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netlightbasemapk

NetLightBaseMAPK

Bases: LightningModule

A LightningModule class for a neural network model.

Attributes:

Name Type Description
l1 int

The number of neurons in the first hidden layer.
epochs int

The number of epochs to train the model for.
batch_size int

The batch size to use during training.
initialization str

The initialization method to use for the weights.
act_fn Module

The activation function to use in the hidden layers.
optimizer str

The optimizer to use during training.
dropout_prob float

The probability of dropping out a neuron during training.
lr_mult float

The learning rate multiplier for the optimizer.
patience int

The number of epochs to wait before early stopping.
_L_in int

The number of input features.
_L_out int

The number of output classes.
layers Sequential

The neural network model.

Examples:

>>> from torch.utils.data import DataLoader
>>> from torchvision.datasets import MNIST
>>> from torchvision.transforms import ToTensor
>>> train_data = MNIST(PATH_DATASETS,
                       train=True,
                       download=True,
                       transform=ToTensor())
>>> train_loader = DataLoader(train_data,
                              batch_size=BATCH_SIZE)
>>> net_light_base = NetLightBase(l1=128,
                                  epochs=10,
                                  batch_size=BATCH_SIZE,
                                  initialization='xavier',
                                  act_fn=nn.ReLU(),
                                  optimizer='Adam',
                                  dropout_prob=0.1,
                                  lr_mult=0.1,
                                  patience=5)
>>> trainer = L.Trainer(max_epochs=10)
>>> trainer.fit(net_light_base, train_loader)
Source code in spotPython/light/classification/netlightbasemapk.py 
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class NetLightBaseMAPK(L.LightningModule):
    """
    A LightningModule class for a neural network model.

    Attributes:
        l1 (int):
            The number of neurons in the first hidden layer.
        epochs (int):
            The number of epochs to train the model for.
        batch_size (int):
            The batch size to use during training.
        initialization (str):
            The initialization method to use for the weights.
        act_fn (nn.Module):
            The activation function to use in the hidden layers.
        optimizer (str):
            The optimizer to use during training.
        dropout_prob (float):
            The probability of dropping out a neuron during training.
        lr_mult (float):
            The learning rate multiplier for the optimizer.
        patience (int):
            The number of epochs to wait before early stopping.
        _L_in (int):
            The number of input features.
        _L_out (int):
            The number of output classes.
        layers (nn.Sequential):
            The neural network model.

    Examples:
        >>> from torch.utils.data import DataLoader
        >>> from torchvision.datasets import MNIST
        >>> from torchvision.transforms import ToTensor
        >>> train_data = MNIST(PATH_DATASETS,
                               train=True,
                               download=True,
                               transform=ToTensor())
        >>> train_loader = DataLoader(train_data,
                                      batch_size=BATCH_SIZE)
        >>> net_light_base = NetLightBase(l1=128,
                                          epochs=10,
                                          batch_size=BATCH_SIZE,
                                          initialization='xavier',
                                          act_fn=nn.ReLU(),
                                          optimizer='Adam',
                                          dropout_prob=0.1,
                                          lr_mult=0.1,
                                          patience=5)
        >>> trainer = L.Trainer(max_epochs=10)
        >>> trainer.fit(net_light_base, train_loader)
    """

    def __init__(
        self,
        l1: int,
        epochs: int,
        batch_size: int,
        initialization: str,
        act_fn: nn.Module,
        optimizer: str,
        dropout_prob: float,
        lr_mult: float,
        patience: int,
        _L_in: int,
        _L_out: int,
    ):
        """
        Initializes the NetLightBase object.

        Args:
            l1 (int): The number of neurons in the first hidden layer.
            epochs (int): The number of epochs to train the model for.
            batch_size (int): The batch size to use during training.
            initialization (str): The initialization method to use for the weights.
            act_fn (nn.Module): The activation function to use in the hidden layers.
            optimizer (str): The optimizer to use during training.
            dropout_prob (float): The probability of dropping out a neuron during training.
            lr_mult (float): The learning rate multiplier for the optimizer.
            patience (int): The number of epochs to wait before early stopping.
            _L_in (int): The number of input features. Not a hyperparameter, but needed to create the network.
            _L_out (int): The number of output classes. Not a hyperparameter, but needed to create the network.

        Returns:
            (NoneType): None

        Raises:
            ValueError: If l1 is less than 4.

