Skip to content

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
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
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,
        *args,
        **kwargs,
    ):
        """
        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, *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
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
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,
    *args,
    **kwargs,
):
    """
    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
275
276
277
278
279
280
281
282
283
284
285
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
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
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
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
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
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
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
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
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)