我们从Python开源项目中，提取了以下9个代码示例，用于说明如何使用chainer.links.ConvolutionND()。
def __init__(self, in_size, out_size, kernel_size=2, attention=False, decoder=False): if kernel_size == 1: super(QRNNLayer, self).__init__(W=Linear(in_size, 3 * out_size)) elif kernel_size == 2: super(QRNNLayer, self).__init__(W=Linear(in_size, 3 * out_size, nobias=True), V=Linear(in_size, 3 * out_size)) else: super(QRNNLayer, self).__init__( conv=L.ConvolutionND(1, in_size, 3 * out_size, kernel_size, stride=1, pad=kernel_size - 1)) if attention: self.add_link('U', Linear(out_size, 3 * in_size)) self.add_link('o', Linear(2 * out_size, out_size)) self.in_size, self.size, self.attention = in_size, out_size, attention self.kernel_size = kernel_size
def __init__(self, in_size, out_size, kernel_size=2, attention=False, decoder=False): if kernel_size == 1: super().__init__(W=Linear(in_size, 3 * out_size)) elif kernel_size == 2: super().__init__(W=Linear(in_size, 3 * out_size, nobias=True), V=Linear(in_size, 3 * out_size)) else: super().__init__( conv=L.ConvolutionND(1, in_size, 3 * out_size, kernel_size, stride=1, pad=kernel_size - 1)) if attention: self.add_link('U', Linear(out_size, 3 * in_size)) self.add_link('o', Linear(2 * out_size, out_size)) self.in_size, self.size, self.attention = in_size, out_size, attention self.kernel_size = kernel_size
def __init__(self): super(VoxelChain, self).__init__( conv1 = L.ConvolutionND(3, 1, 20, 5), # 1 input, 20 outputs, filter size 5 pixels conv2 = L.ConvolutionND(3, 20, 20, 5), # 20 inputs, 20 outputs, filter size 5 pixels fc3=L.Linear(2500, 1300), fc4=L.Linear(1300, 10), ) self.train = True
def __init__(self): init = chainer.initializers.HeNormal(scale=0.01) super(VoxResModule, self).__init__( bnorm1=L.BatchNormalization(size=64), conv1=L.ConvolutionND(3, 64, 64, 3, pad=1, initialW=init), bnorm2=L.BatchNormalization(size=64), conv2=L.ConvolutionND(3, 64, 64, 3, pad=1, initialW=init))
def __init__(self, in_channels=1, n_classes=4): init = chainer.initializers.HeNormal(scale=0.01) super(VoxResNet, self).__init__( conv1a=L.ConvolutionND(3, in_channels, 32, 3, pad=1, initialW=init), bnorm1a=L.BatchNormalization(32), conv1b=L.ConvolutionND(3, 32, 32, 3, pad=1, initialW=init), bnorm1b=L.BatchNormalization(32), conv1c=L.ConvolutionND(3, 32, 64, 3, stride=2, pad=1, initialW=init), voxres2=VoxResModule(), voxres3=VoxResModule(), bnorm3=L.BatchNormalization(64), conv4=L.ConvolutionND(3, 64, 64, 3, stride=2, pad=1, initialW=init), voxres5=VoxResModule(), voxres6=VoxResModule(), bnorm6=L.BatchNormalization(64), conv7=L.ConvolutionND(3, 64, 64, 3, stride=2, pad=1, initialW=init), voxres8=VoxResModule(), voxres9=VoxResModule(), c1deconv=L.DeconvolutionND(3, 32, 32, 3, pad=1, initialW=init), c1conv=L.ConvolutionND(3, 32, n_classes, 3, pad=1, initialW=init), c2deconv=L.DeconvolutionND(3, 64, 64, 4, stride=2, pad=1, initialW=init), c2conv=L.ConvolutionND(3, 64, n_classes, 3, pad=1, initialW=init), c3deconv=L.DeconvolutionND(3, 64, 64, 6, stride=4, pad=1, initialW=init), c3conv=L.ConvolutionND(3, 64, n_classes, 3, pad=1, initialW=init), c4deconv=L.DeconvolutionND(3, 64, 64, 10, stride=8, pad=1, initialW=init), c4conv=L.ConvolutionND(3, 64, n_classes, 3, pad=1, initialW=init) )
def Convolution1D(in_channels, out_channels, ksize, stride=1, pad=0, initialW=None, weightnorm=False): if weightnorm: return WeightnormConvolution1D(in_channels, out_channels, ksize, stride=stride, pad=pad, initialV=initialW) return ConvolutionND(1, in_channels, out_channels, ksize, stride=stride, pad=pad, initialW=initialW)
def __init__(self, n_classes): super(MiddleCNN, self).__init__() with self.init_scope(): self.conv1 = L.ConvolutionND(2, 3, 16, 3, pad=1, initialW=init()) self.bnorm1 = L.BatchNormalization(16) self.conv2 = L.ConvolutionND(2, 16, 16, 3, pad=1, initialW=init()) self.bnorm2 = L.BatchNormalization(16) self.conv3 = L.ConvolutionND(2, 16, 32, 3, pad=1, initialW=init()) self.bnorm3 = L.BatchNormalization(32) self.conv4 = L.ConvolutionND(2, 32, 32, 3, pad=1, initialW=init()) self.bnorm4 = L.BatchNormalization(32) self.fc = L.Linear(None, n_classes)
def __init__(self, n_classes): super(SimpleCNN, self).__init__() with self.init_scope(): self.conv1 = L.ConvolutionND(2, 3, 32, 3, pad=1, initialW=init()) self.bnorm1 = L.BatchNormalization(32) self.conv2 = L.ConvolutionND(2, 32, 64, 3, pad=1, initialW=init()) self.bnorm2 = L.BatchNormalization(64) self.fc = L.Linear(None, n_classes)
