adapt.parameter_based.RegularTransferNN

class adapt.parameter_based.RegularTransferNN(task=None, Xt=None, yt=None, lambdas=1.0, regularizer='l2', verbose=1, copy=True, random_state=None, **params)

Regular Transfer with Neural Network

RegularTransferNN is a parameter-based domain adaptation method.

The method is based on the assumption that a good target estimator can be obtained by adapting the parameters of a pre-trained source estimator using a few labeled target data.

The approach consist in fitting a neural network on target data according to an objective function regularized by the euclidean distance between source and target parameters:

\[\beta_T = \underset{\beta=(\beta_1, ... , \beta_D)}{\text{argmin}} \, ||f(X_T, \beta) - y_T||^2 + \sum_{i=1}^{D} \lambda_i ||\beta_i - {\beta_S}_i||^2\]

Where:

• \(f\) is a neural network with \(D\) layers.

• \(\beta_T\) are the parameters of the target neural network.

• \(\beta_S = \underset{\beta}{\text{argmin}} \, ||f(X_S,\beta) - y_S||^2\) are the source neural network parameters.

• \((X_S, y_S), (X_T, y_T)\) are respectively the source and the target labeled data.

• \(\lambda_i\) is the trade-off parameter of layer \(i\).

Different trade-off can be given to the layer of the neural network through the lambdas parameter.

Parameters

tasktensorflow Model (default=None)

Task netwok. If None, a two layers network with 10 neurons per layer and ReLU activation is used as task network.

Xtnumpy array (default=None)

Target input data.

ytnumpy array (default=None)

Target output data.

lambdasfloat or list of float, optional (default=1.0)

Trade-off parameters. If a list is given, values from lambdas are assigned successively to the list of network layers with weights parameters going from the last layer to the first one. If the length of lambdas is smaller than the length of network layers list, the last trade-off value will be asigned to the remaining layers.

copyboolean (default=True)

Whether to make a copy of estimator or not.

verboseint (default=1)

Verbosity level.

random_stateint (default=None)

Seed of random generator.

paramskey, value arguments

Arguments given at the different level of the adapt object. It can be, for instance, compile or fit parameters of the estimator or kernel parameters etc… Accepted parameters can be found by calling the method _get_legal_params(params).

Yields

optimizerstr or instance of tf.keras.optimizers (default=”rmsprop”)

Optimizer for the task. It should be an instance of tf.keras.optimizers as: tf.keras.optimizers.SGD(0.001) or tf.keras.optimizers.Adam(lr=0.001, beta_1=0.5). A string can also be given as "adam". Default optimizer is rmsprop.

lossstr or instance of tf.keras.losses (default=”mse”)

Loss for the task. It should be an instance of tf.keras.losses as: tf.keras.losses.MeanSquaredError() or tf.keras.losses.CategoricalCrossentropy(). A string can also be given as "mse" or categorical_crossentropy. Default loss is mse.

metricslist of str or list of tf.keras.metrics.Metric instance

List of metrics to be evaluated by the model during training and testing. Typically you will use metrics=['accuracy'].

optimizer_encstr or instance of tf.keras.optimizers

If the Adapt Model has an encoder attribute, a specific optimizer for the encoder network can be given. Typically, this parameter can be used to give a smaller learning rate to the encoder. If not specified, optimizer_enc=optimizer.

optimizer_discstr or instance of tf.keras.optimizers

If the Adapt Model has a discriminator attribute, a specific optimizer for the discriminator network can be given. If not specified, optimizer_disc=optimizer.

kwargskey, value arguments

Any arguments of the fit method from the Tensorflow Model can be given, as epochs and batch_size. Specific arguments from optimizer can also be given as learning_rate or beta_1 for Adam. This allows to perform GridSearchCV from scikit-learn on these arguments.

See also

RegularTransferLR, RegularTransferLC

References

1

[1] C. Chelba and A. Acero. “Adaptation of maximum entropy classifier: Little data can help a lot”. In EMNLP, 2004.

