一个有序的容器,神经网络模块(module)将按照在传入构造器时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(OrderedDict)也可以作为传入参数。 #Example of using Sequentialmodel =nn.Sequential( nn.Conv2d(1,20,5), nn.ReLU(), nn.Conv2d(20,64,5), nn.
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([ ('conv1', nn.Conv2d(1,20,5)), ('relu1', nn.ReLU()), ('conv2', nn.Conv2d(20,64,5)), ('relu2...
# 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...
ReLU() ) print(model1) # Sequential( # (0): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1)) # (1): ReLU() # (2): Conv2d(20, 64, kernel_size=(5, 5), stride=(1, 1)) # (3): ReLU() # ) # Example of using Sequential with OrderedDict import collections model2 ...
Example:: # Using Sequential to create a small model. When `model` is run, # input will first be passed to `Conv2d(1,20,5)`. The output of # `Conv2d(1,20,5)` will be used as the input to the first # `ReLU`; the output of the first `ReLU` will become the input ...
(n_hidden x n_hidden) with dropout option else: dnn_layers.append(nn.ReLU()) dnn_layers.append(nn.Linear(nhid, nhid)) if dropout: dnn_layers.append(nn.Dropout(p=dropout)) # compile DNN layers self.dnn = nn.Sequential(*dnn_layers) def forward(self, x): ...
## FX GRAPH from torch.quantization import quantize_fx m.eval() qconfig_dict = {"": torch.quantization.get_default_qconfig(backend)} m=nn.Sequential( nn.Conv2d(2,64,3), nn.ReLU(), nn.Conv2d(64, 128, 3), nn.ReLU() ) model_to_quantize = m # Prepare model_prepared = quantize_...
=planes*block.expansion:downsample=nn.Sequential(nn.Conv2d(self.inplanes,planes*block.expansion,kernel_size=1,stride=stride,bias=False),nn.BatchNorm2d(planes*block.expansion),)layers=[]layers.append(block(self.inplanes,planes,stride,downsample))self.inplanes=planes*block.expansionforiinrange(1,...
value import GAE env = GymEnv("Pendulum-v1") model = TensorDictModule( nn.Sequential( nn.Linear(3, 128), nn.Tanh(), nn.Linear(128, 128), nn.Tanh(), nn.Linear(128, 128), nn.Tanh(), nn.Linear(128, 2), NormalParamExtractor() ), in_keys=["observation"], out_keys=["loc",...
dnn = nn.Sequential(*dnn_layers) def forward(self, x): # Initialize hidden state hidden_state = torch.zeros(self.n_lstm_layers, x.shape[0], self.nhid) cell_state = torch.zeros(self.n_lstm_layers, x.shape[0], self.nhid) # move hidden state to device if self.use_cuda: hidden...