Module ImportFrom 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 defaults Num Num Num AttributeLoad NameLoad attr NameLoad body Assert CompareGt Call NameLoad NameLoad Num Assign NameStore ListLoad Call AttributeLoad NameLoad attr NameLoad SubscriptLoad NameLoad Index Num NameLoad NameLoad NameLoad keyword NameLoad keyword NameLoad For TupleStore NameStore NameStore Call NameLoad NameLoad SubscriptLoad NameLoad Slice Num body Expr Call AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr NameLoad NameLoad Num keyword NameLoad keyword NameLoad Expr Call AttributeLoad Call NameLoad NameLoad NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad decorator_list FunctionDef arguments args NameParam NameParam defaults body Expr Str Assign NameStore AttributeLoad NameLoad attr For NameStore SubscriptLoad NameLoad Slice Num body Assign NameStore Call NameLoad Call NameLoad NameLoad Return Call SubscriptLoad NameLoad Index Num NameLoad decorator_list decorator_list
N/A chainer.functions.activation relu chainer link chainer.links.connection convolution_2d MLPConvolution2D N/A N/A link ChainList N/A N/A Two-dimensional MLP convolution layer of Network in Network.

    This is an "mlpconv" layer from the Network in Network paper. This layer
    is a two-dimensional convolution layer followed by 1x1 convolution layers
    and interleaved activation functions.

    Note that it does not apply the activation function to the output of the
    last 1x1 convolution layer.

    Args:
        in_channels (int): Number of channels of input arrays.
        out_channels (tuple of ints): Tuple of number of channels. The i-th
            integer indicates the number of filters of the i-th convolution.
        ksize (int or pair of ints): Size of filters (a.k.a. kernels) of the
            first convolution layer. ``ksize=k`` and ``ksize=(k, k)`` are
            equivalent.
        stride (int or pair of ints): Stride of filter applications at the
            first convolution layer. ``stride=s`` and ``stride=(s, s)`` are
            equivalent.
        pad (int or pair of ints): Spatial padding width for input arrays at
            the first convolution layer. ``pad=p`` and ``pad=(p, p)`` are
            equivalent.
        activation (function): Activation function for internal hidden units.
            Note that this function is not applied to the output of this link.
        use_cudnn (bool): If ``True``, then this link uses cuDNN if available.

    See: `Network in Network <http://arxiv.org/abs/1312.4400v3>`.

    Attributes:
        activation (function): Activation function.

     __init__ N/A N/A self in_channels out_channels ksize stride pad wscale activation use_cudnn N/A 1 0 1 N/A relu relu True N/A N/A N/A N/A len out_channels 0 N/A convs N/A N/A N/A convolution_2d Convolution2D in_channels N/A out_channels N/A 0 ksize stride pad wscale wscale use_cudnn use_cudnn N/A N/A n_in n_out N/A zip out_channels N/A out_channels N/A 1 N/A N/A N/A N/A convs append N/A N/A convolution_2d Convolution2D n_in n_out 1 wscale wscale use_cudnn use_cudnn N/A N/A N/A N/A super MLPConvolution2D self __init__ convs N/A N/A self activation activation N/A __call__ N/A N/A self x N/A N/A N/A Computes the output of the mlpconv layer.

        Args:
            x (~chainer.Variable): Input image.

        Returns:
            ~chainer.Variable: Output of the mlpconv layer.

         N/A f N/A self activation N/A l N/A self N/A -1 N/A N/A x N/A f N/A l x N/A N/A N/A self N/A -1 x N/A N/A