上面的代码定义了一个 initialize_model 函数,它接受模型名称、类别数量、特征提取标志和是否使用预训练模型等参数,返回一个经过初始化的模型和输入大小。在这个函数中,我们使用了 torchvision.models.resnet18 这个预训练模型,并根据参数设置模型的全连接层。 示例 接下来,我们将使用 initialize_model 函数来初始化一个...
1.2.1.1 简单一行代码,包裹model即可 model=DataParallel(model.cuda(),device_ids=[0,1,2,3])data=data.cuda() 1.2.1.2 模型保存与加载 1.2.1.2.1 模型保存 # torch.save 注意模型需要调用 model.module.state_dict()# 例子一torch.save(net.module.state_dict(),PATH)# 例子二net=Net()PATH="entire_...
以下是一种常见的初始化方法: import torch import torch.nn as nn import torch.nn.init as init class MyModel(nn.Module): def __init__(self): super(MyModel, self).__init__() self.linear = nn.Linear(100, 10) def initialize_weights(self): for m in self.modules(): if isinstance(m,...
1#Define model2classTheModelClass(nn.Module):3def__init__(self):4super(TheModelClass, self).__init__()5self.conv1 = nn.Conv2d(3, 8, 5)6self.bn = nn.BatchNorm2d(8)7self.conv2 = nn.Conv2d(8, 16, 5)8self.pool = nn.MaxPool2d(2, 2)9self.fc1 = nn.Linear(16 * 5 *...
[1]learning_rate =0.001n_epochs =100# Initialize the model, loss function, and optimizermodel = SimpleNN(input_dim)criterion = nn.MSELoss()optimizer = optim.Adam(model.parameters(), lr=learning_rate)# Define custom compilerdefmy_com...
model, optimizer = amp.initialize(model, optimizer, opt_level="O1") for to in range(500): y_pred = model(x) loss = torch.nn.functional.mse_loss(y_pred, y) optimizer.zero_grad() with amp.scale_loss(loss, optimizer) as scaled_loss: ...
# Initialize the model, loss function, and optimizer model=SimpleNN(input_dim) criterion=nn.MSELoss() optimizer=optim.Adam(model.parameters(), lr=learning_rate) # Define custom compiler defmy_compiler(gm: torch.fx.GraphModule, example_inputs: List[torch.Tensor]): ...
x = self.fc3(x)returnx# Initialize modelmodel = TheModelClass()# Initialize optimizeroptimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)# Print model's state_dictprint("Model's state_dict:")forparam_tensorinmodel.state_dict():print(param_tensor,"\t", model.state_dict()...
# Initialize bias to a small constant valuenn.init.constant_(self.out_proj.bias, 1.0) self.S6 = S6(seq_len, 2*d_model, state_size, device) # Add 1D convolution with kernel size 3self.conv = nn.Conv1d(seq_len, seq_len, kernel_...
# Initialize w and b,where w is initialized to a normal distribution and b is initialized to0# Automatic differentiation is required,sosetrequires grad to True.w=torch.randn((1),requires_grad=True)b=torch.zeros((1),requires_grad=True)foriterationinrange(1000):# forward propagation ...