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litmodel

LitModel

Bases: LightningModule

A LightningModule class for a simple 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.
act_fn str

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

The optimizer to use during training.
learning_rate float

The learning rate for the optimizer.
_L_in int

The number of input features.
_L_out int

The number of output classes.
model 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)
>>> lit_model = LitModel(l1=128, epochs=10, batch_size=BATCH_SIZE, act_fn='relu', optimizer='adam')
>>> trainer = L.Trainer(max_epochs=10)
>>> trainer.fit(lit_model, train_loader)
Source code in spotPython/light/litmodel.py 
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class LitModel(L.LightningModule):
    """
    A LightningModule class for a simple 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.
        act_fn (str): The activation function to use in the hidden layers.
        optimizer (str): The optimizer to use during training.
        learning_rate (float): The learning rate for the optimizer.
        _L_in (int): The number of input features.
        _L_out (int): The number of output classes.
        model (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)
        >>> lit_model = LitModel(l1=128, epochs=10, batch_size=BATCH_SIZE, act_fn='relu', optimizer='adam')
        >>> trainer = L.Trainer(max_epochs=10)
        >>> trainer.fit(lit_model, train_loader)
    """

    def __init__(
        self,
        l1: int,
        epochs: int,
        batch_size: int,
        act_fn: str,
        optimizer: str,
        learning_rate: float = 2e-4,
        _L_in: int = 28 * 28,
        _L_out: int = 10,
    ):
        """
        Initializes the LitModel 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.
            act_fn (str): The activation function to use in the hidden layers.
            optimizer (str): The optimizer to use during training.
            learning_rate (float, optional): The learning rate for the optimizer. Defaults to 2e-4.
            _L_in (int, optional): The number of input features. Defaults to 28 * 28.
            _L_out (int, optional): The number of output classes. Defaults to 10.

        Returns:
           (NoneType): None
        """
        super().__init__()

        # We take in input dimensions as parameters and use those to dynamically build model.
        self._L_out = _L_out
        self.l1 = l1
        self.epochs = epochs
        self.batch_size = batch_size
        self.act_fn = act_fn
        self.optimizer = optimizer
        self.learning_rate = learning_rate

        self.model = nn.Sequential(
            nn.Flatten(),
            nn.Linear(_L_in, l1),
            nn.ReLU(),
            nn.Dropout(0.1),
            nn.Linear(l1, l1),
            nn.ReLU(),
            nn.Dropout(0.1),
            nn.Linear(l1, _L_out),
        )

    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 log probabilities for each class.
        """
        x = self.model(x)
        return F.log_softmax(x, dim=1)

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

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

        Returns:
            torch.Tensor: A tensor containing the loss for this batch.
        """
        x, y = batch
        logits = self(x)
        loss = F.nll_loss(logits, y)
        return loss

    def validation_step(self, batch: tuple, batch_idx: int) -> None:
        """
        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.

        Returns:
            None
        """
        x, y = batch
        logits = self(x)
        loss = F.nll_loss(logits, y)
        preds = torch.argmax(logits, dim=1)
        acc = accuracy(preds, y, task="multiclass", num_classes=10)
        self.log("val_loss", loss, prog_bar=True)
        self.log("val_acc", acc, prog_bar=True)

    def test_step(self, batch: tuple, batch_idx: int) -> 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.

        Returns:
            tuple: A tuple containing the loss and accuracy for this batch.
        """
        x, y = batch
        logits = self(x)
        loss = F.nll_loss(logits, y)
        preds = torch.argmax(logits, dim=1)
        acc = accuracy(preds, y, task="multiclass", num_classes=10)
        self.log("val_loss", loss, prog_bar=True)
        self.log("val_acc", acc, prog_bar=True)
        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)
        return optimizer

__init__(l1, epochs, batch_size, act_fn, optimizer, learning_rate=0.0002, _L_in=28 * 28, _L_out=10)

Initializes the LitModel 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
act_fn str

The activation function to use in the hidden layers.

 required
optimizer str

The optimizer to use during training.

 required
learning_rate float

The learning rate for the optimizer. Defaults to 2e-4.

 0.0002
_L_in int

The number of input features. Defaults to 28 * 28.

 28 * 28
_L_out int

The number of output classes. Defaults to 10.

 10

Returns:

Type Description
NoneType

None
Source code in spotPython/light/litmodel.py 
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def __init__(
    self,
    l1: int,
    epochs: int,
    batch_size: int,
    act_fn: str,
    optimizer: str,
    learning_rate: float = 2e-4,
    _L_in: int = 28 * 28,
    _L_out: int = 10,
):
    """
    Initializes the LitModel 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.
        act_fn (str): The activation function to use in the hidden layers.
        optimizer (str): The optimizer to use during training.
        learning_rate (float, optional): The learning rate for the optimizer. Defaults to 2e-4.
        _L_in (int, optional): The number of input features. Defaults to 28 * 28.
        _L_out (int, optional): The number of output classes. Defaults to 10.

    Returns:
       (NoneType): None
    """
    super().__init__()

    # We take in input dimensions as parameters and use those to dynamically build model.
    self._L_out = _L_out
    self.l1 = l1
    self.epochs = epochs
    self.batch_size = batch_size
    self.act_fn = act_fn
    self.optimizer = optimizer
    self.learning_rate = learning_rate

    self.model = nn.Sequential(
        nn.Flatten(),
        nn.Linear(_L_in, l1),
        nn.ReLU(),
        nn.Dropout(0.1),
        nn.Linear(l1, l1),
        nn.ReLU(),
        nn.Dropout(0.1),
        nn.Linear(l1, _L_out),
    )

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/litmodel.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)
    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 log probabilities for each class.
Source code in spotPython/light/litmodel.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 log probabilities for each class.
    """
    x = self.model(x)
    return F.log_softmax(x, dim=1)

test_step(batch, batch_idx)

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

Returns:

Name Type Description
tuple tuple

A tuple containing the loss and accuracy for this batch.
Source code in spotPython/light/litmodel.py 
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def test_step(self, batch: tuple, batch_idx: int) -> 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.

    Returns:
        tuple: A tuple containing the loss and accuracy for this batch.
    """
    x, y = batch
    logits = self(x)
    loss = F.nll_loss(logits, y)
    preds = torch.argmax(logits, dim=1)
    acc = accuracy(preds, y, task="multiclass", num_classes=10)
    self.log("val_loss", loss, prog_bar=True)
    self.log("val_acc", acc, prog_bar=True)
    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.
Source code in spotPython/light/litmodel.py 
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def training_step(self, batch: tuple) -> torch.Tensor:
    """
    Performs a single training step.

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

    Returns:
        torch.Tensor: A tensor containing the loss for this batch.
    """
    x, y = batch
    logits = self(x)
    loss = F.nll_loss(logits, y)
    return loss

validation_step(batch, batch_idx)

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

Returns:

Type Description
None

None
Source code in spotPython/light/litmodel.py 
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def validation_step(self, batch: tuple, batch_idx: int) -> None:
    """
    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.

    Returns:
        None
    """
    x, y = batch
    logits = self(x)
    loss = F.nll_loss(logits, y)
    preds = torch.argmax(logits, dim=1)
    acc = accuracy(preds, y, task="multiclass", num_classes=10)
    self.log("val_loss", loss, prog_bar=True)
    self.log("val_acc", acc, prog_bar=True)