numpy
+
 import theano
 from theano import Op, Apply
 import theano.tensor as T
 from theano.gradient import grad_not_implemented
 from theano.gradient import grad_undefined
 
-import numpy
-
 
 class Images2Neibs(Op):
 
@@ -206,7 +206,7 @@ class Images2Neibs(Op):
                                 z_col = j + d * i
 
                                 z[0][z_row, z_col] = ten4[n, s, ten4_2, ten4_3]
-                                
+
     def infer_shape(self, node, input_shape):
         in_shape = input_shape[0]
         c, d = node.inputs[1]
@@ -223,7 +223,7 @@ class Images2Neibs(Op):
         z_dim0 = grid_c * grid_d * in_shape[1] * in_shape[0]
         z_dim1 = c * d
         return [(z_dim0, z_dim1)]
-    
+
     def c_code(self, node, name, inp, out, sub):
         ten4, neib_shape, neib_step = inp
         z, = out
@@ -417,21 +417,21 @@ class Images2Neibs(Op):
 
 
 def images2neibs(ten4, neib_shape, neib_step=None, mode='valid'):
-    """ 
+    """
     Function :func:`images2neibs <theano.sandbox.neighbours.images2neibs>`
-    allows to apply a sliding window operation to a tensor containing 
+    allows to apply a sliding window operation to a tensor containing
     images
-    or other two-dimensional objects. 
-    The sliding window operation loops 
-    over points in input data and stores a rectangular neighbourhood of 
-    each point.   
-    It is possible to assign a step of selecting patches (parameter 
-    `neib_step`). 
-
-    :param ten4:     A 4-dimensional tensor which represents 
+    or other two-dimensional objects.
+    The sliding window operation loops
+    over points in input data and stores a rectangular neighbourhood of
+    each point.
+    It is possible to assign a step of selecting patches (parameter
+    `neib_step`).
+
+    :param ten4:     A 4-dimensional tensor which represents
                      a list of lists of images.a list of lists of images.
                      It should have shape (list 1 dim, list 2 dim,
-                     row, col). The first two dimensions can be 
+                     row, col). The first two dimensions can be
                      useful to store different channels and batches.
     :type ten4:      A 4d tensor-like.
     :param neib_shape: A tuple containing two
@@ -442,20 +442,20 @@ def images2neibs(ten4, neib_shape, neib_step=None, mode='valid'):
     :type neib_shape: A 1d tensor-like of 2 values.
     :param neib_step: (dr,dc) where dr is the number of rows to
                       skip between patch and dc is the number of
-                      columns. The parameter should be a tuple of two elements: 
-                      number 
-                      of rows and number of columns to skip each iteration. 
+                      columns. The parameter should be a tuple of two elements:
+                      number
+                      of rows and number of columns to skip each iteration.
                       Basically, when the step is 1, the neighbourhood of every
-                      first element is taken and every possible rectangular 
+                      first element is taken and every possible rectangular
                       subset is returned. By default it is equal to
                       `neib_shape` in other words, the
-                      patches are disjoint. When the step is greater than 
+                      patches are disjoint. When the step is greater than
                       `neib_shape`, some elements are omitted. When None, this
                       is the same as
                       neib_shape(patch are disjoint)
 
-                      .. note:: Currently the step size should be chosen in the way that the 
-                         corresponding dimension :math:`i` (width or height) is equal to 
+                      .. note:: Currently the step size should be chosen in the way that the
+                         corresponding dimension :math:`i` (width or height) is equal to
                          :math:`n * step\_size_i + neib\_shape_i` for some :math:`n`
     :type neib_step: A 1d tensor-like of 2 values.
     :param mode:
@@ -489,29 +489,29 @@ def images2neibs(ten4, neib_shape, neib_step=None, mode='valid'):
                                   = flattened version of ten4[i,j,l:l+r,k:k+c]
                              idx += 1
 
-          .. note:: The operation isn't necessarily implemented internally with 
-             these for loops, they're just the easiest way to describe the 
+          .. note:: The operation isn't necessarily implemented internally with
+             these for loops, they're just the easiest way to describe the
              output pattern.
 
     Example:
-  
+
     .. code-block:: python
-  
+
         # Defining variables
         images = T.tensor4('images')
         neibs = images2neibs(images, neib_shape=(5, 5))
-  
-        # Constructing theano function 
+
+        # Constructing theano function
         window_function = theano.function([images], neibs)
-  
+
         # Input tensor (one image 10x10)
         im_val = np.arange(100.).reshape((1, 1, 10, 10))
-  
+
         # Function application
         neibs_val = window_function(im_val)
-  
-    .. note:: The underlying code will construct a 2D tensor of disjoint 
-       patches 5x5. The output has shape 4x25. 
+
+    .. note:: The underlying code will construct a 2D tensor of disjoint
+       patches 5x5. The output has shape 4x25.
     """
     return Images2Neibs(mode)(ten4, neib_shape, neib_step)
 
@@ -524,25 +524,24 @@ def neibs2images(neibs, neib_shape, original_shape, mode='valid'):
     the output of :func:`images2neibs <theano.sandbox.neigbours.neibs2images>`
     and reconstructs its input.
 
