给出了以下错误: In automatic_optimization, when `training_step` returns a dict, the 'loss' key needs to be present 我检查了训练步骤的方法,很好: def training_step(self, batch, batch_idx): """ Lightning calls this insid
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) # Customize training tokenizer.train( files, vocab_size = vocab_size, min_frequ...
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trainer.lightning_module.automatic_optimization: return if opt_indices is None: opt_indices = [] for config in self.trainer.lr_scheduler_configs: if config.opt_idx not in opt_indices: continue if update_plateau_schedulers ^ config.reduce_on_plateau: continue current_idx = self.batch_idx if...
我们向Lightning中添加了两类新的结果对象:TrainResult和EvalResult。它们是用来保存训练/评估/测试每一步输出的精致的字典对象。它们是用来控制在哪里和什么时候记录日志,以及如何在加速器之间进行同步: 在training_step 中使用 TrainResult 自动记录结果: 我们添加到 TrainResult 的 train_loss 会自动地在 tensorboard ...
Deprecated pytorch_lightning.logging (#767) Deprecated LightningModule.load_from_metrics in favour of LightningModule.load_from_checkpoint (#995, #1079) Deprecated @data_loader decorator (#926) Deprecated model steps training_end, validation_end and test_end (#1051, #1056) Removed Removed dependenc...
training, validation, metrics, and logging, greatly simplifying any need to write an external training loop. The model also used a PyTorch Lightning Trainer object that made switching the entire training flow over to the GPU a breeze.Building models from Lightning Modules is a great way to gain...
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昨夜に引き続き、Fine-tuningをやってみました。 今夜は、通常のものと異なり、入力画像としてGray画像を利用します。 Gray⇒カテゴライズします。 人の目だとまあ、分かるわな~ 第一層をGrayつまり1Chに対応させればいいので、もう簡単にまとめようと思います。