参数量(Parameters) 参数量即模型中需要学习的参数的数量,通常用来衡量模型的复杂度。在 PyTorch 中,我们可以通过model.parameters()来获取模型的参数,并统计参数的数量。 下面是一个示例代码,演示如何统计模型的参数量: importtorchimporttorch.nnasnnclassSimpleModel(nn.Module):def__init__(self):super(SimpleMode...
sum() # calculate the gradient of batch sample loss l.backward() # using small batch random gradient descent to iter model parameters sgd([w, b], lr, batch_size) # reset parameter gradient w.grad.data.zero_() b.grad.data.zero_() train_l = loss(net(features, w, b), labels) ...
init.xavier_uniform_(self.layer2[0].weight,gain= nn.init.calculate_gain('conv2d')) self.layer3 = nn.Sequential( nn.Conv2d(in_channels= 20, out_channels= 30, kernel_size= 3, stride= 1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2) ) init.xavier_uniform_(self...
# Configuration flags and hyperparametersUSE_MAMBA = 1DIFFERENT_H_STATES_RECURRENT_UPDATE_MECHANISM = 0 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') 定义超参数和初始化 d_model = 8state_size = 128 # Example stat...
pretrained = Falsemodel_path = "ST_GNN_Model.pth"if pretrained:model.load_state_dict(torch.load(model_path))optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=1e-7)criterion = torch.nn.MSELoss()model.to(...
#torch_model.parameters传递模型中所有的参数,也可以只有选择想传递的参数 # optimizer = torch.optim.Adam(torch_model.parameters()) #Adam优化器 optimizer.zero_grad() #先把优化器归零 #pytorch的反向传播操作 torch_loss.backward() #完成梯度计算 ...
importnumpyasnp# model是我们在pytorch定义的神经网络层# model.parameters()取出这个model所有的权重参数para =sum([np.prod(list(p.size()))forpinmodel.parameters()]) 假设我们有这样一个model: Sequential( (conv_1):Conv2d(3,64, kernel_size=(3,3), stride=(1,1), padding=(1,1)) ...
learning_rate = 1e-2 num_epochs = 20 criterion = nn.CrossEntropyLoss() model.to(device) # It seems that SGD optimizer is better than Adam optimizer for ResNet18 training on CIFAR10. optimizer = optim.SGD( model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=1e-5 ) # ...
同样的model.conv1是nn.Conv2d同样继承了Module,conv1除了自己的方法和属性外,同样具有8个属性和那些方法,我们可以使用model.conv1.weight,model.fc1.weight,model.conv1.in_channels,model.fc1.in_features, model.conv1._modules,model.conv1.modules(),model.parameters()等,可以nn.init.kaiming_normal_(mode...
normal.Normal(0,1) inputs = [normal.sample((C, L)), normal.sample((C, L))] for qscheme in [torch.per_tensor_affine, torch.per_tensor_symmetric]: obs = MovingAverageMinMaxObserver(qscheme=qscheme) for x in inputs: obs(x) print(f"Qscheme: {qscheme} | {obs.calculate_qparams()...