Examples

>>> from adapt.utils import make_regression_da
>>> from adapt.parameter_based import RegularTransferNN
>>> Xs, ys, Xt, yt = make_regression_da()
>>> src_model = RegularTransferNN(loss="mse", lambdas=0., random_state=0)
>>> src_model.fit(Xs, ys, epochs=100, verbose=0)
>>> print(src_model.score(Xt, yt))
1/1 [==============================] - 0s 127ms/step - loss: 0.2744
0.27443504333496094
>>> model = RegularTransferNN(src_model.task_, loss="mse", lambdas=1., random_state=0)
>>> model.fit(Xt[:3], yt[:3], epochs=100, verbose=0)
>>> model.score(Xt, yt)
1/1 [==============================] - 0s 109ms/step - loss: 0.0832
0.08321201056241989

Attributes

task_tensorflow Model

Network.

history_dict

history of the losses and metrics across the epochs of the network training.

Methods

__init__([task, Xt, yt, lambdas, ...])
compile([optimizer, loss, metrics, ...])	Configures the model for training.
fit([Xt, yt])	Fit RegularTransferNN.
get_params([deep])	Get parameters for this estimator.
load_weights(filepath[, skip_mismatch, ...])	Loads all layer weights from a saved files.
predict(x[, batch_size, verbose, steps, ...])	Generates output predictions for the input samples.
predict_disc(X)	Not used.
predict_task(X)	Return predictions of the task on the encoded features.
save_weights(filepath[, overwrite, ...])	Saves all layer weights.
score(X, y[, sample_weight])	Return the evaluation of the model on X, y.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Return X
unsupervised_score(Xs, Xt)	Return unsupervised score.

__init__(task=None, Xt=None, yt=None, lambdas=1.0, regularizer='l2', verbose=1, copy=True, random_state=None, **params)

compile(optimizer=None, loss=None, metrics=None, loss_weights=None, weighted_metrics=None, run_eagerly=None, steps_per_execution=None, **kwargs)

Configures the model for training.

Parameters

optimizer: str or `tf.keras.optimizer` instance

Optimizer

loss: str or `tf.keras.losses.Loss` instance

Loss function. A loss function is any callable with the signature loss = fn(y_true, y_pred), where y_true are the ground truth values, and y_pred are the model’s predictions. y_true should have shape (batch_size, d0, .. dN) (except in the case of sparse loss functions such as sparse categorical crossentropy which expects integer arrays of shape (batch_size, d0, .. dN-1)). y_pred should have shape (batch_size, d0, .. dN). The loss function should return a float tensor. If a custom Loss instance is used and reduction is set to None, return value has shape (batch_size, d0, .. dN-1) i.e. per-sample or per-timestep loss values; otherwise, it is a scalar. If the model has multiple outputs, you can use a different loss on each output by passing a dictionary or a list of losses. The loss value that will be minimized by the model will then be the sum of all individual losses, unless loss_weights is specified.

metrics: list of str or list of `tf.keras.metrics.Metric` instance

List of metrics to be evaluated by the model during training and testing. Typically you will use metrics=[‘accuracy’]. A function is any callable with the signature result = fn(y_true, y_pred). To specify different metrics for different outputs of a multi-output model, you could also pass a dictionary, such as metrics={‘output_a’: ‘accuracy’, ‘output_b’: [‘accuracy’, ‘mse’]}. You can also pass a list to specify a metric or a list of metrics for each output, such as metrics=[[‘accuracy’], [‘accuracy’, ‘mse’]] or metrics=[‘accuracy’, [‘accuracy’, ‘mse’]]. When you pass the strings ‘accuracy’ or ‘acc’, we convert this to one of tf.keras.metrics.BinaryAccuracy, tf.keras.metrics.CategoricalAccuracy, tf.keras.metrics.SparseCategoricalAccuracy based on the loss function used and the model output shape. We do a similar conversion for the strings ‘crossentropy’ and ‘ce’ as well.

loss_weights: List or dict of floats

Scalars to weight the loss contributions of different model outputs. The loss value that will be minimized by the model will then be the weighted sum of all individual losses, weighted by the loss_weights coefficients. If a list, it is expected to have a 1:1 mapping to the model’s outputs. If a dict, it is expected to map output names (strings) to scalar coefficients.

