nn_condnet_regressor
NNCondNetRegressor
¶
Bases: LightningModule
A LightningModule class for a conditional 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. |
batch_norm |
bool
|
Whether to use batch normalization or not. |
_L_in |
int
|
The number of input features. |
_L_out |
int
|
The number of output classes. |
_L_cond |
int
|
The number of neurons in the conditional hidden layer. |
_torchmetric |
str
|
The metric to use for the loss function. If |
layers |
Sequential
|
The neural network model. |
Examples:
>>> from torch.utils.data import DataLoader
from spotpython.light.regression import NNLinearRegressor
from torch import nn
import lightning as L
import torch
from torch.utils.data import TensorDataset
PATH_DATASETS = './data'
BATCH_SIZE = 128
# generate data
num_samples = 1_000
input_dim = 10
X = torch.randn(num_samples, input_dim) # random data for example
Y = torch.randn(num_samples, 1) # random target for example
data_set = TensorDataset(X, Y)
train_loader = DataLoader(dataset=data_set, batch_size=BATCH_SIZE)
test_loader = DataLoader(dataset=data_set, batch_size=BATCH_SIZE)
val_loader = DataLoader(dataset=data_set, batch_size=BATCH_SIZE)
batch_x, batch_y = next(iter(train_loader))
print(batch_x.shape)
print(batch_y.shape)
net_light_base = NNLinearRegressor(l1=128,
batch_norm=True,
epochs=10,
batch_size=BATCH_SIZE,
initialization='xavier',
act_fn=nn.ReLU(),
optimizer='Adam',
dropout_prob=0.1,
lr_mult=0.1,
patience=5,
_L_in=input_dim,
_L_out=1,
_torchmetric="mean_squared_error",)
trainer = L.Trainer(max_epochs=2, enable_progress_bar=True)
trainer.fit(net_light_base, train_loader)
# validation and test should give the same result, because the data is the same
trainer.validate(net_light_base, val_loader)
trainer.test(net_light_base, test_loader)
GPU available: True (mps), used: True
TPU available: False, using: 0 TPU cores
HPU available: False, using: 0 HPUs
| Name | Type | Params | Mode | In sizes | Out sizes
----------------------------------------------------------------------
0 | layers | Sequential | 20.8 K | train | [128, 10] | [128, 1]
----------------------------------------------------------------------
20.8 K Trainable params
0 Non-trainable params
20.8 K Total params
0.083 Total estimated model params size (MB)
69 Modules in train mode
0 Modules in eval mode
torch.Size([128, 10])
torch.Size([128, 1])
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Validate metric ┃ DataLoader 0 ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ hp_metric │ 81.1978988647461 │
│ val_loss │ 81.1978988647461 │
└───────────────────────────┴───────────────────────────┘
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Test metric ┃ DataLoader 0 ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ hp_metric │ 81.1978988647461 │
│ val_loss │ 81.1978988647461 │
└───────────────────────────┴───────────────────────────┘
[{'val_loss': 81.1978988647461, 'hp_metric': 81.1978988647461}]
Source code in spotpython/light/regression/nn_condnet_regressor.py
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|
__init__(l1, epochs, batch_size, initialization, act_fn, optimizer, dropout_prob, lr_mult, patience, batch_norm, _L_cond, _L_in, _L_out, _torchmetric, *args, **kwargs)
¶
Initializes the NNLinearRegressor 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 |
batch_norm |
bool
|
Whether to use batch normalization or not. |
required |
_L_cond |
int
|
The number of neurons in the conditional hidden layer. |
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 |
_torchmetric |
str
|
The metric to use for the loss function. If |
required |
Returns:
Type | Description |
---|---|
NoneType
|
None |
Raises:
Type | Description |
---|---|
ValueError
|
If l1 is less than 4. |
Source code in spotpython/light/regression/nn_condnet_regressor.py
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|
configure_optimizers()
¶
Configures the optimizer for the model.
Notes
The default Lightning way is to define an optimizer as
optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)
.
spotpython uses an optimizer handler to create the optimizer, which
adapts the learning rate according to the lr_mult hyperparameter as
well as other hyperparameters. See spotpython.hyperparameters.optimizer.py
for details.
Returns:
Type | Description |
---|---|
Optimizer
|
torch.optim.Optimizer: The optimizer to use during training. |
Source code in spotpython/light/regression/nn_condnet_regressor.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 output of the model. |
Source code in spotpython/light/regression/nn_condnet_regressor.py
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|
predict_step(batch, batch_idx, prog_bar=False)
¶
Performs a single prediction 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 |
---|---|
Tensor
|
torch.Tensor: A tensor containing the prediction for this batch. |
Source code in spotpython/light/regression/nn_condnet_regressor.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:
Type | Description |
---|---|
Tensor
|
torch.Tensor: A tensor containing the loss for this batch. |
Source code in spotpython/light/regression/nn_condnet_regressor.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. |
Source code in spotpython/light/regression/nn_condnet_regressor.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 |
---|---|
Tensor
|
torch.Tensor: A tensor containing the loss for this batch. |
Source code in spotpython/light/regression/nn_condnet_regressor.py
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|