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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|
__init__(l1, epochs, batch_size, initialization, act_fn, optimizer, dropout_prob, lr_mult, patience, _L_in, _L_out, *args, **kwargs)
¶
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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|
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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|
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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|
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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|
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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|
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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