nn.Parameter(torch.zeros(self.output_dim, 1))
时间: 2023-11-12 17:19:36 浏览: 32
这是一个技术问题,它似乎与网络参数有关。在PyTorch中,nn.Parameter(torch.zeros(self.output_dim, 1))被用来创建一个存储在计算图中的可训练参数(trainable parameter)。这个参数是一个output_dim x 1的张量,所有元素都被初始化为0。
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class NormedLinear(nn.Module): def __init__(self, feat_dim, num_classes): super().__init__() self.weight = nn.Parameter(torch.Tensor(feat_dim, num_classes)) self.weight.data.uniform_(-1, 1).renorm_(2, 1, 1e-5).mul_(1e5) def forward(self, x): return F.normalize(x, dim=1).mm(F.normalize(self.weight, dim=0)) class LearnableWeightScalingLinear(nn.Module): def __init__(self, feat_dim, num_classes, use_norm=False): super().__init__() self.classifier = NormedLinear(feat_dim, num_classes) if use_norm else nn.Linear(feat_dim, num_classes) self.learned_norm = nn.Parameter(torch.ones(1, num_classes)) def forward(self, x): return self.classifier(x) * self.learned_norm class DisAlignLinear(nn.Module): def __init__(self, feat_dim, num_classes, use_norm=False): super().__init__() self.classifier = NormedLinear(feat_dim, num_classes) if use_norm else nn.Linear(feat_dim, num_classes) self.learned_magnitude = nn.Parameter(torch.ones(1, num_classes)) self.learned_margin = nn.Parameter(torch.zeros(1, num_classes)) self.confidence_layer = nn.Linear(feat_dim, 1) torch.nn.init.constant_(self.confidence_layer.weight, 0.1) def forward(self, x): output = self.classifier(x) confidence = self.confidence_layer(x).sigmoid() return (1 + confidence * self.learned_magnitude) * output + confidence * self.learned_margin class MLP_ConClassfier(nn.Module): def __init__(self): super(MLP_ConClassfier, self).__init__() self.num_inputs, self.num_hiddens_1, self.num_hiddens_2, self.num_hiddens_3, self.num_outputs \ = 41, 512, 128, 32, 5 self.num_proj_hidden = 32 self.mlp_conclassfier = nn.Sequential( nn.Linear(self.num_inputs, self.num_hiddens_1), nn.ReLU(), nn.Linear(self.num_hiddens_1, self.num_hiddens_2), nn.ReLU(), nn.Linear(self.num_hiddens_2, self.num_hiddens_3), ) self.fc1 = torch.nn.Linear(self.num_hiddens_3, self.num_proj_hidden) self.fc2 = torch.nn.Linear(self.num_proj_hidden, self.num_hiddens_3) self.linearclassfier = nn.Linear(self.num_hiddens_3, self.num_outputs) self.NormedLinearclassfier = NormedLinear(feat_dim=self.num_hiddens_3, num_classes=self.num_outputs) self.DisAlignLinearclassfier = DisAlignLinear(feat_dim=self.num_hiddens_3, num_classes=self.num_outputs, use_norm=True) self.LearnableWeightScalingLinearclassfier = LearnableWeightScalingLinear(feat_dim=self.num_hiddens_3, num_classes=self.num_outputs, use_norm=True)
这段代码定义了一个名为MLP_ConClassfier的神经网络模型,它包含了多个子模块,包括三个不同的分类器:NormedLinearclassfier、DisAlignLinearclassfier和LearnableWeightScalingLinearclassfier。这些分类器都是基于输入特征进行分类的,并且使用不同的方法来实现分类功能。此外,该模型还包含了一个MLP网络,用于将输入特征映射到更高维的特征空间中。该模型的输入特征维度为41,输出类别数为5。
请详细解释以下代码:class BandedFourierLayer(nn.Module): def __init__(self, in_channels, out_channels, band, num_bands, length=201): super().__init__() self.length = length self.total_freqs = (self.length // 2) + 1 self.in_channels = in_channels self.out_channels = out_channels self.band = band # zero indexed self.num_bands = num_bands self.num_freqs = self.total_freqs // self.num_bands + (self.total_freqs % self.num_bands if self.band == self.num_bands - 1 else 0) self.start = self.band * (self.total_freqs // self.num_bands) self.end = self.start + self.num_freqs # case: from other frequencies self.weight = nn.Parameter(torch.empty((self.num_freqs, in_channels, out_channels), dtype=torch.cfloat)) self.bias = nn.Parameter(torch.empty((self.num_freqs, out_channels), dtype=torch.cfloat)) self.reset_parameters() def forward(self, input): # input - b t d b, t, _ = input.shape input_fft = fft.rfft(input, dim=1) output_fft = torch.zeros(b, t // 2 + 1, self.out_channels, device=input.device, dtype=torch.cfloat) output_fft[:, self.start:self.end] = self._forward(input_fft) return fft.irfft(output_fft, n=input.size(1), dim=1) def _forward(self, input): output = torch.einsum('bti,tio->bto', input[:, self.start:self.end], self.weight) return output + self.bias def reset_parameters(self) -> None: nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5)) fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight) bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0 nn.init.uniform_(self.bias, -bound, bound)
这段代码定义了一个名为BandedFourierLayer的类,继承自PyTorch的nn.Module类,用于实现基于傅里叶变换的卷积神经网络层。具体来说:
1. \_\_init\_\_函数中,初始化了该层的一些参数,包括输入输出通道数、带宽、频带数量和信号长度等。其中,self.num_freqs表示每个频带的频率数量,self.start和self.end表示当前频带在总的频率范围内的起始和结束位置。此外,该层还定义了两个可训练参数:weight和bias,分别对应每个频带的卷积核和偏置。
2. forward函数中,首先对输入信号进行离散傅里叶变换,得到傅里叶系数表示,然后根据当前频带的起始和结束位置,将傅里叶系数分为不同的频带,并将每个频带的系数输入到对应的卷积核中进行卷积操作。最后,将卷积结果进行逆傅里叶变换,得到输出信号。
3. _forward函数中,实现了卷积操作。具体来说,它首先使用torch.einsum函数将输入信号与卷积核进行点积操作,得到卷积结果,然后加上偏置。
4. reset_parameters函数中,初始化了可训练参数weight和bias的值,其中weight使用kaiming_uniform初始化方法,bias使用均匀分布初始化方法。