self.dropout = nn.Dropout(0.2)# Dropout层 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)...
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。
我们可以在神经网络类中定义一个forward方法来实现前向传播: classNet(nn.Module):def__init__(self):super(Net,self).__init__()self.fc1=nn.Linear(784,512)self.fc2=nn.Linear(512,256)self.fc3=nn.Linear(256,10)defforward(self,x):x=x.view(x.size(0),-1)# 将输入数据展平为一维向量x=...
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("lrs") print(net(12)) 好文要顶 关注我 收藏该文 微信分享 寻找烟花 粉丝- 0 关注- 1 +加关注 0 0 升级成为会员 « ...
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...
class Your_model(nn.Module): def __init__(self): super(Your_model, self).__init__() pass def forward(self,x): pass return x 四、定义早停类(此步骤可以省略) class EarlyStopping(): def __init__(self,patience=7,verbose=False,delta=0): self.patience = patience self.verbose = verbos...
def forward(self, x): return x[:, :, :-self.chomp_size].contiguous() forward方法定义了模块的前向传播逻辑。 输入x是一个三维张量,假设其形状为(batch_size, channels, sequence_length)。 x[:, :, :-self.chomp_size]使用切片操作去除最后一个维度上的最后self.chomp_size个元素。 .contiguous()...
self.dim = dim self.qk_dim = qk_dim self.qkv = nn.Linear(dim, qk_dim + qk_dim + dim, bias=bias)def forward(self, x): q, kv = self.qkv(x).split([self.qk_dim, self.qk_dim+self.dim], dim=-1) return q, kv # q, k, v = self.qkv(x).split([self.qk_dim, ...
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 ...
And, oh yes, his signature line of self-introduction, “Bond, James Bond.” Interestingly, even as the world was moving closer to women’s rights in the 1960s, the film producers made the women in Bond movies seem more reliant on him than they were in the 1950s Fleming novels, more ...