        Examples:
            >>> from torch.utils.data import DataLoader
            >>> from torchvision.datasets import MNIST
            >>> from torchvision.transforms import ToTensor
            >>> train_data = MNIST(PATH_DATASETS, train=True, download=True, transform=ToTensor())
            >>> train_loader = DataLoader(train_data, batch_size=BATCH_SIZE)
            >>> net_light_base = NetLightBase(l1=128, epochs=10, batch_size=BATCH_SIZE,
                                                initialization='xavier', act_fn=nn.ReLU(),
                                                optimizer='Adam', dropout_prob=0.1, lr_mult=0.1,
                                                patience=5)
            >>> trainer = L.Trainer(max_epochs=10)
            >>> trainer.fit(net_light_base, train_loader)

        """
        super().__init__()
        # Attribute 'act_fn' is an instance of `nn.Module` and is already saved during
        # checkpointing. It is recommended to ignore them
        # using `self.save_hyperparameters(ignore=['act_fn'])`
        # self.save_hyperparameters(ignore=["act_fn"])
        #
        self._L_in = _L_in
        self._L_out = _L_out
        # _L_in and _L_out are not hyperparameters, but are needed to create the network
        self.save_hyperparameters(ignore=["_L_in", "_L_out"])
        if self.hparams.l1 < 4:
            raise ValueError("l1 must be at least 4")

        hidden_sizes = [self.hparams.l1, self.hparams.l1 // 2, self.hparams.l1 // 2, self.hparams.l1 // 4]
        self.train_mapk = MAPK(k=3)
        self.valid_mapk = MAPK(k=3)
        self.test_mapk = MAPK(k=3)

        # Create the network based on the specified hidden sizes
        layers = []
        layer_sizes = [self._L_in] + hidden_sizes
        layer_size_last = layer_sizes[0]
        for layer_size in layer_sizes[1:]:
            layers += [
                nn.Linear(layer_size_last, layer_size),
                self.hparams.act_fn,
                nn.Dropout(self.hparams.dropout_prob),
            ]
            layer_size_last = layer_size
        layers += [nn.Linear(layer_sizes[-1], self._L_out)]
        # nn.Sequential summarizes a list of modules into a single module, applying them in sequence
        self.layers = nn.Sequential(*layers)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Performs a forward pass through the model.

        Args:
            x (torch.Tensor): A tensor containing a batch of input data.

        Returns:
            torch.Tensor: A tensor containing the probabilities for each class.

        Examples:
            >>> from torch.utils.data import DataLoader
            >>> from torchvision.datasets import MNIST
            >>> from torchvision.transforms import ToTensor
            >>> train_data = MNIST(PATH_DATASETS, train=True, download=True, transform=ToTensor())
            >>> train_loader = DataLoader(train_data, batch_size=BATCH_SIZE)
            >>> net_light_base = NetLightBase(l1=128,
                                              epochs=10,
                                              batch_size=BATCH_SIZE,
                                              initialization='xavier', act_fn=nn.ReLU(),
                                              optimizer='Adam', dropout_prob=0.1, lr_mult=0.1,
                                              patience=5)

        """
        x = self.layers(x)
        return F.softmax(x, dim=1)

    def training_step(self, batch: tuple) -> torch.Tensor:
        """
        Performs a single training step.

        Args:
            batch (tuple): A tuple containing a batch of input data and labels.

        Returns:
            torch.Tensor: A tensor containing the loss for this batch.

        Examples:
            >>> from torch.utils.data import DataLoader
            >>> from torchvision.datasets import MNIST
            >>> from torchvision.transforms import ToTensor
            >>> train_data = MNIST(PATH_DATASETS, train=True, download=True, transform=ToTensor())
            >>> train_loader = DataLoader(train_data, batch_size=BATCH_SIZE)
            >>> net_light_base = NetLightBase(l1=128,
                                                epochs=10,
                                                batch_size=BATCH_SIZE,
                                                initialization='xavier', act_fn=nn.ReLU(),
                                                optimizer='Adam', dropout_prob=0.1, lr_mult=0.1,
                                                patience=5)
            >>> trainer = L.Trainer(max_epochs=10)
            >>> trainer.fit(net_light_base, train_loader)

        """
        x, y = batch
        logits = self(x)
        # compute cross entropy loss from logits and y
        loss = F.cross_entropy(logits, y)
        # self.train_mapk(logits, y)
        # self.log("train_mapk", self.train_mapk, on_step=True, on_epoch=False)
        return loss

    def validation_step(self, batch: tuple, batch_idx: int, prog_bar: bool = False):
        """
        Performs a single validation step.

