skorch.net¶
Neural net classes.
-
class
skorch.net.
NeuralNet
(module, criterion, optimizer=<class 'torch.optim.sgd.SGD'>, lr=0.01, max_epochs=10, batch_size=128, iterator_train=<class 'torch.utils.data.dataloader.DataLoader'>, iterator_valid=<class 'torch.utils.data.dataloader.DataLoader'>, dataset=<class 'skorch.dataset.Dataset'>, train_split=<skorch.dataset.CVSplit object>, callbacks=None, predict_nonlinearity='auto', warm_start=False, verbose=1, device='cpu', **kwargs)[source]¶ NeuralNet base class.
The base class covers more generic cases. Depending on your use case, you might want to use
NeuralNetClassifier
orNeuralNetRegressor
.In addition to the parameters listed below, there are parameters with specific prefixes that are handled separately. To illustrate this, here is an example:
>>> net = NeuralNet( ... ..., ... optimizer=torch.optimizer.SGD, ... optimizer__momentum=0.95, ...)
This way, when
optimizer
is initialized,NeuralNet
will take care of setting themomentum
parameter to 0.95.(Note that the double underscore notation in
optimizer__momentum
means that the parametermomentum
should be set on the objectoptimizer
. This is the same semantic as used by sklearn.)Furthermore, this allows to change those parameters later:
net.set_params(optimizer__momentum=0.99)
This can be useful when you want to change certain parameters using a callback, when using the net in an sklearn grid search, etc.
By default an
EpochTimer
,BatchScoring
(for both training and validation datasets), andPrintLog
callbacks are installed for the user’s convenience.Parameters: - module : torch module (class or instance)
A PyTorch
Module
. In general, the uninstantiated class should be passed, although instantiated modules will also work.- criterion : torch criterion (class)
The uninitialized criterion (loss) used to optimize the module.
- optimizer : torch optim (class, default=torch.optim.SGD)
The uninitialized optimizer (update rule) used to optimize the module
- lr : float (default=0.01)
Learning rate passed to the optimizer. You may use
lr
instead of usingoptimizer__lr
, which would result in the same outcome.- max_epochs : int (default=10)
The number of epochs to train for each
fit
call. Note that you may keyboard-interrupt training at any time.- batch_size : int (default=128)
Mini-batch size. Use this instead of setting
iterator_train__batch_size
anditerator_test__batch_size
, which would result in the same outcome. Ifbatch_size
is -1, a single batch with all the data will be used during training and validation.- iterator_train : torch DataLoader
The default PyTorch
DataLoader
used for training data.- iterator_valid : torch DataLoader
The default PyTorch
DataLoader
used for validation and test data, i.e. during inference.- dataset : torch Dataset (default=skorch.dataset.Dataset)
The dataset is necessary for the incoming data to work with pytorch’s
DataLoader
. It has to implement the__len__
and__getitem__
methods. The provided dataset should be capable of dealing with a lot of data types out of the box, so only change this if your data is not supported. You should generally pass the uninitializedDataset
class and define additional arguments to X and y by prefixing them withdataset__
. It is also possible to pass an initialzedDataset
, in which case no additional arguments may be passed.- train_split : None or callable (default=skorch.dataset.CVSplit(5))
If None, there is no train/validation split. Else, train_split should be a function or callable that is called with X and y data and should return the tuple
dataset_train, dataset_valid
. The validation data may be None.- callbacks : None, “disable”, or list of Callback instances (default=None)
Which callbacks to enable. There are three possible values:
If
callbacks=None
, only use default callbacks, those returned byget_default_callbacks
.If
callbacks="disable"
, disable all callbacks, i.e. do not run any of the callbacks.If
callbacks
is a list of callbacks, use those callbacks in addition to the default callbacks. Each callback should be an instance ofCallback
.Callback names are inferred from the class name. Name conflicts are resolved by appending a count suffix starting with 1, e.g.
