optimizer_idx (int) – When using multiple optimizers, this argument will also be present. hiddens (Tensor) – Passed in if truncated_bptt_steps > 0. 返回值:Any of. Tensor - The loss tensor dict - A dictionary. Can include any keys, but must include...
那在pytorch_lightning 中如何设置呢?其实跟pytorch是一样的,基本上不需要修改: # 重写configure_optimizers()函数即可 # 设置优化器 def configure_optimizers(self): weight_decay = 1e-6 # l2正则化系数 # 假如有两个网络,一个encoder一个decoder optimizer = optim.Adam([{'encoder_params': self.encoder....
optimizer_idx(int) – When using multiple optimizers, this argument will also be present. hiddens(Tensor) – Passed in if truncated_bptt_steps > 0. 返回值:Any of. Tensor- The loss tensor dict- A dictionary. Can include any keys, but must include the key'loss' None- Training will skip ...
# 设置优化器 def configure_optimizers(self): weight_decay = 1e-6 # l2正则化系数 # 假如有两个网络,一个encoder一个decoder optimizer = optim.Adam([{'encoder_params': self.encoder.parameters()}, {'decoder_params': self.decoder.parameters()}], lr=learning_rate, weight_decay=weight_decay) ...
同理,在model_interface中建立class MInterface(pl.LightningModule):类,作为模型的中间接口。__init__()函数中import相应模型类,然后老老实实加入configure_optimizers, training_step, validation_step等函数,用一个接口类控制所有模型。不同部分使用输入参数控制。
同理,在model_interface中建立class MInterface(pl.LightningModule):类,作为模型的中间接口。__init__()函数中import相应模型类,然后老老实实加入configure_optimizers, training_step, validation_step等函数,用一个接口...
1. `__init__()`(初始化 LightningModule ) 2. `prepare_data()` (准备数据,包括下载数据、预处理等等) 3. `configure_optimizers()` (配置优化器) 测试“验证代码”。 提前来做的意义在于:不需要等待漫长的训练过程才发现验证代码有错。 这部分就是提前执行 “验证代码”,所以和下面的验证部分是一样的...
classHybridOptim(torch.optim.Optimizer):"""Wrapper around multiple optimizers that should be stepped together at a single time. This isa hack to avoid PyTorch Lightning calling ``training_step`` once for each optimizer, whichincreases training time and is not always necessary.Modified from the rep...
optimizer_idx (int) – When using multiple optimizers, this argument will also be present. hiddens (Tensor) – Passed in if truncated_bptt_steps > 0. 返回值:Any of. Tensor - The loss tensor dict - A dictionary. Can include any keys, but must include the key 'loss' ...
Change how 16-bit is initialized. Add your own way of doing distributed training. Add learning rate schedulers. Use multiple optimizers. Change the frequency of optimizer updates. Get started today withNGC PyTorch LightningDocker Container from the NGC catalog....