        Args:
            batch (tuple): A tuple containing a batch of input data and labels.
            batch_idx (int): The index of the current batch.
            prog_bar (bool, optional): Whether to display the progress bar. Defaults to False.

        Returns:
            (NoneType): None

        Examples:
            >>> from torch.utils.data import DataLoader
            >>> from torchvision.datasets import MNIST
            >>> from torchvision.transforms import ToTensor
            >>> val_data = MNIST(PATH_DATASETS, train=False, download=True, transform=ToTensor())
            >>> val_loader = DataLoader(val_data, batch_size=BATCH_SIZE)
            >>> net_light_base = NetLightBase(l1=128,
                                                epochs=10,
                                                batch_size=BATCH_SIZE,
                                                initialization='xavier', act_fn=nn.ReLU(),
                                                optimizer='Adam', dropout_prob=0.1, lr_mult=0.1,
                                                patience=5)
            >>> trainer = L.Trainer(max_epochs=10)
            >>> trainer.fit(net_light_base, val_loader)

        """
        x, y = batch
        logits = self(x)
        # compute cross entropy loss from logits and y
        loss = F.cross_entropy(logits, y)
        # loss = F.nll_loss(logits, y)
        preds = torch.argmax(logits, dim=1)
        acc = accuracy(preds, y, task="multiclass", num_classes=self._L_out)
        self.valid_mapk(logits, y)
        self.log("valid_mapk", self.valid_mapk, on_step=False, on_epoch=True, prog_bar=prog_bar)
        self.log("val_loss", loss, prog_bar=prog_bar)
        self.log("val_acc", acc, prog_bar=prog_bar)
        self.log("hp_metric", loss, prog_bar=prog_bar)

    def test_step(self, batch: tuple, batch_idx: int, prog_bar: bool = False) -> tuple:
        """
        Performs a single test step.

        Args:
            batch (tuple): A tuple containing a batch of input data and labels.
            batch_idx (int): The index of the current batch.
            prog_bar (bool, optional): Whether to display the progress bar. Defaults to False.

        Returns:
            tuple: A tuple containing the loss and accuracy for this batch.
        """
        x, y = batch
        logits = self(x)
        # compute cross entropy loss from logits and y
        loss = F.cross_entropy(logits, y)
        preds = torch.argmax(logits, dim=1)
        acc = accuracy(preds, y, task="multiclass", num_classes=self._L_out)
        self.test_mapk(logits, y)
        self.log("test_mapk", self.test_mapk, on_step=True, on_epoch=True, prog_bar=prog_bar)
        self.log("val_loss", loss, prog_bar=prog_bar)
        self.log("val_acc", acc, prog_bar=prog_bar)
        self.log("hp_metric", loss, prog_bar=prog_bar)
        return loss, acc

    def configure_optimizers(self) -> torch.optim.Optimizer:
        """
        Configures the optimizer for the model.

        Returns:
            torch.optim.Optimizer: The optimizer to use during training.

        """
        # optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)
        optimizer = optimizer_handler(
            optimizer_name=self.hparams.optimizer, params=self.parameters(), lr_mult=self.hparams.lr_mult
        )
        return optimizer

__init__(l1, epochs, batch_size, initialization, act_fn, optimizer, dropout_prob, lr_mult, patience, _L_in, _L_out)

Initializes the NetLightBase object.

Parameters:

Name Type Description Default
l1 int

The number of neurons in the first hidden layer.

 required
epochs int

The number of epochs to train the model for.

 required
batch_size int

The batch size to use during training.

 required
initialization str

The initialization method to use for the weights.

 required
act_fn Module

The activation function to use in the hidden layers.

 required
optimizer str

The optimizer to use during training.

 required
dropout_prob float

The probability of dropping out a neuron during training.

 required
lr_mult float

The learning rate multiplier for the optimizer.

 required
patience int

The number of epochs to wait before early stopping.

 required
_L_in int

The number of input features. Not a hyperparameter, but needed to create the network.

 required
_L_out int

The number of output classes. Not a hyperparameter, but needed to create the network.

 required

Returns:

Type Description
NoneType

None

Raises:

Type Description
ValueError

If l1 is less than 4.