EpochScoring_1
. Alternatively, a tuple(name, callback)
can be passed, wherename
should be unique. Callbacks may or may not be instantiated. The callback name can be used to set parameters on specific callbacks (e.g., for the callback with name'print_log'
, usenet.set_params(callbacks__print_log__keys_ignored=['epoch', 'train_loss'])
).- predict_nonlinearity : callable, None, or ‘auto’ (default=’auto’)
The nonlinearity to be applied to the prediction. When set to ‘auto’, infers the correct nonlinearity based on the criterion (softmax for
CrossEntropyLoss
and sigmoid forBCEWithLogitsLoss
). If it cannot be inferred or if the parameter is None, just use the identity function. Don’t pass a lambda function if you want the net to be pickleable.In case a callable is passed, it should accept the output of the module (the first output if there is more than one), which is a PyTorch tensor, and return the transformed PyTorch tensor.
This can be useful, e.g., when
predict_proba()
should return probabilities but a criterion is used that does not expect probabilities. In that case, the module can return whatever is required by the criterion and thepredict_nonlinearity
transforms this output into probabilities.The nonlinearity is applied only when calling
predict()
orpredict_proba()
but not anywhere else – notably, the loss is unaffected by this nonlinearity.- warm_start : bool (default=False)
Whether each fit call should lead to a re-initialization of the module (cold start) or whether the module should be trained further (warm start).
- verbose : int (default=1)
This parameter controls how much print output is generated by the net and its callbacks. By setting this value to 0, e.g. the summary scores at the end of each epoch are no longer printed. This can be useful when running a hyperparameter search. The summary scores are always logged in the history attribute, regardless of the verbose setting.
- device : str, torch.device (default=’cpu’)
The compute device to be used. If set to ‘cuda’, data in torch tensors will be pushed to cuda tensors before being sent to the module. If set to None, then all compute devices will be left unmodified.
Attributes: - prefixes_ : list of str
Contains the prefixes to special parameters. E.g., since there is the
'module'
prefix, it is possible to set parameters like so:NeuralNet(..., optimizer__momentum=0.95)
.- cuda_dependent_attributes_ : list of str
Contains a list of all attribute prefixes whose values depend on a CUDA device. If a
NeuralNet
trained with a CUDA-enabled device is unpickled on a machine without CUDA or with CUDA disabled, the listed attributes are mapped to CPU. Expand this list if you want to add other cuda-dependent attributes.- initialized_ : bool
Whether the
NeuralNet
was initialized.- module_ : torch module (instance)
The instantiated module.
- criterion_ : torch criterion (instance)
The instantiated criterion.
- callbacks_ : list of tuples
The complete (i.e. default and other), initialized callbacks, in a tuple with unique names.
Methods
check_is_fitted
([attributes])Checks whether the net is initialized evaluation_step
(Xi[, training])Perform a forward step to produce the output used for prediction and scoring. fit
(X[, y])Initialize and fit the module. fit_loop
(X[, y, epochs])The proper fit loop. forward
(X[, training, device])Gather and concatenate the output from forward call with input data. forward_iter
(X[, training, device])Yield outputs of module forward calls on each batch of data. get_dataset
(X[, y])Get a dataset that contains the input data and is passed to the iterator. get_iterator
(dataset[, training])Get an iterator that allows to loop over the batches of the given data. get_loss
(y_pred, y_true[, X, training])Return the loss for this batch. get_params_for
(prefix)Collect and return init parameters for an attribute. get_params_for_optimizer
(prefix, …)Collect and return init parameters for an optimizer. get_split_datasets
(X[, y])Get internal train and validation datasets. get_train_step_accumulator
()Return the train step accumulator. infer
(x, **fit_params)Perform a single inference step on a batch of data. initialize
()Initializes all components of the NeuralNet
and returns self.initialize_callbacks
