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#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file is distributed
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
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import pytorch_lightning as pl
import torch
from gluonts.torch.modules.loss import DistributionLoss, NegativeLogLikelihood
from .module import SimpleFeedForwardModel
[docs]class SimpleFeedForwardLightningModule(pl.LightningModule):
"""
A ``pl.LightningModule`` class that can be used to train a
``SimpleFeedForwardModel`` with PyTorch Lightning.
This is a thin layer around a (wrapped) ``SimpleFeedForwardModel`` object,
that exposes the methods to evaluate training and validation loss.
Parameters
----------
model
``SimpleFeedForwardModel`` to be trained.
loss
Loss function to be used for training,
default: ``NegativeLogLikelihood()``.
lr
Learning rate, default: ``1e-3``.
weight_decay
Weight decay regularization parameter, default: ``1e-8``.
"""
def __init__(
self,
model: SimpleFeedForwardModel,
loss: DistributionLoss = NegativeLogLikelihood(),
lr: float = 1e-3,
weight_decay: float = 1e-8,
):
super().__init__()
self.save_hyperparameters()
self.model = model
self.loss = loss
self.lr = lr
self.weight_decay = weight_decay
def _compute_loss(self, batch):
context = batch["past_target"]
target = batch["future_target"]
observed_target = batch["future_observed_values"]
assert context.shape[-1] == self.model.context_length
assert target.shape[-1] == self.model.prediction_length
distr_args, loc, scale = self.model(context)
distr = self.model.distr_output.distribution(distr_args, loc, scale)
return (
self.loss(distr, target) * observed_target
).sum() / torch.maximum(torch.tensor(1.0), observed_target.sum())
[docs] def training_step(self, batch, batch_idx: int): # type: ignore
"""
Execute training step.
"""
train_loss = self._compute_loss(batch)
self.log(
"train_loss",
train_loss,
on_epoch=True,
on_step=False,
prog_bar=True,
)
return train_loss
[docs] def validation_step(self, batch, batch_idx: int): # type: ignore
"""
Execute validation step.
"""
val_loss = self._compute_loss(batch)
self.log(
"val_loss", val_loss, on_epoch=True, on_step=False, prog_bar=True
)
return val_loss