Examples:

>>> from torch.utils.data import DataLoader
>>> from torchvision.datasets import MNIST
>>> from torchvision.transforms import ToTensor
>>> train_data = MNIST(PATH_DATASETS, train=True, download=True, transform=ToTensor())
>>> train_loader = DataLoader(train_data, batch_size=BATCH_SIZE)
>>> net_light_base = NetLightBase(l1=128, epochs=10, batch_size=BATCH_SIZE,
                                    initialization='xavier', act_fn=nn.ReLU(),
                                    optimizer='Adam', dropout_prob=0.1, lr_mult=0.1,
                                    patience=5)
>>> trainer = L.Trainer(max_epochs=10)
>>> trainer.fit(net_light_base, train_loader)
Source code in spotPython/light/classification/netlightbasemapk.py 
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def __init__(
    self,
    l1: int,
    epochs: int,
    batch_size: int,
    initialization: str,
    act_fn: nn.Module,
    optimizer: str,
    dropout_prob: float,
    lr_mult: float,
    patience: int,
    _L_in: int,
    _L_out: int,
):
    """
    Initializes the NetLightBase object.

    Args:
        l1 (int): The number of neurons in the first hidden layer.
        epochs (int): The number of epochs to train the model for.
        batch_size (int): The batch size to use during training.
        initialization (str): The initialization method to use for the weights.
        act_fn (nn.Module): The activation function to use in the hidden layers.
        optimizer (str): The optimizer to use during training.
        dropout_prob (float): The probability of dropping out a neuron during training.
        lr_mult (float): The learning rate multiplier for the optimizer.
        patience (int): The number of epochs to wait before early stopping.
        _L_in (int): The number of input features. Not a hyperparameter, but needed to create the network.
        _L_out (int): The number of output classes. Not a hyperparameter, but needed to create the network.

    Returns:
        (NoneType): None

    Raises:
        ValueError: If l1 is less than 4.

    Examples:
        >>> from torch.utils.data import DataLoader
        >>> from torchvision.datasets import MNIST
        >>> from torchvision.transforms import ToTensor
        >>> train_data = MNIST(PATH_DATASETS, train=True, download=True, transform=ToTensor())
        >>> train_loader = DataLoader(train_data, batch_size=BATCH_SIZE)
        >>> net_light_base = NetLightBase(l1=128, epochs=10, batch_size=BATCH_SIZE,
                                            initialization='xavier', act_fn=nn.ReLU(),
                                            optimizer='Adam', dropout_prob=0.1, lr_mult=0.1,
                                            patience=5)
        >>> trainer = L.Trainer(max_epochs=10)
        >>> trainer.fit(net_light_base, train_loader)

    """
    super().__init__()
    # Attribute 'act_fn' is an instance of `nn.Module` and is already saved during
    # checkpointing. It is recommended to ignore them
    # using `self.save_hyperparameters(ignore=['act_fn'])`
    # self.save_hyperparameters(ignore=["act_fn"])
    #
    self._L_in = _L_in
    self._L_out = _L_out
    # _L_in and _L_out are not hyperparameters, but are needed to create the network
    self.save_hyperparameters(ignore=["_L_in", "_L_out"])
    if self.hparams.l1 < 4:
        raise ValueError("l1 must be at least 4")

    hidden_sizes = [self.hparams.l1, self.hparams.l1 // 2, self.hparams.l1 // 2, self.hparams.l1 // 4]
    self.train_mapk = MAPK(k=3)
    self.valid_mapk = MAPK(k=3)
    self.test_mapk = MAPK(k=3)

    # Create the network based on the specified hidden sizes
    layers = []
    layer_sizes = [self._L_in] + hidden_sizes
    layer_size_last = layer_sizes[0]
    for layer_size in layer_sizes[1:]:
        layers += [
            nn.Linear(layer_size_last, layer_size),
            self.hparams.act_fn,
            nn.Dropout(self.hparams.dropout_prob),
        ]
        layer_size_last = layer_size
    layers += [nn.Linear(layer_sizes[-1], self._L_out)]
    # nn.Sequential summarizes a list of modules into a single module, applying them in sequence
    self.layers = nn.Sequential(*layers)

configure_optimizers()

Configures the optimizer for the model.

Returns:

Type Description
Optimizer

torch.optim.Optimizer: The optimizer to use during training.
Source code in spotPython/light/classification/netlightbasemapk.py 
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def configure_optimizers(self) -> torch.optim.Optimizer:
    """
    Configures the optimizer for the model.

    Returns:
        torch.optim.Optimizer: The optimizer to use during training.