()Initializes all callbacks and save the result in the callbacks_
attribute.initialize_criterion
()Initializes the criterion. initialize_history
()Initializes the history. initialize_module
()Initializes the module. initialize_optimizer
([triggered_directly])Initialize the model optimizer. load_params
([f_params, f_optimizer, …])Loads the the module’s parameters, history, and optimizer, not the whole object. notify
(method_name, **cb_kwargs)Call the callback method specified in method_name
with parameters specified incb_kwargs
.on_batch_begin
(net[, Xi, yi, training])on_epoch_begin
(net[, dataset_train, …])on_epoch_end
(net[, dataset_train, dataset_valid])on_train_begin
(net[, X, y])on_train_end
(net[, X, y])partial_fit
(X[, y, classes])Fit the module. predict
(X)Where applicable, return class labels for samples in X. predict_proba
(X)Return the output of the module’s forward method as a numpy array. run_single_epoch
(dataset, training, prefix, …)Compute a single epoch of train or validation. save_params
([f_params, f_optimizer, …])Saves the module’s parameters, history, and optimizer, not the whole object. set_params
(**kwargs)Set the parameters of this class. train_step
(Xi, yi, **fit_params)Prepares a loss function callable and pass it to the optimizer, hence performing one optimization step. train_step_single
(Xi, yi, **fit_params)Compute y_pred, loss value, and update net’s gradients. validation_step
(Xi, yi, **fit_params)Perform a forward step using batched data and return the resulting loss. check_data get_default_callbacks get_params initialize_virtual_params on_batch_end on_grad_computed -
check_is_fitted
(attributes=None, *args, **kwargs)[source]¶ Checks whether the net is initialized
Parameters: - attributes : iterable of str or None (default=None)
All the attributes that are strictly required of a fitted net. By default, this is the module_ attribute.
- Other arguments as in
- ``sklearn.utils.validation.check_is_fitted``.
Raises: - skorch.exceptions.NotInitializedError
When the given attributes are not present.
-
evaluation_step
(Xi, training=False)[source]¶ Perform a forward step to produce the output used for prediction and scoring.
Therefore the module is set to evaluation mode by default beforehand which can be overridden to re-enable features like dropout by setting
training=True
.
-
fit
(X, y=None, **fit_params)[source]¶ Initialize and fit the module.
If the module was already initialized, by calling fit, the module will be re-initialized (unless
warm_start
is True).Parameters: - X : input data, compatible with skorch.dataset.Dataset
By default, you should be able to pass:
- numpy arrays
- torch tensors
- pandas DataFrame or Series
- scipy sparse CSR matrices
- a dictionary of the former three
- a list/tuple of the former three
- a Dataset
If this doesn’t work with your data, you have to pass a
Dataset
that can deal with the data.- y : target data, compatible with skorch.dataset.Dataset
The same data types as for
X
are supported. If your X is a Dataset that contains the target,y
may be set to None.- **fit_params : dict
Additional parameters passed to the
forward
method of the module and to theself.train_split
call.
-
fit_loop
(X, y=None, epochs=None, **fit_params)[source]¶ The proper fit loop.
Contains the logic of what actually happens during the fit loop.
Parameters: - X : input data, compatible with skorch.dataset.Dataset
By default, you should be able to pass:
- numpy arrays
- torch tensors
- pandas DataFrame or Series
- scipy sparse CSR matrices
- a dictionary of the former three
- a list/tuple of the former three
- a Dataset
If this doesn’t work with your data, you have to pass a
Dataset
that can deal with the data.- y : target data, compatible with skorch.dataset.Dataset
The same data types as for
X
are supported. If your X is a Dataset that contains the target,y
may be set to None.- epochs : int or None (default=None)
If int, train for this number of epochs; if None, use
self.max_epochs
.- **fit_params : dict
Additional parameters passed to the
forward
method of the module and to theself.train_split
call.
-
forward
(X, training=False, device='cpu')[source]¶ Gather and concatenate the output from forward call with input data.
