nn.Conv2d(20,64,5), nn.ReLU() )#Example of using Sequential with OrderedDictmodel =nn.Sequential(OrderedDict([ ('conv1', nn.Conv2d(1,20,5)), ('relu1', nn.ReLU()), ('conv2', nn.Conv2d(20,64,5)), ('relu2', nn.ReLU()) ])) 接下来看一下Sequential源码,是如何实现的: htt...
一个有序的容器,神经网络模块将按照在传入构造器的顺序依次被添加到计算图中执行,同时以神经网络模块为元素的有序字典也可以作为传入参数。 直白理解:将网络的各个层组合到一起 #Example of using Sequentialmodel =nn.Sequential( nn.Conv2d(1,20,5), nn.ReLU(), nn.Conv2d(20,64,5), nn.ReLU() )#Exam...
# Example of using Sequentialmodel=nn.Sequential(nn.Conv2d(1,20,5),nn.ReLU(),nn.Conv2d(20,64,5),nn.ReLU())# Example of using Sequential with OrderedDictmodel=nn.Sequential(OrderedDict([('conv1',nn.Conv2d(1,20,5)),('ReLU1',nn.ReLU()),('conv2',nn.Conv2d(20,64,5)),('ReLU...
从Sequential的__init__函数来看,利用Sequential来构建网络时,只接受以下两种形式: # Example of using Sequential model = nn.Sequential( nn.Conv2d(1,20,5), nn.ReLU(), nn.Conv2d(20,64,5), nn.ReLU() ) # Example of using Sequential with OrderedDict model = nn.Sequential(OrderedDict([ ('...
nn.Conv2d(1,20,5), nn.ReLU(), nn.Conv2d(20,64,5), nn.ReLU() ) # Example of using Sequential with OrderedDict model = nn.Sequential(OrderedDict([ ('conv1', nn.Conv2d(1,20,5)), ('relu1', nn.ReLU()), ('conv2', nn.Conv2d(20,64,5)), ('relu2', nn.ReLU()) ])) ...
nn.Sequential一次性创建一系列操作。和tensorflow中的Sequential完全一样。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 mlp_layer=nn.Sequential(nn.Linear(5,2),nn.BatchNorm1d(2),nn.ReLU())test_example=torch.randn(5,5)+1print("input: ")print(test_example)print("output: ")print(mlp_lay...
net = nn.Sequential( nn.Linear(num_inputs, num_hidden) # 传入其他层 ) 1. 2. 3. 4. 使用方法二:作为一个有序字典 将以特定神经网络模块为元素的有序字典(OrderedDict)为参数传入。 官方Example : model = nn.Sequential(OrderedDict([ ('conv1', nn.Conv2d(1,20,5)), ...
nn.Sequential一次性创建一系列操作。和tensorflow中的Sequential完全一样。 复制 mlp_layer = nn.Sequential(nn.Linear(5, 2),nn.BatchNorm1d(2),nn.ReLU())test_example = torch.randn(5,5) + 1print("input: ")print(test_example)print("output: ")print(mlp_layer(test_example))# 输出如下input:...
nn.Sequential allows you to build a neural net by specifying sequentially the building blocks (nn.Module's) of that net. Here's an example:class Flatten(nn.Module):def forward(self, x):N, C, H, W = x.size() # read in N, C, H, W return x.view(N, -1)simple_cnn = nn....
基于pytorch中的Sequential用法说明 class torch.nn.Sequential(* args) 一个时序容器。Modules 会以他们传入的顺序被添加到容器中。当然,也可以传入一个OrderedDict。 为了更容易的理解如何使用Sequential, 下面给出了一个例子: # Example of using Sequential model = nn.Sequential( nn.Conv2d(1,20,5), nn....