def initialize_parameters_he(layers_dims): """ Arguments: layer_dims -- python array (list) containing the size of each layer. Returns: parameters -- python dictionary containing your parameters "W1", "b1", ..., "WL", "bL": W1 -- weight matrix of shape (layers_dims[1], layers_di...
parameters(): if isinstance(param, torch.nn.parameter.UninitializedParameter): raise RuntimeError( "Modules with uninitialized parameters can't be used with `DistributedDataParallel`. " "Run a dummy forward pass to correctly initialize the modules" ) # used for intra-node param sync and inter-...
[0], -1) x = self.classifier(x) return x def initialize(self): for p in self.parameters(): p.data.fill_(20191104) ## 建立一个网络 net = LeNet2(classes=2019) # "训练" print("训练前: ", net.features[0].weight[0, ...]) net.initialize() print("训练后: ", net.features[...
· cuda():将所有 parameters 和 buffer 转移到 GPU 上 · float():将所有浮点类型的 parameters 和 buffer 转变成 float32 类型 · double():将所有浮点类型的 parameters 和 buffer 转变成 double 类型 · half():将所有浮点类型的 parameters 和 buffer 转变成 float16 类型 · bfloat16():将所有浮点类...
parameters(), lr=1e-3) model, optimizer = amp.initialize(model, optimizer, opt_level="O2") for img, label in dataloader: out = model(img) loss = LOSS(out, label) # loss.backward() with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() optimizer.step() ...
# Initialize the model model = Mamba(seq_len, d_model, state_size, device).to(device) # Define the loss function and optimizercriterion = nn.CrossEntropyLoss()optimizer = optim.AdamW(model.parameters(), lr=5e-6) # Training loopnum_epoc...
基类中有parameters()、modules()、children()等方法 import torch import torch.nnasnnclassmyModel(nn.Module): def __init__(self, num_classes): super(myModel, self).__init__() self.conv1= nn.Sequential(nn.Conv2d(3,64, kernel_size=3, padding=1), ...
# Initialize parameters with Glorot / fan_avg. for p in model.parameters(): if p.dim() > 1: nn.init.xavier_uniform_(p) return model 6.1.7 实战案例 下面我们用一个人造的玩具级的小任务,来实战体验下Transformer的训练,加深我们的理解,并且验证我们上面所述代码是否work。
(ff), dropout), N), Decoder(DecoderLayer(d_model, c(attn), c(attn), c(ff), dropout), N), backbone, c(position), nn.Sequential(Embeddings(d_model, tgt_vocab), c(position)), Generator(d_model, tgt_vocab)) # 此处的generator并没有在类内调用 # Initialize parameters with Glorot / ...
# Initialize the parameter vector `a`nn.init.xavier_normal_(a) # we obtained `h_transformed` in the previous code snippet # calculating the dot product of all node embeddings# and first half the attention vector parameters (corresponding to nei...