    """
    # optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)
    optimizer = optimizer_handler(
        optimizer_name=self.hparams.optimizer, params=self.parameters(), lr_mult=self.hparams.lr_mult
    )
    return optimizer

forward(x)

Performs a forward pass through the model.

Parameters:

Name Type Description Default
x Tensor

A tensor containing a batch of input data.

 required

Returns:

Type Description
Tensor

torch.Tensor: A tensor containing the probabilities for each class.

Examples:

>>> from torch.utils.data import DataLoader
>>> from torchvision.datasets import MNIST
>>> from torchvision.transforms import ToTensor
>>> train_data = MNIST(PATH_DATASETS, train=True, download=True, transform=ToTensor())
>>> train_loader = DataLoader(train_data, batch_size=BATCH_SIZE)
>>> net_light_base = NetLightBase(l1=128,
                                  epochs=10,
                                  batch_size=BATCH_SIZE,
                                  initialization='xavier', act_fn=nn.ReLU(),
                                  optimizer='Adam', dropout_prob=0.1, lr_mult=0.1,
                                  patience=5)
Source code in spotPython/light/classification/netlightbasemapk.py 
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def forward(self, x: torch.Tensor) -> torch.Tensor:
    """
    Performs a forward pass through the model.

    Args:
        x (torch.Tensor): A tensor containing a batch of input data.

    Returns:
        torch.Tensor: A tensor containing the probabilities for each class.

    Examples:
        >>> from torch.utils.data import DataLoader
        >>> from torchvision.datasets import MNIST
        >>> from torchvision.transforms import ToTensor
        >>> train_data = MNIST(PATH_DATASETS, train=True, download=True, transform=ToTensor())
        >>> train_loader = DataLoader(train_data, batch_size=BATCH_SIZE)
        >>> net_light_base = NetLightBase(l1=128,
                                          epochs=10,
                                          batch_size=BATCH_SIZE,
                                          initialization='xavier', act_fn=nn.ReLU(),
                                          optimizer='Adam', dropout_prob=0.1, lr_mult=0.1,
                                          patience=5)

    """
    x = self.layers(x)
    return F.softmax(x, dim=1)

test_step(batch, batch_idx, prog_bar=False)

Performs a single test step.

Parameters:

Name Type Description Default
batch tuple

A tuple containing a batch of input data and labels.

 required
batch_idx int

The index of the current batch.

 required
prog_bar bool

Whether to display the progress bar. Defaults to False.

 False

Returns:

Name Type Description
tuple tuple

A tuple containing the loss and accuracy for this batch.
Source code in spotPython/light/classification/netlightbasemapk.py 
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def test_step(self, batch: tuple, batch_idx: int, prog_bar: bool = False) -> tuple:
    """
    Performs a single test step.

    Args:
        batch (tuple): A tuple containing a batch of input data and labels.
        batch_idx (int): The index of the current batch.
        prog_bar (bool, optional): Whether to display the progress bar. Defaults to False.

    Returns:
        tuple: A tuple containing the loss and accuracy for this batch.
    """
    x, y = batch
    logits = self(x)
    # compute cross entropy loss from logits and y
    loss = F.cross_entropy(logits, y)
    preds = torch.argmax(logits, dim=1)
    acc = accuracy(preds, y, task="multiclass", num_classes=self._L_out)
    self.test_mapk(logits, y)
    self.log("test_mapk", self.test_mapk, on_step=True, on_epoch=True, prog_bar=prog_bar)
    self.log("val_loss", loss, prog_bar=prog_bar)
    self.log("val_acc", acc, prog_bar=prog_bar)
    self.log("hp_metric", loss, prog_bar=prog_bar)
    return loss, acc

training_step(batch)

Performs a single training step.

Parameters:

Name Type Description Default
batch tuple

A tuple containing a batch of input data and labels.

 required

Returns:

Type Description
Tensor

torch.Tensor: A tensor containing the loss for this batch.

Examples:

>>> from torch.utils.data import DataLoader
>>> from torchvision.datasets import MNIST
>>> from torchvision.transforms import ToTensor
>>> train_data = MNIST(PATH_DATASETS, train=True, download=True, transform=ToTensor())
>>> train_loader = DataLoader(train_data, batch_size=BATCH_SIZE)
>>> net_light_base = NetLightBase(l1=128,
                                    epochs=10,
                                    batch_size=BATCH_SIZE,
                                    initialization='xavier', act_fn=nn.ReLU(),
                                    optimizer='Adam', dropout_prob=0.1, lr_mult=0.1,
                                    patience=5)
>>> trainer = L.Trainer(max_epochs=10)
>>> trainer.fit(net_light_base, train_loader)
Source code in spotPython/light/classification/netlightbasemapk.py 
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def training_step(self, batch: tuple) -> torch.Tensor:
    """
    Performs a single training step.