-    :param neibs: matrix like the one obtained by 
+    :param neibs: matrix like the one obtained by
                   :func:`images2neibs <theano.sandbox.neigbours.neibs2images>`
-    :param neib_shape: `neib_shape` that was used in 
+    :param neib_shape: `neib_shape` that was used in
                   :func:`images2neibs <theano.sandbox.neigbours.neibs2images>`
-    :param original_shape: original shape of the 4d tensor given to 
+    :param original_shape: original shape of the 4d tensor given to
                   :func:`images2neibs <theano.sandbox.neigbours.neibs2images>`
 
-    :return: Reconstructs the input of 
+    :return: Reconstructs the input of
                   :func:`images2neibs <theano.sandbox.neigbours.neibs2images>`,
                   a 4d tensor of shape `original_shape`.
 
     .. note:: Currently, the function doesn't support tensors created with
        `neib_step` different from default value. This means that it may be
-       impossible to compute the gradient of a variable gained by 
-       :func:`images2neibs <theano.sandbox.neigbours.neibs2images>` w.r.t. 
-       its inputs in this case, because it uses 
-       :func:`images2neibs <theano.sandbox.neigbours.neibs2images>` for 
+       impossible to compute the gradient of a variable gained by
+       :func:`images2neibs <theano.sandbox.neigbours.neibs2images>` w.r.t.
+       its inputs in this case, because it uses
+       :func:`images2neibs <theano.sandbox.neigbours.neibs2images>` for
        gradient computation.
-    
 
     Example, which uses a tensor gained in example for
     :func:`images2neibs <theano.sandbox.neigbours.neibs2images>`:

theano/tensor/nnet/sigm.py

@@ -7,7 +7,6 @@ from __future__ import print_function
 import warnings
 
 import numpy
-from six.moves import xrange
 
 import theano
 from theano import config, gof, printing, scalar
@@ -92,7 +91,7 @@ class ScalarSigmoid(scalar.UnaryScalarOp):
         x, = inp
         z, = out
         if (not theano.config.lib.amdlibm or
-            node.inputs[0].dtype != node.outputs[0].dtype):
+                node.inputs[0].dtype != node.outputs[0].dtype):
             raise theano.gof.utils.MethodNotDefined()
         dtype = node.inputs[0].dtype
         if dtype == 'float32' and self.amd_float32 is not None:
@@ -129,9 +128,8 @@ class ScalarSigmoid(scalar.UnaryScalarOp):
         """
         This method was used to generate the graph: sigmoid_prec.png in the doc
         """
-        import matplotlib
         data = numpy.arange(-15, 15, .1)
-        val = 1/(1+numpy.exp(-data))
+        val = 1 / (1 + numpy.exp(-data))
 
         def hard_sigmoid(x):
             return theano.tensor.nnet.hard_sigmoid(x)
@@ -164,10 +162,10 @@ scalar_sigmoid = ScalarSigmoid(scalar.upgrade_to_float, name='scalar_sigmoid')
 sigmoid = elemwise.Elemwise(scalar_sigmoid, name='sigmoid')
 
 sigmoid_inplace = elemwise.Elemwise(
-        ScalarSigmoid(scalar.transfer_type(0)),
-        inplace_pattern={0: 0},
-        name='sigmoid_inplace',
-        )
+    ScalarSigmoid(scalar.transfer_type(0)),
+    inplace_pattern={0: 0},
+    name='sigmoid_inplace',
+)
 
 pprint.assign(sigmoid, printing.FunctionPrinter('sigmoid'))
 
@@ -240,7 +238,7 @@ pprint.assign(ultra_fast_sigmoid,
               printing.FunctionPrinter('ultra_fast_sigmoid'))
 
 
-#@opt.register_uncanonicalize
+# @opt.register_uncanonicalize
 @gof.local_optimizer([sigmoid])
 def local_ultra_fast_sigmoid(node):
     """
@@ -290,7 +288,7 @@ def hard_sigmoid(x):
     return x
 
