Module Import alias Import alias ImportFrom alias ImportFrom alias ClassDef bases AttributeLoad NameLoad attr body Expr Str FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults Num Num Num Num NameLoad NameLoad NameLoad NameLoad NameLoad body Assign TupleStore NameStore NameStore Call NameLoad NameLoad Assign AttributeStore NameLoad attr Call NameLoad NameLoad Assign AttributeStore NameLoad attr Call NameLoad NameLoad Assign AttributeStore NameLoad attr IfExp CompareIs NameLoad NameLoad TupleLoad NameLoad NameLoad NameLoad Assign AttributeStore NameLoad attr NameLoad Assign NameStore TupleLoad NameLoad NameLoad NameLoad NameLoad Expr Call AttributeLoad Call NameLoad NameLoad NameLoad attr keyword NameLoad If CompareIsNot NameLoad NameLoad body Assert CompareEq AttributeLoad NameLoad attr TupleLoad NameLoad NameLoad NameLoad NameLoad Assign SubscriptStore AttributeLoad AttributeLoad NameLoad attr attr Ellipsis NameLoad orelse Assign AttributeStore AttributeLoad NameLoad attr attr Call AttributeLoad Call AttributeLoad AttributeLoad NameLoad attr attr Num BinOpMult NameLoad Call AttributeLoad NameLoad attr BinOpDiv Num BinOpMult BinOpMult NameLoad NameLoad NameLoad TupleLoad NameLoad NameLoad NameLoad NameLoad attr AttributeLoad NameLoad attr If CompareIsNot NameLoad NameLoad body Assert CompareEq AttributeLoad NameLoad attr TupleLoad NameLoad Expr Call AttributeLoad NameLoad attr Str NameLoad Assign SubscriptStore AttributeLoad AttributeLoad NameLoad attr attr Ellipsis NameLoad orelse If UnaryOpNot NameLoad body Expr Call AttributeLoad NameLoad attr Str NameLoad Assign AttributeStore AttributeLoad NameLoad attr attr Call AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr NameLoad NameLoad orelse Assign AttributeStore NameLoad attr NameLoad decorator_list FunctionDef arguments args NameParam NameParam defaults body Return Call AttributeLoad NameLoad attr NameLoad AttributeLoad NameLoad attr AttributeLoad NameLoad attr AttributeLoad NameLoad attr AttributeLoad NameLoad attr AttributeLoad NameLoad attr AttributeLoad NameLoad attr decorator_list decorator_list FunctionDef arguments args NameParam defaults body If Call NameLoad NameLoad Str body Return NameLoad Return TupleLoad NameLoad NameLoad decorator_list
N/A N/A math N/A numpy chainer.functions.connection deconvolution_2d chainer link Deconvolution2D N/A N/A link Link N/A N/A Two dimensional deconvolution function.

    This link wraps the :func:`~chainer.functions.deconvolution_2d` function
    and holds the filter weight and bias vector as parameters.

    Args:
        in_channels (int): Number of channels of input arrays.
        out_channels (int): Number of channels of output arrays.
        ksize (int or pair of ints): Size of filters (a.k.a. kernels).
            ``ksize=k`` and ``ksize=(k, k)`` are equivalent.
        stride (int or pair of ints): Stride of filter applications.
            ``stride=s`` and ``stride=(s, s)`` are equivalent.
        pad (int or pair of ints): Spatial padding width for input arrays.
            ``pad=p`` and ``pad=(p, p)`` are equivalent.
        wscale (float): Scaling factor of the initial weight.
        bias (float): Initial bias value.
        nobias (bool): If ``True``, then this function does not use the bias
            term.
        outsize (tuple): Expected output size of deconvolutional operation.
            It should be pair of height and width :math:`(out_H, out_W)`.
            Default value is ``None`` and the outsize is estimated by
            input size, stride and pad.
        use_cudnn (bool): If ``True``, then this function uses cuDNN if
            available.
        initialW (4-D array): Initial weight value. If ``None``, then this
            function uses to initialize ``wscale``.
        initial_bias (1-D array): Initial bias value. If ``None``, then this
            function uses to initialize ``bias``.

    The filter weight has four dimensions :math:`(c_I, c_O, k_H, k_W)`
    which indicate the number of the number of input channels, output channels,
    height and width of the kernels, respectively.
    The filter weight is initialized with i.i.d. Gaussian random samples, each
    of which has zero mean and deviation :math:`\sqrt{1/(c_I k_H k_W)}` by
    default. The deviation is scaled by ``wscale`` if specified.

    The bias vector is of size :math:`c_O`.
    Its elements are initialized by ``bias`` argument.
    If ``nobias`` argument is set to True, then this function does not hold
    the bias parameter.

    .. seealso::
       See :func:`chainer.functions.deconvolution_2d` for the definition of
       two-dimensional convolution.
     __init__ N/A N/A self in_channels out_channels ksize stride pad wscale bias nobias outsize use_cudnn initialW initial_bias N/A 1 0 1 0 False None True None None N/A N/A N/A kh kw N/A _pair ksize N/A N/A self stride N/A _pair stride N/A N/A self pad N/A _pair pad N/A N/A self outsize N/A N/A outsize None N/A None None outsize N/A N/A self use_cudnn use_cudnn N/A W_shape N/A in_channels out_channels kh kw N/A N/A N/A N/A super Deconvolution2D self __init__ W W_shape N/A N/A initialW None N/A N/A N/A N/A initialW shape N/A in_channels out_channels kh kw N/A N/A N/A N/A self W data N/A initialW N/A N/A N/A N/A self W data N/A N/A N/A N/A N/A numpy random normal 0 N/A wscale N/A N/A math sqrt N/A 1.0 N/A N/A kh kw in_channels N/A in_channels out_channels kh kw astype N/A numpy float32 N/A N/A initial_bias None N/A N/A N/A N/A initial_bias shape N/A out_channels N/A N/A N/A self add_param b out_channels N/A N/A N/A N/A self b data N/A initial_bias N/A N/A N/A nobias N/A N/A N/A N/A self add_param b out_channels N/A N/A N/A self b data N/A N/A numpy repeat N/A N/A numpy float32 bias out_channels N/A N/A N/A self b None N/A __call__ N/A N/A self x N/A N/A N/A N/A N/A deconvolution_2d deconvolution_2d x N/A self W N/A self b N/A self stride N/A self pad N/A self outsize N/A self use_cudnn N/A N/A _pair N/A N/A x N/A N/A N/A N/A hasattr x __getitem__ N/A N/A x N/A N/A x x N/A