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src/p/y/pylearn2-HEAD/pylearn2/datasets/preprocessing.py   pylearn2(Download)
                         input_space=input_space,
                         border_mode='full')
    convout = transformer.lmul(X)
 
    # For each pixel, remove mean of 9x9 neighborhood
                         input_space=input_space,
                         border_mode='full')
    sum_sqr_XX = transformer.lmul(X**2)
 
    denom = tensor.sqrt(sum_sqr_XX[:, mid:-mid, mid:-mid, :])

src/p/y/pylearn2-HEAD/pylearn2/linear/tests/test_conv2d.py   pylearn2(Download)
        conv2d = Conv2D(self.filters, 1, input_space, output_axes=axes)
        f_axes = theano.function([self.image_tensor],
                                 conv2d.lmul(self.image_tensor))
        f = theano.function([self.image_tensor],
                            self.conv2d.lmul(self.image_tensor))
        conv2d = Conv2D(filters, 1, input_space)
        f = theano.function([self.image_tensor],
                            conv2d.lmul(self.image_tensor))
        assert f(image).shape == (1, 2, 2, 2)