Source code for chainer.links.connection.deconvolution_nd

from chainer.functions.connection import deconvolution_nd
from chainer import initializers
from chainer import link
from chainer.utils import conv_nd

[docs]class DeconvolutionND(link.Link): """N-dimensional deconvolution function. This link wraps :func:`~chainer.functions.deconvolution_nd` function and holds the filter weight and bias vector as its parameters. Args: ndim (int): Number of spatial dimensions. in_channels (int): Number of channels of input arrays. out_channels (int): Number of channels of output arrays. ksize (int or tuple of ints): Size of filters (a.k.a. kernels). ``ksize=k`` and ``ksize=(k, k, ..., k)`` are equivalent. stride (int or tuple of ints): Stride of filter application. ``stride=s`` and ``stride=(s, s, ..., s)`` are equivalent. pad (int or tuple of ints): Spatial padding width for input arrays. ``pad=p`` and ``pad=(p, p, ..., p)`` are equivalent. outsize (tuple of ints): Expected output size of deconvolutional operation. It should be a tuple of ints that represents the output size of each dimension. Default value is ``None`` and the outsize is estimated with input size, stride and pad. initialW: Value used to initialize the filter weight. May be an initializer instance of another value the same with that :func:`~chainer.init_weight` function can take. initial_bias: Value used to initialize the bias vector. May be an initializer instance or another value except ``None`` the same with that :func:`~chainer.init_weight` function can take. If ``None`` is supplied, this link does not use the bias vector. use_cudnn (bool): If ``True``, then this link uses cuDNN if available. .. seealso:: :func:`~chainer.functions.deconvolution_nd` Attributes: W (~chainer.Variable): Weight parameter. b (~chainer.Variable): Bias parameter. If ``initial_bias`` is ``None``, set to ``None``. """ def __init__(self, ndim, in_channels, out_channels, ksize, stride=1, pad=0, outsize=None, initialW=None, initial_bias=0, use_cudnn=True): ksize = conv_nd.as_tuple(ksize, ndim) self.stride = stride self.pad = pad self.use_cudnn = use_cudnn self.outsize = outsize super(DeconvolutionND, self).__init__() W_shape = (in_channels, out_channels) + ksize initialW = initializers._get_initializer(initialW) self.add_param('W', W_shape, initializer=initialW) if initial_bias is None: self.b = None else: initial_bias = initializers._get_initializer(initial_bias) self.add_param('b', out_channels, initializer=initial_bias) def __call__(self, x): return deconvolution_nd.deconvolution_nd( x, self.W, b=self.b, stride=self.stride, pad=self.pad, outsize=self.outsize, use_cudnn=self.use_cudnn)