本文整理汇总了Python中chainer.links.DeconvolutionND方法的典型用法代码示例。如果您正苦于以下问题：Python links.DeconvolutionND方法的具体用法？Python links.DeconvolutionND怎么用？Python links.DeconvolutionND使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类chainer.links的用法示例。
在下文中一共展示了links.DeconvolutionND方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from chainer import links [as 别名]
# 或者: from chainer.links import DeconvolutionND [as 别名]
def __init__(self, nb_inputs, channel_list, ksize_list, no_act_last=False):
super(Decoder, self).__init__()
self.nb_layers = len(channel_list)
self.no_act_last = no_act_last
channel_list = channel_list + [nb_inputs]
for idx, (nb_in, nb_out, ksize) in enumerate(zip(channel_list[:-1], channel_list[1:], ksize_list[::-1])):
self.add_link("deconv{}".format(idx), L.DeconvolutionND(1, nb_in, nb_out, ksize))
if no_act_last and idx == self.nb_layers - 1:
continue
self.add_link("bn{}".format(idx), L.BatchNormalization(nb_out)) 示例2: added
# 需要导入模块: from chainer import links [as 别名]
# 或者: from chainer.links import DeconvolutionND [as 别名]
def added(self, link):
# Define axis and register ``u`` if the weight is initialized.
if not hasattr(link, self.weight_name):
raise ValueError(
'Weight \'{}\' does not exist!'.format(self.weight_name))
if isinstance(link, (L.Deconvolution2D, L.DeconvolutionND)):
self.axis = 1
if getattr(link, self.weight_name).array is not None:
self._prepare_parameters(link) 示例3: __init__
# 需要导入模块: from chainer import links [as 别名]
# 或者: from chainer.links import DeconvolutionND [as 别名]
def __init__(self, in_channels=1, n_classes=4):
init = chainer.initializers.HeNormal(scale=0.01)
super().__init__()
with self.init_scope():
self.conv1a = L.ConvolutionND(
3, in_channels, 32, 3, pad=1, initialW=init)
self.bnorm1a = L.BatchNormalization(32)
self.conv1b = L.ConvolutionND(
3, 32, 32, 3, pad=1, initialW=init)
self.bnorm1b = L.BatchNormalization(32)
self.conv1c = L.ConvolutionND(
3, 32, 64, 3, stride=2, pad=1, initialW=init)
self.voxres2 = VoxResModule()
self.voxres3 = VoxResModule()
self.bnorm3 = L.BatchNormalization(64)
self.conv4 = L.ConvolutionND(
3, 64, 64, 3, stride=2, pad=1, initialW=init)
self.voxres5 = VoxResModule()
self.voxres6 = VoxResModule()
self.bnorm6 = L.BatchNormalization(64)
self.conv7 = L.ConvolutionND(
3, 64, 64, 3, stride=2, pad=1, initialW=init)
self.voxres8 = VoxResModule()
self.voxres9 = VoxResModule()
self.c1deconv = L.DeconvolutionND(
3, 32, 32, 3, pad=1, initialW=init)
self.c1conv = L.ConvolutionND(
3, 32, n_classes, 3, pad=1, initialW=init)
self.c2deconv = L.DeconvolutionND(
3, 64, 64, 4, stride=2, pad=1, initialW=init)
self.c2conv = L.ConvolutionND(
3, 64, n_classes, 3, pad=1, initialW=init)
self.c3deconv = L.DeconvolutionND(
3, 64, 64, 6, stride=4, pad=1, initialW=init)
self.c3conv = L.ConvolutionND(
3, 64, n_classes, 3, pad=1, initialW=init)
self.c4deconv = L.DeconvolutionND(
3, 64, 64, 10, stride=8, pad=1, initialW=init)
self.c4conv = L.ConvolutionND(
3, 64, n_classes, 3, pad=1, initialW=init) 示例4: __init__
# 需要导入模块: from chainer import links [as 别名]
# 或者: from chainer.links import DeconvolutionND [as 别名]
def __init__(self, n_frames=16, z_slow_dim=256, z_fast_dim=256, wscale=0.01):
super(FrameSeedGeneratorInitUniform, self).__init__()
w = chainer.initializers.Uniform(wscale)
with self.init_scope():
self.dc0 = L.DeconvolutionND(1, z_slow_dim, 512, 1, 1, 0, initialW=w)
self.dc1 = L.DeconvolutionND(1, 512, 256, 4, 2, 1, initialW=w)
self.dc2 = L.DeconvolutionND(1, 256, 128, 4, 2, 1, initialW=w)
self.dc3 = L.DeconvolutionND(1, 128, 128, 4, 2, 1, initialW=w)
self.dc4 = L.DeconvolutionND(1, 128, z_fast_dim, 4, 2, 1, initialW=w)
self.bn0 = L.BatchNormalization(512)
self.bn1 = L.BatchNormalization(256)
self.bn2 = L.BatchNormalization(128)
self.bn3 = L.BatchNormalization(128)
self.z_slow_dim = z_slow_dim
self.z_fast_dim = z_fast_dim