The outputs from
self.module_.forward
are gathered on the compute device specified bydevice
and then concatenated using PyTorchcat()
. If multiple outputs are returned byself.module_.forward
, each one of them must be able to be concatenated this way.Parameters: - X : input data, compatible with skorch.dataset.Dataset
By default, you should be able to pass:
- numpy arrays
- torch tensors
- pandas DataFrame or Series
- scipy sparse CSR matrices
- a dictionary of the former three
- a list/tuple of the former three
- a Dataset
If this doesn’t work with your data, you have to pass a
Dataset
that can deal with the data.- training : bool (default=False)
Whether to set the module to train mode or not.
- device : string (default=’cpu’)
The device to store each inference result on. This defaults to CPU memory since there is genereally more memory available there. For performance reasons this might be changed to a specific CUDA device, e.g. ‘cuda:0’.
Returns: - y_infer : torch tensor
The result from the forward step.
-
forward_iter
(X, training=False, device='cpu')[source]¶ Yield outputs of module forward calls on each batch of data. The storage device of the yielded tensors is determined by the
device
parameter.Parameters: - X : input data, compatible with skorch.dataset.Dataset
By default, you should be able to pass:
- numpy arrays
- torch tensors
- pandas DataFrame or Series
- scipy sparse CSR matrices
- a dictionary of the former three
- a list/tuple of the former three
- a Dataset
If this doesn’t work with your data, you have to pass a
Dataset
that can deal with the data.- training : bool (default=False)
Whether to set the module to train mode or not.
- device : string (default=’cpu’)
The device to store each inference result on. This defaults to CPU memory since there is genereally more memory available there. For performance reasons this might be changed to a specific CUDA device, e.g. ‘cuda:0’.
Yields: - yp : torch tensor
Result from a forward call on an individual batch.
-
get_dataset
(X, y=None)[source]¶ Get a dataset that contains the input data and is passed to the iterator.
Override this if you want to initialize your dataset differently.
Parameters: - X : input data, compatible with skorch.dataset.Dataset
By default, you should be able to pass:
- numpy arrays
- torch tensors
- pandas DataFrame or Series
- scipy sparse CSR matrices
- a dictionary of the former three
- a list/tuple of the former three
- a Dataset
If this doesn’t work with your data, you have to pass a
Dataset
that can deal with the data.- y : target data, compatible with skorch.dataset.Dataset
The same data types as for
X
are supported. If your X is a Dataset that contains the target,y
may be set to None.
Returns: - dataset
The initialized dataset.
-
get_iterator
(dataset, training=False)[source]¶ Get an iterator that allows to loop over the batches of the given data.
If
self.iterator_train__batch_size
and/orself.iterator_test__batch_size
are not set, useself.batch_size
instead.Parameters: - dataset : torch Dataset (default=skorch.dataset.Dataset)
Usually,
self.dataset
, initialized with the corresponding data, is passed toget_iterator
.- training : bool (default=False)
Whether to use
iterator_train
oriterator_test
.
Returns: - iterator
An instantiated iterator that allows to loop over the mini-batches.
-
get_loss
(y_pred, y_true, X=None, training=False)[source]¶ Return the loss for this batch.
Parameters: - y_pred : torch tensor
Predicted target values
- y_true : torch tensor
True target values.
- X : input data, compatible with skorch.dataset.Dataset
By default, you should be able to pass:
- numpy arrays
- torch tensors
- pandas DataFrame or Series
- scipy sparse CSR matrices
- a dictionary of the former three
- a list/tuple of the former three
- a Dataset
If this doesn’t work with your data, you have to pass a
Dataset
that can deal with the data.- training : bool (default=False)
Whether train mode should be used or not.
-
get_params_for
(prefix)[source]¶ Collect and return init parameters for an attribute.
Attributes could be, for instance, pytorch modules, criteria, or data loaders (for optimizers, use
get_params_for_optimizer()
instead). Use the returned arguments to initialize the given attribute like this:# inside initialize_module method kwargs = self.get_params_for('module') self.module_ = self.module(**kwargs)
Proceed analogously for the criterion etc.
