torch.flatten() 损失函数层nn.MSELoss(): nn.functional.mse_loss() 实例:以下两种使用激活函数的方法等价 importtorch.nnasnnimporttorch.nn.functionalasFinput= torch.rand((3,5))# 方法 1:layer = nn.Sigmoid() output1 = layer(input)#
super(FlattenLayer, self).__init__() def forward(self, x): # x shape: (batch, *, *, ...) return x.view(x.shape[0], -1) net = nn.Sequential( nin_block(1, 96, kernel_size=11, stride=4, padding=0), nn.MaxPool2d(kernel_size=3, stride=2), nin_block(96, 256, kernel_...
flatten = nn.Flatten() flat_image = flatten(input_image) print(flat_image.size()) 输出的形状为 torch.Size([3, 784]) nn.Linear模块 线性层利用其存储的权重和偏差对输入应用线性映射 layer1 = nn.Linear(in_features=28*28, out_features=20) # 将28*28维度映射到20 hidden1 = layer1(flat_im...
FlattenLayer(), nn.Linear(num_inputs, num_hiddens1), nn.ReLU(), nn.Dropout(drop_prob1), nn.Linear(num_hiddens1, num_hiddens2), nn.ReLU(), nn.Dropout(drop_prob2), nn.Linear(num_hiddens2, 10) ) for param in net.parameters(): nn.init.normal_(param, mean=0, std=0.01) ...
nn.Sequential是一个有序的模块容器。数据按照定义的顺序通过所有模块。您可以使用顺序容器来组合一个快速网络,例如seq_modules. 代码语言:javascript 代码运行次数:0 运行 AI代码解释 seq_modules = nn.Sequential( flatten, layer1, nn.ReLU(), nn.Linear(20, 10) ) input_image = torch.rand(3,28,28) lo...
class FlattenLayer(nn.Module): # 定义一个tensor形状转换的层 def __init__(self): super(FlattenLayer, self).__init__() def forward(self, x): # x shape: (batch, *, *, ...) return x.view(x.shape[0], -1) mnist_train = torchvision.datasets.FashionMNIST(root='~/Datasets/Fashion...
self.linear2 = nn.Linear(32,1) self.sigmoid = nn.Sigmoid() def forward(self,x): x = self.conv1(x) x = self.pool(x) x = self.conv2(x) x = self.pool(x) x = self.adaptive_pool(x) x = self.flatten(x) x = self.linear1(x) ...
from torch.nn import init import numpy as np 1. 2. 3. 4. 定义数据集: class FlattenLayer(nn.Module): # 定义一个tensor形状转换的层 def __init__(self): super(FlattenLayer, self).__init__() def forward(self, x): # x shape: (batch, *, *, ...) ...
size(0), -1) # flatten layer x = self.fc(x) return x 这个函数会将输入x传递给网络,并返回一个实数向量。这个向量代表了输入的内部表示。最后,我们需要定义整个网络的计算过程: def forward(self, input1, input2): output1 = self.forward_once(input1) output2 = self.forward_once(input2) ...
使用nn.Sequential模型容器 classModel_lay(nn.Module):"""使用sequential构建网络,Sequential()函数的功能是将网络的层组合到一起"""def__init__(self, in_dim, n_hidden_1, n_hidden_2, out_dim):super(Model_lay,self).__init__()self.flatten = nn.Flatten...