 
-#@opt.register_uncanonicalize
+# @opt.register_uncanonicalize
 @gof.local_optimizer([sigmoid])
 def local_hard_sigmoid(node):
     if (isinstance(node.op, tensor.Elemwise) and
@@ -412,7 +410,7 @@ def is_1pexp(t):
     """
     if t.owner and t.owner.op == tensor.add:
         scalars, scalar_inputs, nonconsts = \
-                opt.scalarconsts_rest(t.owner.inputs)
+            opt.scalarconsts_rest(t.owner.inputs)
         # scalar_inputs are potentially dimshuffled and fill'd scalars
         if len(nonconsts) == 1:
             maybe_exp = nonconsts[0]
@@ -439,11 +437,12 @@ def is_1pexp(t):
     return None
 
 
-AddConfigVar('warn.identify_1pexp_bug',
-        'Warn if Theano versions prior to 7987b51 (2011-12-18) could have '
-        'yielded a wrong result due to a bug in the is_1pexp function',
-        BoolParam(theano.configdefaults.warn_default('0.4.1')),
-        in_c_key=False)
+AddConfigVar(
+    'warn.identify_1pexp_bug',
+    'Warn if Theano versions prior to 7987b51 (2011-12-18) could have '
+    'yielded a wrong result due to a bug in the is_1pexp function',
+    BoolParam(theano.configdefaults.warn_default('0.4.1')),
+    in_c_key=False)
 
 
 def is_exp(var):
@@ -778,9 +777,9 @@ def perform_sigm_times_exp(tree, exp_x=None, exp_minus_x=None, sigm_x=None,
         rval = False
         for sub_idx, sub_tree in enumerate(inputs):
             rval |= perform_sigm_times_exp(
-                    tree=sub_tree, parent=tree, child_idx=sub_idx,
-                    exp_x=exp_x, exp_minus_x=exp_minus_x, sigm_x=sigm_x,
-                    sigm_minus_x=sigm_minus_x, full_tree=full_tree)
+                tree=sub_tree, parent=tree, child_idx=sub_idx,
+                exp_x=exp_x, exp_minus_x=exp_minus_x, sigm_x=sigm_x,
+                sigm_minus_x=sigm_minus_x, full_tree=full_tree)
         return rval
     else:
         # Reached a leaf: if it is an exponential or a sigmoid, then we
@@ -867,15 +866,15 @@ def local_inv_1_plus_exp(node):
         inv_arg = node.inputs[0]
         if inv_arg.owner and inv_arg.owner.op == tensor.add:
             scalars, scalar_inputs, nonconsts = \
-                    opt.scalarconsts_rest(inv_arg.owner.inputs)
+                opt.scalarconsts_rest(inv_arg.owner.inputs)
             # scalar_inputs are potentially dimshuffled and fill'd scalars
             if len(nonconsts) == 1:
                 if nonconsts[0].owner and nonconsts[0].owner.op == tensor.exp:
                     if scalars and numpy.allclose(numpy.sum(scalars), 1):
                         return opt._fill_chain(
-                                sigmoid(
-                                    tensor.neg(nonconsts[0].owner.inputs[0])),
-                                scalar_inputs)
+                            sigmoid(
+                                tensor.neg(nonconsts[0].owner.inputs[0])),
+                            scalar_inputs)
 
 # Registration is below, and conditional.
 
@@ -892,7 +891,7 @@ def local_1msigmoid(node):
         if sub_r.owner and sub_r.owner.op == sigmoid:
             try:
                 val_l = opt.get_scalar_constant_value(sub_l)
-            except Exception as e:
+            except Exception:
                 return
             if numpy.allclose(numpy.sum(val_l), 1):
                 return [sigmoid(-sub_r.owner.inputs[0])]
@@ -921,7 +920,6 @@ if 0:
         print(sigm_canonicalize(node))
 
     def sigm_canonicalize(node):
-        add = tensor.add
         mul = tensor.mul
         div = tensor.true_div
 

theano/tests/test_flake8.py

@@ -88,15 +88,7 @@ whitelist_flake8 = [
     "tensor/signal/conv.py",
     "tensor/signal/tests/test_conv.py",
     "tensor/signal/tests/test_downsample.py",
-    "tensor/nnet/nnet.py",
-    "tensor/nnet/Conv3D.py",
     "tensor/nnet/__init__.py",
-    "tensor/nnet/ConvTransp3D.py",
-    "tensor/nnet/sigm.py",
-    "tensor/nnet/ConvGrad3D.py",
-    "tensor/nnet/conv3d2d.py",
-    "tensor/nnet/conv.py",
-    "tensor/nnet/neighbours.py",
     "tensor/nnet/tests/test_conv.py",
     "tensor/nnet/tests/test_neighbours.py",
     "tensor/nnet/tests/test_nnet.py",