The reason to use this method is so that it’s possible to change the init parameters with
set_params()
, which in turn makes grid search and other similar things work.Note that in general, as a user, you never have to deal with this method because
initialize_module()
etc. are already taking care of this. You only need to deal with this if you overrideinitialize_module()
(or similar methods) because you have some custom code that requires it.Parameters: - prefix : str
The name of the attribute whose arguments should be returned. E.g. for the module, it should be
'module'
.
Returns: - kwargs : dict
Keyword arguments to be used as init parameters.
-
get_params_for_optimizer
(prefix, named_parameters)[source]¶ Collect and return init parameters for an optimizer.
Parse kwargs configuration for the optimizer identified by the given prefix. Supports param group assignment using wildcards:
optimizer__lr=0.05, optimizer__param_groups=[ ('rnn*.period', {'lr': 0.3, 'momentum': 0}), ('rnn0', {'lr': 0.1}), ]
Generally, use this method like this:
# inside initialize_optimizer method named_params = self.module_.named_parameters() pgroups, kwargs = self.get_params_for_optimizer('optimizer', named_params) if 'lr' not in kwargs: kwargs['lr'] = self.lr self.optimizer_ = self.optimizer(*pgroups, **kwargs)
The reason to use this method is so that it’s possible to change the init parameters with
set_params()
, which in turn makes grid search and other similar things work.Note that in general, as a user, you never have to deal with this method because
initialize_optimizer()
is already taking care of this. You only need to deal with this if you overrideinitialize_optimizer()
because you have some custom code that requires it.Parameters: - prefix : str
The name of the optimizer whose arguments should be returned. Typically, this should just be
'optimizer'
. There can be exceptions, however, e.g. if you want to use more than one optimizer.- named_parameters : iterator
Iterator over the parameters of the module that is intended to be optimized. It’s the return value of
my_module.named_parameters()
.
Returns: - args : tuple
All positional arguments for this optimizer (right now only one, the parameter groups).
- kwargs : dict
All other parameters for this optimizer, e.g. the learning rate.
-
get_split_datasets
(X, y=None, **fit_params)[source]¶ Get internal train and validation datasets.
The validation dataset can be None if
self.train_split
is set to None; then internal validation will be skipped.Override this if you want to change how the net splits incoming data into train and validation part.
Parameters: - X : input data, compatible with skorch.dataset.Dataset
By default, you should be able to pass:
- numpy arrays
- torch tensors
- pandas DataFrame or Series
- scipy sparse CSR matrices
- a dictionary of the former three
- a list/tuple of the former three
- a Dataset
If this doesn’t work with your data, you have to pass a
Dataset
that can deal with the data.- y : target data, compatible with skorch.dataset.Dataset
The same data types as for
X
are supported. If your X is a Dataset that contains the target,y
may be set to None.- **fit_params : dict
Additional parameters passed to the
self.train_split
call.
Returns: - dataset_train
The initialized training dataset.
- dataset_valid
The initialized validation dataset or None
-
get_train_step_accumulator
()[source]¶ Return the train step accumulator.
By default, the accumulator stores and retrieves the first value from the optimizer call. Most optimizers make only one call, so first value is at the same time the only value.
In case of some optimizers, e.g. LBFGS,
train_step_calc_gradient
is called multiple times, as the loss function is evaluated multiple times per optimizer call. If you don’t want to return the first value in that case, override this method to return your custom accumulator.
-
infer
(x, **fit_params)[source]¶ Perform a single inference step on a batch of data.
Parameters: - x : input data
A batch of the input data.
- **fit_params : dict
Additional parameters passed to the
forward
method of the module and to theself.train_split
call.
