核函数kernel_sigmoid = Kernel('type', 'sigm', 'gamma', 0.1, 'offset', 0)中gamma和offset
时间: 2023-12-30 10:06:56 浏览: 31
在核函数kernel_sigmoid = Kernel('type', 'sigm', 'gamma', 0.1, 'offset', 0)中,gamma和offset是Sigmoid核函数的两个参数。
Sigmoid核函数的公式为:K(x, y) = tanh(gamma * x' * y + offset),其中x和y是输入样本的特征向量,gamma和offset是Sigmoid核函数的两个参数。
gamma控制Sigmoid函数的斜率,即控制了核函数映射后的特征空间的形状。当gamma越大时,Sigmoid函数的斜率越陡峭,核函数映射后的特征空间就越接近于原始空间,分类器的拟合能力就越强;当gamma越小时,Sigmoid函数的斜率越平缓,核函数映射后的特征空间就越复杂,分类器的拟合能力就越弱。
offset控制Sigmoid函数的水平位移,即控制了Sigmoid函数的取值范围。当offset越大时,Sigmoid函数的取值范围就越偏向于正值,分类器的预测结果就越偏向于正类;当offset越小时,Sigmoid函数的取值范围就越偏向于负值,分类器的预测结果就越偏向于负类。
需要根据具体的数据集和任务,通过交叉验证等方法来确定Sigmoid核函数的最优参数。
相关问题
如何将self.conv1 = nn.Conv2d(4 * num_filters, num_filters, kernel_size=3, padding=1) self.conv_offset1 = nn.Conv2d(512, 18, kernel_size=3, stride=1, padding=1) init_offset1 = torch.Tensor(np.zeros([18, 512, 3, 3])) self.conv_offset1.weight = torch.nn.Parameter(init_offset1) # 初始化为0 self.conv_mask1 = nn.Conv2d(512, 9, kernel_size=3, stride=1, padding=1) init_mask1 = torch.Tensor(np.zeros([9, 512, 3, 3]) + np.array([0.5])) self.conv_mask1.weight = torch.nn.Parameter(init_mask1) # 初始化为0.5 与torchvision.ops.deform_conv2d,加入到:class NLayerDiscriminator(nn.Module): def init(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True): super(NLayerDiscriminator, self).init() self.use_parallel = use_parallel if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d kw = 4 padw = int(np.ceil((kw-1)/2)) sequence = [ nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True) ] nf_mult = 1 for n in range(1, n_layers): nf_mult_prev = nf_mult nf_mult = min(2n, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = min(2n_layers, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] if use_sigmoid: sequence += [nn.Sigmoid()] self.model = nn.Sequential(*sequence) def forward(self, input): return self.model(input)中,请给出修改后的代码
可以将self.conv1、self.conv_offset1和self.conv_mask1加入到NLayerDiscriminator中的nn.Sequential序列中,如下所示:
```
import torchvision
from torchvision.ops.deform_conv import DeformConv2d
class NLayerDiscriminator(nn.Module):
def __init__(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True):
super(NLayerDiscriminator, self).__init__()
self.use_parallel = use_parallel
if type(norm_layer) == functools.partial:
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
kw = 4
padw = int(np.ceil((kw-1)/2))
nf_mult = 1
sequence = [
nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
nn.LeakyReLU(0.2, True)
]
for n in range(1, n_layers):
nf_mult_prev = nf_mult
nf_mult = min(2**n, 8)
sequence += [
nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True)
]
nf_mult_prev = nf_mult
nf_mult = min(2**n_layers, 8)
sequence += [
DeformConv2d(
ndf * nf_mult_prev, ndf * nf_mult, kernel_size=3, stride=1, padding=1,
dilation=1, groups=1, deformable_groups=1, bias=False
),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True),
nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)
]
if use_sigmoid:
sequence += [nn.Sigmoid()]
self.model = nn.Sequential(*sequence)
self.conv1 = nn.Conv2d(4 * ndf, ndf, kernel_size=3, padding=1)
self.conv_offset1 = nn.Conv2d(ndf, 18, kernel_size=3, stride=1, padding=1)
init_offset1 = torch.Tensor(np.zeros([18, ndf, 3, 3]))
self.conv_offset1.weight = torch.nn.Parameter(init_offset1)
self.conv_mask1 = nn.Conv2d(ndf, 9, kernel_size=3, stride=1, padding=1)
init_mask1 = torch.Tensor(np.zeros([9, ndf, 3, 3]) + np.array([0.5]))
self.conv_mask1.weight = torch.nn.Parameter(init_mask1)
def forward(self, input):
x = input
for module in self.model:
if isinstance(module, DeformConv2d):
offset_mask = torch.cat([self.conv_offset1(x), self.conv_mask1(x)], dim=1)
x = module(x, offset_mask)
else:
x = module(x)
