class VAE(torch.nn.Module): def __init__(self, input_size, hidden_sizes, batch_size): super(VAE, self).__init__() self.input_size = input_size self.hidden_sizes = hidden_sizes self.batch_size = batch_size self.fc = torch.nn.Linear(input_size, hidden_sizes[0]) self.BN = torch.nn.BatchNorm1d(hidden_sizes[0]) self.fc1 = torch.nn.Linear(hidden_sizes[0], hidden_sizes[1]) self.BN1 = torch.nn.BatchNorm1d(hidden_sizes[1]) self.fc2 = torch.nn.Linear(hidden_sizes[1], hidden_sizes[2]) self.BN2 = torch.nn.BatchNorm1d(hidden_sizes[2]) self.fc3_mu = torch.nn.Linear(hidden_sizes[2], hidden_sizes[3]) self.fc3_sig = torch.nn.Linear(hidden_sizes[2], hidden_sizes[3]) self.fc4 = torch.nn.Linear(hidden_sizes[3], hidden_sizes[2]) self.BN4 = torch.nn.BatchNorm1d(hidden_sizes[2]) self.fc5 = torch.nn.Linear(hidden_sizes[2], hidden_sizes[1]) self.BN5 = torch.nn.BatchNorm1d(hidden_sizes[1]) self.fc6 = torch.nn.Linear(hidden_sizes[1], hidden_sizes[0]) self.BN6 = torch.nn.BatchNorm1d(hidden_sizes[0]) self.fc7 = torch.nn.Linear(hidden_sizes[0], input_size)def sample_z(self, x_size, mu, log_var): eps = torch.randn(x_size, self.hidden_sizes[-1]) return(mu + torch.exp(log_var/2) * eps) def forward(self, x): ########### # Encoder # ########### out1 = self.fc(x) out1 = nn.relu(self.BN(out1)) out2 = self.fc1(out1) out2 = nn.relu(self.BN1(out2)) out3 = self.fc2(out2) out3 = nn.relu(self.BN2(out3)) mu = self.fc3_mu(out3) sig = nn.softplus(self.fc3_sig(out3)) ########### # Decoder # ########### # sample from the distro sample = self.sample_z(x.size(0), mu, sig) out4 = self.fc4(sample) out4 = nn.relu(self.BN4(out4)) out5 = self.fc5(out4) out5 = nn.relu(self.BN5(out5)) out6 = self.fc6(out5) out6 = nn.relu(self.BN6(out6)) out7 = nn.sigmoid(self.fc7(out6)) return(out7, mu, sig)vae = VAE(input_size, hidden_sizes, batch_size)vae.eval()x_sample, z_mu, z_var = vae(X)错误是：
File "VAE_LongTensor.py", line 200, in <module> x_sample, z_mu, z_var = vae(X) ValueError: expected 2D or 3D input (got 1D input)
回答：
当你在PyTorch中构建一个nn.Module来处理1D信号时，PyTorch实际上期望输入是2D的：第一个维度是“迷你批次”维度。因此，你需要为你的X添加一个单例维度：
x_sample, z_mu, z_var = vae(X[None, ...])