-
initialize_callbacks
()[source]¶ Initializes all callbacks and save the result in the
callbacks_
attribute.Both
default_callbacks
andcallbacks
are used (in that order). Callbacks may either be initialized or not, and if they don’t have a name, the name is inferred from the class name. Theinitialize
method is called on all callbacks.The final result will be a list of tuples, where each tuple consists of a name and an initialized callback. If names are not unique, a ValueError is raised.
-
initialize_module
()[source]¶ Initializes the module.
Note that if the module has learned parameters, those will be reset.
-
initialize_optimizer
(triggered_directly=True)[source]¶ Initialize the model optimizer. If
self.optimizer__lr
is not set, useself.lr
instead.Parameters: - triggered_directly : bool (default=True)
Only relevant when optimizer is re-initialized. Initialization of the optimizer can be triggered directly (e.g. when lr was changed) or indirectly (e.g. when the module was re-initialized). If and only if the former happens, the user should receive a message informing them about the parameters that caused the re-initialization.
-
load_params
(f_params=None, f_optimizer=None, f_criterion=None, f_history=None, checkpoint=None, **kwargs)[source]¶ Loads the the module’s parameters, history, and optimizer, not the whole object.
To save and load the whole object, use pickle.
f_params
,f_optimizer
, etc. uses PyTorch’sload()
.If you’ve created a custom module, e.g.
net.mymodule_
, you can save that as well by passingf_mymodule
.Parameters: - f_params : file-like object, str, None (default=None)
Path of module parameters. Pass
None
to not load.- f_optimizer : file-like object, str, None (default=None)
Path of optimizer. Pass
None
to not load.- f_criterion : file-like object, str, None (default=None)
Path of criterion. Pass
None
to not save- f_history : file-like object, str, None (default=None)
Path to history. Pass
None
to not load.- checkpoint :
Checkpoint
, None (default=None) Checkpoint to load params from. If a checkpoint and a
f_*
path is passed in, thef_*
will be loaded. PassNone
to not load.
Examples
>>> before = NeuralNetClassifier(mymodule) >>> before.save_params(f_params='model.pkl', >>> f_optimizer='optimizer.pkl', >>> f_history='history.json') >>> after = NeuralNetClassifier(mymodule).initialize() >>> after.load_params(f_params='model.pkl', >>> f_optimizer='optimizer.pkl', >>> f_history='history.json')
-
notify
(method_name, **cb_kwargs)[source]¶ Call the callback method specified in
method_name
with parameters specified incb_kwargs
.Method names can be one of: * on_train_begin * on_train_end * on_epoch_begin * on_epoch_end * on_batch_begin * on_batch_end
-
partial_fit
(X, y=None, classes=None, **fit_params)[source]¶ Fit the module.
If the module is initialized, it is not re-initialized, which means that this method should be used if you want to continue training a model (warm start).
Parameters: - X : input data, compatible with skorch.dataset.Dataset
By default, you should be able to pass:
- numpy arrays
- torch tensors
- pandas DataFrame or Series
- scipy sparse CSR matrices
- a dictionary of the former three
- a list/tuple of the former three
- a Dataset
If this doesn’t work with your data, you have to pass a
Dataset
that can deal with the data.- y : target data, compatible with skorch.dataset.Dataset
The same data types as for
X
are supported. If your X is a Dataset that contains the target,y
may be set to None.- classes : array, sahpe (n_classes,)
Solely for sklearn compatibility, currently unused.
- **fit_params : dict
Additional parameters passed to the
forward
method of the module and to theself.train_split
call.
-
predict
(X)[source]¶ Where applicable, return class labels for samples in X.
If the module’s forward method returns multiple outputs as a tuple, it is assumed that the first output contains the relevant information and the other values are ignored. If all values are relevant, consider using
forward()
instead.Parameters: - X : input data, compatible with skorch.dataset.Dataset
By default, you should be able to pass:
- numpy arrays
- torch tensors
- pandas DataFrame or Series
- scipy sparse CSR matrices
- a dictionary of the former three
- a list/tuple of the former three
- a Dataset
If this doesn’t work with your data, you have to pass a
Dataset
that can deal with the data.