return x
```
这里使用了torchvision.ops.deform_conv2d中的DeformConv2d作为最后一层卷积层,因此需要将self.conv1、self.conv_offset1和self.conv_mask1的通道数修改为ndf。在forward中,对于DeformConv2d层,需要将self.conv_offset1和self.conv_mask1的输出拼接起来作为offset_mask输入。
class NLayerDiscriminator(nn.Module): def init(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True): super(NLayerDiscriminator, self).init() self.use_parallel = use_parallel if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d kw = 4 padw = int(np.ceil((kw - 1) / 2)) sequence = [ nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True) ] nf_mult = 1 for n in range(1, n_layers): nf_mult_prev = nf_mult nf_mult = min(2 ** n, 8) if n == 1: num_filters = ndf * nf_mult self.conv1 = nn.Conv2d(4 * num_filters, num_filters, kernel_size=3, padding=1) self.conv_offset1 = nn.Conv2d(512, 18, kernel_size=3, stride=1, padding=1) init_offset1 = torch.Tensor(np.zeros([18, 512, 3, 3])) self.conv_offset1.weight = torch.nn.Parameter(init_offset1) self.conv_mask1 = nn.Conv2d(512, 9, kernel_size=3, stride=1, padding=1) init_mask1 = torch.Tensor(np.zeros([9, 512, 3, 3]) + np.array([0.5])) self.conv_mask1.weight = torch.nn.Parameter(init_mask1) sequence += [ torchvision.ops.DeformConv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = min(2 ** n_layers, 8) sequence += [ torchvision.ops.DeformConv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True), nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw) ] if use_sigmoid: sequence += [nn.Sigmoid()] self.model = nn.Sequential(*sequence) def forward(self, input): offset1 = self.conv_offset1(input) mask1 = self.conv_mask1(input) input = torch.cat([input, offset1, mask1], dim=1) return self.model(input),运行上述代码出现错误:RuntimeError: Given groups=1, weight of size [18, 512, 3, 3], expected input[1, 3, 512, 512] to have 512 channels, but got 3 channels instead,如何修改,给出代码
根据错误提示,可以发现是因为 `conv_offset1` 的输入通道数为 512,但是 `input` 的通道数为 3。因此需要将 `conv_offset1` 的输入通道数修改为 3。同时,根据代码中的注释,`num_filters` 应该为 `ndf * nf_mult_prev`,因此需要将 `num_filters` 的计算公式修改为 `ndf * nf_mult_prev * 4`。修改后的代码如下:
```
class NLayerDiscriminator(nn.Module):
def __init__(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True):
super(NLayerDiscriminator, self).__init__()
self.use_parallel = use_parallel
if type(norm_layer) == functools.partial:
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
kw = 4
padw = int(np.ceil((kw - 1) / 2))
sequence = [
nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
nn.LeakyReLU(0.2, True)
]
nf_mult = 1
for n in range(1, n_layers):
nf_mult_prev = nf_mult
nf_mult = min(2 ** n, 8)
if n == 1:
num_filters = ndf * nf_mult_prev * 4
self.conv1 = nn.Conv2d(num_filters, num_filters, kernel_size=3, padding=1)
self.conv_offset1 = nn.Conv2d(3, 18, kernel_size=3, stride=1, padding=1)
init_offset1 = torch.Tensor(np.zeros([18, 3, 3, 3]))
self.conv_offset1.weight = torch.nn.Parameter(init_offset1)
self.conv_mask1 = nn.Conv2d(3, 9, kernel_size=3, stride=1, padding=1)
init_mask1 = torch.Tensor(np.zeros([9, 3, 3, 3]) + np.array([0.5]))
self.conv_mask1.weight = torch.nn.Parameter(init_mask1)
sequence += [
torchvision.ops.DeformConv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True)
]
nf_mult_prev = nf_mult
nf_mult = min(2 ** n_layers, 8)
sequence += [
torchvision.ops.DeformConv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True),
nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)
]
if use_sigmoid:
sequence += [nn.Sigmoid()]
self.model = nn.Sequential(*sequence)
def forward(self, input):
offset1 = self.conv_offset1(input)
mask1 = self.conv_mask1(input)
input = torch.cat([input, offset1, mask1], dim=1)
return self.model(input)
```
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