from sklearn.svm import SVC svc = SVC(C=float('inf'), kernel="linear") svc.fit(X, y) 在这段代码中,'C'参数被设置为float('inf'),即正无穷大,这超出了'C'参数允许的范围。 4. 修改参数 修改后的代码: python from sklearn.svm import SVC svc = SVC(C=1e10, kernel="linear") # ...
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kernel_size=128, padding=128 // 2, groups=16, ) self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2) def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.conv(x.transpose(1, 2)) x = t_func.gelu(x[:, :, :-1]) return x.transpose(1, 2) class ...
return int((kernel_size*dilation - dilation)/2) def load_checkpoint(filepath, device): assert os.path.isfile(filepath) print("Loading '{}'".format(filepath)) checkpoint_dict = torch.load(filepath, map_location=device) print("Complete.") return checkpoint_dict def save_checkpoint(filepath...
+ dropout_input (float): + The dropout probability applied after the input feature is projected + to ``embed_dim``. + This option corresponds to "dropout_input" from fairseq. + Expected values are 0.1 for both Base and Large arch. + pos_conv_kernel (int): + The kernel size of ...
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ones_kernel, stride=self.stride, padding=self.padding, groups=x.size(1), ) # Count the non-masked (valid) elements in each pooling window valid_count = nn.functional.conv1d( mask.float(), ones_kernel, stride=self.stride, padding=self.padding, groups=x.size(1), ) valid_count = valid...
return int((kernel_size*dilation - dilation)/2) def load_checkpoint(filepath, device): assert os.path.isfile(filepath) print("Loading '{}'".format(filepath)) checkpoint_dict = torch.load(filepath, map_location=device) print("Complete.") return checkpoint_dict def save_checkpoint(filepath...