    Args:
        batch (tuple): A tuple containing a batch of input data and labels.

    Returns:
        torch.Tensor: A tensor containing the loss for this batch.

    Examples:
        >>> from torch.utils.data import DataLoader
        >>> from torchvision.datasets import MNIST
        >>> from torchvision.transforms import ToTensor
        >>> train_data = MNIST(PATH_DATASETS, train=True, download=True, transform=ToTensor())
        >>> train_loader = DataLoader(train_data, batch_size=BATCH_SIZE)
        >>> net_light_base = NetLightBase(l1=128,
                                            epochs=10,
                                            batch_size=BATCH_SIZE,
                                            initialization='xavier', act_fn=nn.ReLU(),
                                            optimizer='Adam', dropout_prob=0.1, lr_mult=0.1,
                                            patience=5)
        >>> trainer = L.Trainer(max_epochs=10)
        >>> trainer.fit(net_light_base, train_loader)

    """
    x, y = batch
    logits = self(x)
    # compute cross entropy loss from logits and y
    loss = F.cross_entropy(logits, y)
    # self.train_mapk(logits, y)
    # self.log("train_mapk", self.train_mapk, on_step=True, on_epoch=False)
    return loss

validation_step(batch, batch_idx, prog_bar=False)

Performs a single validation step.

Parameters:

Name Type Description Default
batch tuple

A tuple containing a batch of input data and labels.

 required
batch_idx int

The index of the current batch.

 required
prog_bar bool

Whether to display the progress bar. Defaults to False.

 False

Returns:

Type Description
NoneType

None

Examples:

>>> from torch.utils.data import DataLoader
>>> from torchvision.datasets import MNIST
>>> from torchvision.transforms import ToTensor
>>> val_data = MNIST(PATH_DATASETS, train=False, download=True, transform=ToTensor())
>>> val_loader = DataLoader(val_data, batch_size=BATCH_SIZE)
>>> net_light_base = NetLightBase(l1=128,
                                    epochs=10,
                                    batch_size=BATCH_SIZE,
                                    initialization='xavier', act_fn=nn.ReLU(),
                                    optimizer='Adam', dropout_prob=0.1, lr_mult=0.1,
                                    patience=5)
>>> trainer = L.Trainer(max_epochs=10)
>>> trainer.fit(net_light_base, val_loader)
Source code in spotPython/light/classification/netlightbasemapk.py 
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def validation_step(self, batch: tuple, batch_idx: int, prog_bar: bool = False):
    """
    Performs a single validation step.

    Args:
        batch (tuple): A tuple containing a batch of input data and labels.
        batch_idx (int): The index of the current batch.
        prog_bar (bool, optional): Whether to display the progress bar. Defaults to False.

    Returns:
        (NoneType): None

    Examples:
        >>> from torch.utils.data import DataLoader
        >>> from torchvision.datasets import MNIST
        >>> from torchvision.transforms import ToTensor
        >>> val_data = MNIST(PATH_DATASETS, train=False, download=True, transform=ToTensor())
        >>> val_loader = DataLoader(val_data, batch_size=BATCH_SIZE)
        >>> net_light_base = NetLightBase(l1=128,
                                            epochs=10,
                                            batch_size=BATCH_SIZE,
                                            initialization='xavier', act_fn=nn.ReLU(),
                                            optimizer='Adam', dropout_prob=0.1, lr_mult=0.1,
                                            patience=5)
        >>> trainer = L.Trainer(max_epochs=10)
        >>> trainer.fit(net_light_base, val_loader)

    """
    x, y = batch
    logits = self(x)
    # compute cross entropy loss from logits and y
    loss = F.cross_entropy(logits, y)
    # loss = F.nll_loss(logits, y)
    preds = torch.argmax(logits, dim=1)
    acc = accuracy(preds, y, task="multiclass", num_classes=self._L_out)
    self.valid_mapk(logits, y)
    self.log("valid_mapk", self.valid_mapk, on_step=False, on_epoch=True, prog_bar=prog_bar)
    self.log("val_loss", loss, prog_bar=prog_bar)
    self.log("val_acc", acc, prog_bar=prog_bar)
    self.log("hp_metric", loss, prog_bar=prog_bar)