Returns: - y_pred : numpy ndarray
-
predict_proba
(X)[source]¶ Return the output of the module’s forward method as a numpy array.
If the module’s forward method returns multiple outputs as a tuple, it is assumed that the first output contains the relevant information and the other values are ignored. If all values are relevant, consider using
forward()
instead.Parameters: - X : input data, compatible with skorch.dataset.Dataset
By default, you should be able to pass:
- numpy arrays
- torch tensors
- pandas DataFrame or Series
- scipy sparse CSR matrices
- a dictionary of the former three
- a list/tuple of the former three
- a Dataset
If this doesn’t work with your data, you have to pass a
Dataset
that can deal with the data.
Returns: - y_proba : numpy ndarray
-
run_single_epoch
(dataset, training, prefix, step_fn, **fit_params)[source]¶ Compute a single epoch of train or validation.
Parameters: - dataset : torch Dataset
The initialized dataset to loop over.
- training : bool
Whether to set the module to train mode or not.
- prefix : str
Prefix to use when saving to the history.
- step_fn : callable
Function to call for each batch.
- **fit_params : dict
Additional parameters passed to the
step_fn
.
-
save_params
(f_params=None, f_optimizer=None, f_criterion=None, f_history=None, **kwargs)[source]¶ Saves the module’s parameters, history, and optimizer, not the whole object.
To save the whole object, use pickle. This is necessary when you need additional learned attributes on the net, e.g. the
classes_
attribute onskorch.classifier.NeuralNetClassifier
.f_params
,f_optimizer
, etc. use PyTorch’ssave()
.If you’ve created a custom module, e.g.
net.mymodule_
, you can save that as well by passingf_mymodule
.Parameters: - f_params : file-like object, str, None (default=None)
Path of module parameters. Pass
None
to not save- f_optimizer : file-like object, str, None (default=None)
Path of optimizer. Pass
None
to not save- f_criterion : file-like object, str, None (default=None)
Path of criterion. Pass
None
to not save- f_history : file-like object, str, None (default=None)
Path to history. Pass
None
to not save
Examples
>>> before = NeuralNetClassifier(mymodule) >>> before.save_params(f_params='model.pkl', ... f_optimizer='optimizer.pkl', ... f_history='history.json') >>> after = NeuralNetClassifier(mymodule).initialize() >>> after.load_params(f_params='model.pkl', ... f_optimizer='optimizer.pkl', ... f_history='history.json')
-
set_params
(**kwargs)[source]¶ Set the parameters of this class.
Valid parameter keys can be listed with
get_params()
.Returns: - self
-
train_step
(Xi, yi, **fit_params)[source]¶ Prepares a loss function callable and pass it to the optimizer, hence performing one optimization step.
Loss function callable as required by some optimizers (and accepted by all of them): https://pytorch.org/docs/master/optim.html#optimizer-step-closure
The module is set to be in train mode (e.g. dropout is applied).
Parameters: - Xi : input data
A batch of the input data.
- yi : target data
A batch of the target data.
- **fit_params : dict
Additional parameters passed to the
forward
method of the module and to the train_split call.
-
train_step_single
(Xi, yi, **fit_params)[source]¶ Compute y_pred, loss value, and update net’s gradients.
The module is set to be in train mode (e.g. dropout is applied).
Parameters: - Xi : input data
A batch of the input data.
- yi : target data
A batch of the target data.
- **fit_params : dict
Additional parameters passed to the
forward
method of the module and to theself.train_split
call.
-
validation_step
(Xi, yi, **fit_params)[source]¶ Perform a forward step using batched data and return the resulting loss.
The module is set to be in evaluation mode (e.g. dropout is not applied).
Parameters: - Xi : input data
A batch of the input data.
- yi : target data
A batch of the target data.
- **fit_params : dict
Additional parameters passed to the
forward
method of the module and to theself.train_split
call.