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...
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 ...
seq(x) net_seq = net_seq() print(net_seq) #net_seq( # (seq): 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() # ) #) nn.Sequential中可以使用...
pytorch nn.Sequential()动态添加方法 之前我们使用nn.Sequential()都是直接写死的,就如下所示: # Example ofusingSequential model=nn.Sequential( nn.Conv2d(1,20,5), nn.ReLU(), nn.Conv2d(20,64,5), nn.ReLU() ) # Example ofusingSequential with OrderedDict model=nn.Sequential(OrderedDict([ ('c...
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 ...
self.block=nn.Sequential(*[Block(embed_size, num_head) for_inrange(num_layers)]) self.layer_norm=nn.LayerNorm(embed_size) defforward(self, idx,targets=None): B,T=idx.shape # idx和targets都是(B,T)整数张量logits=self.token_embedding_table(idx) # (B,T,C)ps=self.possitional_embeddin...
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...
model = models.Sequential([ layers.Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)), layers.MaxPooling2D((2,2)), layers.Conv2D(64, (3,3), activation='relu'), layers.MaxPooling2D((2,2)), layers.Conv2D(64, (3,3), activation='relu'), ...
class Model(nn.Module): def __init__(self): super(Model, self).__init__() self.model1 = Sequential( Conv2d(3, 32, 5, padding=2), MaxPool2d(2), Conv2d(32, 32, 5, padding=2), MaxPool2d(2), Conv2d(32, 64, 5, padding=2), ...
## 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_...