#修改下面这段定义深度学习模型参数的代码,要求在训练集上训练时"t_timesteps"=303,在验证集上验证时"t_timesteps"=803,使用pytorch_lightning训练::class tf_net(nn.Module): """ Vector Quantised Attention-Recurrent Network for Neural Audio Coding """ def __init__( self, ): super(tf_net, self).__init__() cfg = { "sr": 44100, "complex_as_channel": True, "is_mono": True, "n_fft": 2048, "bandsplits": [ (1000, 100), (4000, 250), (8000, 500), (16000, 1000), (20000, 2000), ], "t_timesteps": 303, "fc_dim": 128 }
时间: 2024-02-16 18:27:01 浏览: 24
可以使用pytorch_lightning的模块参数化来完成这个任务。在pytorch_lightning中,我们可以使用 `hparams` 字典来存储所有超参数。在训练和验证阶段,我们可以使用不同的 `hparams` 值来控制超参数。下面是修改后的代码:
```python
import pytorch_lightning as pl
class tf_net(pl.LightningModule):
"""Vector Quantised Attention-Recurrent Network for Neural Audio Coding"""
def __init__(self, sr, complex_as_channel, is_mono, n_fft, bandsplits, t_timesteps, fc_dim):
super(tf_net, self).__init__()
self.save_hyperparameters()
self.cfg = {
"sr": sr,
"complex_as_channel": complex_as_channel,
"is_mono": is_mono,
"n_fft": n_fft,
"bandsplits": bandsplits,
"t_timesteps": t_timesteps,
"fc_dim": fc_dim,
}
# define your network architecture here
def training_step(self, batch, batch_idx):
# use self.hparams.t_timesteps to access the value of t_timesteps in the training phase
t_timesteps = self.hparams.t_timesteps
# training step implementation
def validation_step(self, batch, batch_idx):
# use self.hparams.t_timesteps to access the value of t_timesteps in the validation phase
t_timesteps = self.hparams.t_timesteps
# validation step implementation
def configure_optimizers(self):
# define your optimizer
return optimizer
```
然后,你可以使用以下代码来训练和验证模型:
```python
# create a new instance of tf_net with different values of t_timesteps for training and validation
model = tf_net(sr=44100, complex_as_channel=True, is_mono=True, n_fft=2048, bandsplits=[(1000, 100), (4000, 250), (8000, 500), (16000, 1000), (20000, 2000)], t_timesteps=303, fc_dim=128)
# create a trainer object with the required hyperparameters
trainer = pl.Trainer(gpus=1, max_epochs=100, progress_bar_refresh_rate=20)
# train the model on the training set with t_timesteps=303
trainer.fit(model, train_dataloader)
# validate the model on the validation set with t_timesteps=803
model.hparams.t_timesteps = 803
trainer.validate(model, val_dataloader)
```