dnn.py 文件源码

def dnn_gradweight(img, topgrad,
                   kerns_shp,
                   border_mode='valid', subsample=(1, 1),
                   conv_mode='conv'):
    """
    GPU convolution gradient with respect to weight using cuDNN from NVIDIA.

    The memory layout to use is 'bc01', that is 'batch', 'channel',
    'first dim', 'second dim' in that order.

    FIXME parameters doc

    :warning: The cuDNN library only works with GPU that have a compute
      capability of 3.0 or higer.  This means that older GPU will not
      work with this Op.
    """

    img = gpu_contiguous(img)
    topgrad = gpu_contiguous(topgrad)
    kerns_shp = theano.tensor.as_tensor_variable(kerns_shp)
    desc = GpuDnnConvDesc(border_mode=border_mode, subsample=subsample,
                          conv_mode=conv_mode)(img.shape, kerns_shp)
    out = gpu_alloc_empty(*kerns_shp)
    return GpuDnnConvGradW()(img, topgrad, out, desc)