from pytorch_lightning import LightningModule from torch_optimizer import TorchOptimizer from skopt.space import Real, Integer # 定义PyTorch Lightning模型结构 class MyModel(LightningModule): def __init__(self, lr, hidden_size): super().__init__() self.lr = lr self.hidden_size = hidden_size...
def constraint(params): return params["hidden_size"] % 32 == 0 and params["lr"] <= 0.01 2、日志与检查点机制 TorchOptimizer集成了PyTorch Lightning的日志记录和检查点功能: trainer_args = { "logger": TensorBoardLogger(save_dir="logs"), "callbacks": [ModelCheckpoint(monitor="val_loss")] ...
fromtorch_optimizerimportTorchOptimizer fromskopt.spaceimportReal, Integer # 定义PyTorch Lightning模型结构 classMyModel(LightningModule): def__init__(self, lr, hidden_size): super().__init__() self.lr=lr self.hidden_size=hidden_size self.layer=torch.nn.Linear(hidden_size, 1) defforward(self...
input_size = 784 #28*28 hidden_sizes = [128, 64] output_size = 10 model = nn.Sequential(nn.Linear(input_size, hidden_sizes[0]), nn.ReLU(), nn.Linear(hidden_sizes[0], hidden_sizes[1]), nn.ReLU(), nn.Linear(hidden_sizes[1], output_size), nn.LogSoftmax(dim=1)) print(mode...
(c_in=1,c_out=1,seq_len=20,hidden_size=[128,128],n_layers=2,bias=True,rnn_dropout=0.2,bidirectional=True,fc_dropout=0.2) else: self.model = InceptionTimePlus(c_in=1,c_out=1,seq_len=20,nf=32,nb_filters=None,fc_dropout=0.2) self.task_layer = tl.Linear(output_dim) self....
classMyModel(LightningModule): def__init__(self, lr, hidden_size): super().__init__() self.lr=lr self.hidden_size=hidden_size self.layer=torch.nn.Linear(hidden_size, 1) defforward(self, x): returnself.layer(x) deftraining_step(self, batch, batch_idx): ...
classColaModel(pl.LightningModule):def__init__(self,model_name="google/bert_uncased_L-2_H-128_A-2",lr=1e-2):super(ColaModel,self).__init__()self.bert=AutoModel.from_pretrained(model_name)self.W=nn.Linear(self.bert.config.hidden_size,2) ...
hidden_size – 用来表示memory num_layers – 默认为1 1. 2. 3. out,(ht,ct) = forward(x, [ht_0, ct_0])其中ht_0, ct_0为最开始ht和ct的状态 X=[seq_len, batch, feature_len] h/c=[number_layers, batch, hidden_len] out=[seq_len, batch, hidden_len] ...
class ColaModel(pl.LightningModule): def __init__(self,model_name="google/bert_uncased_L-2_H-128_A-2",lr=1e-2): super(ColaModel,self).__init__()self.bert=AutoModel.from_pretrained(model_name)self.W=nn.Linear(self.bert.config.hidden_size,2) ...
最后,第三部分提供了一个我总结出来的易用于大型项目、容易迁移、易于复用的模板,有兴趣的可以去GitHub— https://github.com/miracleyoo/pytorch-lightning-template 试用。 02 核心 Pytorch-Lighting 的一大特点是把模型和系统分开来看。模型是像Resnet18, RNN之类的纯模型, ...