self.value = initial_value self.mutex = Lock def adjust(self, other): with self.mutex: value = self.value = max(self.value, other) + 1 returnvalue def forward(self): with self.mutex: self.value += 1 returnself.value 算法的实现其实非常简单,就是转发的时候时间戳+1;收到消息后进行校正,...
class Net(Module): def __init__(self, a, b, ...): super(net, self).__init__() self... # parameters self... # layers def forward(self): x = ... x = ... # 数据流 return x net = Net(a, b, ...) net.train() ... optimizer = torch.optim.SGD(net.parameters(), l...
self.relu = nn.ReLU# ReLU激活函数 self.sigmoid = nn.Sigmoid# Sigmoid激活函数 self.to(self.device)# 将模型移动到指定设备 defforward(self, x): x = x.to(self.device) x =self.fc1(x) x =self.relu(x) x =self.dropout(x) x =self.fc2(x) x =self.sigmoid(x) returnx 资料来源:中金...
class State: def __init__(self, x, y, sum): self.x = x self.y = y self.sum = sum def __str__(self): return 'x=%d y=%d sum=%d'%(self.x, self.y, self.sum) class Solution(object): min = float('inf') def forward(self, a_list, b_list, prev_state): s1_min, s2...
def forward(self, x: Tensor) -> Dict[str, Tensor]: : 函数参数中的冒号是参数的类型建议符,此处建议输入实参为Tensor类型。 -> 函数后面跟着的箭头是函数返回值的类型建议符,此处建议函数返回值类型为字典,键值类型分别str,Tensor。
class test(nn.Module): def __init__(self,name): super(test,self).__init__() self.name = name def forward(self,x): return str(x)+self.name net = test(&
out_featuresself.dropout = dropoutself.act = actself.weight = Parameter(torch.FloatTensor(in_features, out_features))self.reset_parameters()defreset_parameters(self):torch.nn.init.xavier_uniform_(self.weight)defforward(self,input, adj):input= F.dropout(input, self.dropout, self.training)support...
我现在可以用tkinter模块,和tkMessageBox 分享4赞 novelai吧 『远道 求助求大佬帮帮Python代码: def forward(self, text): if self.use_tknz_fn: def init (self, version="E:\AI\gitatt", device="cuda", max_length=77, else: tokens = text z= self.transformer(tokens, return embeddings=True) ...
self.input_channel = input_channeldef init_spkembed(self, units, f0, volume, spk_id = None, spk_mix_dict = None, aug_shift = None, @@ -124,6 +133,12 @@ def forward(self, units, f0, volume, spk_id = None, spk_mix_dict = None, aug_shi ...
act = F.relu def forward(self, x): x = self.act(self.conv1(x)) x = self.act(self.conv2(x)) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.act(self.fc1(x)) x = self.dropout2(x) x = self.fc2(x) output ...