如: python values = {'loss': loss, 'acc': acc, ..., 'metric_n': metric_n} self.log_dict(values) save_hyperparameters:储存init中输入的所有超参。后续访问可以由self.hparams.argX方式进行。同时,超参表也会被存到文件中。 函数内建变量: device:
save_hyperparameters() def prepare_data(self): """ download data once """ MNIST(self.hparams.root_dir, train=True, download=True) MNIST(self.hparams.root_dir, train=False, download=True) def setup(self, stage=None): """ setup dataset for each machine """ dataset = MNIST(self.hparams...
importpytorch_lightningaspl# 示例模型# 全连接层(Linear)classFullyConnected(pl.LightningModule):def__init__(self,hparams=None):# 这里初始化的时候只传入hparamssuper(FullyConnected,self).__init__()save_hyperparameters(hparams)# 这里保存所有超参数self.input_dim=hparams.input_sizeself.hidden_dim=hpara...
save_hyperparameters() self.net = net self.train_acc = Accuracy() self.val_acc = Accuracy() self.test_acc = Accuracy() def forward(self,x): x = self.net(x) return x #定义loss def training_step(self, batch, batch_idx): x, y = batch preds = self(x) loss = nn.CrossEntropy...
self.save_hyperparameters(hparams) defvalidation_step(self, batch, batch_idx): metrics=self._compute_metrics(batch) self.log_dict(metrics, prog_bar=True) returnmetrics # Ignite的灵活实验 @trainer.on(Events.EPOCH_COMPLETED) deflog_experiments(engine): ...
self.save_hyperparameters() self.l1 = torch.nn.Linear(28*28, self.hparams.hidden_dim) self.l2 = torch.nn.Linear(self.hparams.hidden_dim,10)defforward(self, x): x = x.view(x.size(0), -1) x = torch.relu(self.l1(x))
importtorchfromtorchimportnnimportpytorch_lightningasplclassLitModel(pl.LightningModule):def__init__(self, input_dim, hidden_dim, output_dim, learning_rate=2e-4):super().__init__()self.save_hyperparameters()# 自动保存初始化参数self.layer = nn.Sequential( ...
针对CIFARModule,这里使用self.save_hyperparameters()来保存超参数,并在初始化函数中定义好损失函数和模型。之后在函数configure_optimizers中,定义好优化器和学习率的schedule,并返回定义好的优化器和schedule。这里的configure_optimizers返回值有多种形式,非常的灵活,具体的可以参考官网:https://pytorch-lightning.readthed...
# Save the arguments as hyperparameters. self.save_hyperparameters() # Loading model using the function defined above. self.model = get_model( model_name=self.hparams.model_name, num_classes=self.hparams.num_classes, freeze_backbone=self.hparams.freeze_backbone, ...
self.save_hyperparameters() self.l1 = nn.Linear(self.hparams.in_dim, self.hparams.out_dim) # 如果在训练和保存模型时,超参数设置如下,在加载后可以替换这些超参数。 LitModel(in_dim=32, out_dim=10) # 仍然使用in_dim=32, out_dim=10 ...