def conv1d_mc0(input, filters, image_shape=None, filter_shape=None, border_mode='valid', subsample=(1,), filter_flip=True):
 if (image_shape is None):
  image_shape_mc0 = None
 else:
  image_shape_mc0 = (image_shape[0], image_shape[1], 1, image_shape[2])
 if (filter_shape is None):
  filter_shape_mc0 = None
 else:
  filter_shape_mc0 = (filter_shape[0], filter_shape[1], 1, filter_shape[2])
 if isinstance(border_mode, tuple):
  (border_mode,) = border_mode
 if isinstance(border_mode, int):
  border_mode = (0, border_mode)
 input_mc0 = input.dimshuffle(0, 1, 'x', 2)
 filters_mc0 = filters.dimshuffle(0, 1, 'x', 2)
 conved = T.nnet.conv2d(input_mc0, filters_mc0, image_shape_mc0, filter_shape_mc0, subsample=(1, subsample[0]), border_mode=border_mode, filter_flip=filter_flip)
 return conved[:, :, 0, :]

def conv1d_mc0 ( input , filters , image_shape = None , filter_shape = None , border_mode = 'valid' , subsample = ( 1 , ) , filter_flip = True ) : if ( image_shape is None ) : image_shape_mc0 = None else : image_shape_mc0 = ( image_shape [ 0 ] , image_shape [ 1 ] , 1 , image_shape [ 2 ] ) if ( filter_shape is None ) : filter_shape_mc0 = None else : filter_shape_mc0 = ( filter_shape [ 0 ] , filter_shape [ 1 ] , 1 , filter_shape [ 2 ] ) if isinstance ( border_mode , tuple ) : ( border_mode , ) = border_mode if isinstance ( border_mode , int ) : border_mode = ( 0 , border_mode ) input_mc0 = input . dimshuffle ( 0 , 1 , 'x' , 2 ) filters_mc0 = filters . dimshuffle ( 0 , 1 , 'x' , 2 ) conved = T . nnet . conv2d ( input_mc0 , filters_mc0 , image_shape_mc0 , filter_shape_mc0 , subsample = ( 1 , subsample [ 0 ] ) , border_mode = border_mode , filter_flip = filter_flip ) return conved [ : , : , 0 , : ]
using conv2d with width == 1 .