weighted_metrics: list of metrics

List of metrics to be evaluated and weighted by sample_weight or class_weight during training and testing.

run_eagerly: bool (default=False)

If True, this Model’s logic will not be wrapped in a tf.function. Recommended to leave this as None unless your Model cannot be run inside a tf.function. run_eagerly=True is not supported when using tf.distribute.experimental.ParameterServerStrategy.

steps_per_execution: int (default=1)

The number of batches to run during each tf.function call. Running multiple batches inside a single tf.function call can greatly improve performance on TPUs or small models with a large Python overhead. At most, one full epoch will be run each execution. If a number larger than the size of the epoch is passed, the execution will be truncated to the size of the epoch. Note that if steps_per_execution is set to N, Callback.on_batch_begin and Callback.on_batch_end methods will only be called every N batches (i.e. before/after each tf.function execution).

**kwargs: key, value arguments

Arguments supported for backwards compatibility only.

Returns

None: None

fit(Xt=None, yt=None, **fit_params)

Fit RegularTransferNN.

Parameters

Xtnumpy array (default=None)

Target input data.

ytnumpy array (default=None)

Target output data.

fit_paramskey, value arguments

Arguments given to the fit method of the model (epochs, batch_size, callbacks…).

Returns

selfreturns an instance of self

get_params(deep=True)

Get parameters for this estimator.

Parameters

deepbool, default=True

Not used, here for scikit-learn compatibility.

Returns

paramsdict

Parameter names mapped to their values.

load_weights(filepath, skip_mismatch=False, by_name=False, options=None)

Loads all layer weights from a saved files.

The saved file could be a SavedModel file, a .keras file (v3 saving format), or a file created via model.save_weights().

By default, weights are loaded based on the network’s topology. This means the architecture should be the same as when the weights were saved. Note that layers that don’t have weights are not taken into account in the topological ordering, so adding or removing layers is fine as long as they don’t have weights.

Partial weight loading

If you have modified your model, for instance by adding a new layer (with weights) or by changing the shape of the weights of a layer, you can choose to ignore errors and continue loading by setting skip_mismatch=True. In this case any layer with mismatching weights will be skipped. A warning will be displayed for each skipped layer.

Weight loading by name

If your weights are saved as a .h5 file created via model.save_weights(), you can use the argument by_name=True.

In this case, weights are loaded into layers only if they share the same name. This is useful for fine-tuning or transfer-learning models where some of the layers have changed.

Note that only topological loading (by_name=False) is supported when loading weights from the .keras v3 format or from the TensorFlow SavedModel format.

Args:

filepath: String, path to the weights file to load. For weight files

in TensorFlow format, this is the file prefix (the same as was passed to save_weights()). This can also be a path to a SavedModel or a .keras file (v3 saving format) saved via model.save().

skip_mismatch: Boolean, whether to skip loading of layers where

there is a mismatch in the number of weights, or a mismatch in the shape of the weights.

by_name: Boolean, whether to load weights by name or by topological

order. Only topological loading is supported for weight files in the .keras v3 format or in the TensorFlow SavedModel format.

options: Optional tf.train.CheckpointOptions object that specifies

options for loading weights (only valid for a SavedModel file).

predict(x, batch_size=None, verbose=0, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False)

Generates output predictions for the input samples.

Computation is done in batches. This method is designed for performance in large scale inputs. For small amount of inputs that fit in one batch, directly using __call__() is recommended for faster execution, e.g., model(x), or model(x, training=False) if you have layers such as tf.keras.layers.BatchNormalization that behaves differently during inference. Also, note the fact that test loss is not affected by regularization layers like noise and dropout.

Parameters

x: array

Input samples.

batch_size: int (default=`None`)

Number of samples per batch. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of dataset, generators, or keras.utils.Sequence instances (since they generate batches).

verbose: int (default=0)

Verbosity mode, 0 or 1.

steps: int (default=None)

Total number of steps (batches of samples) before declaring the prediction round finished. Ignored with the default value of None. If x is a tf.data dataset and steps is None, predict() will run until the input dataset is exhausted.

callbacks: List of `keras.callbacks.Callback` instances.

List of callbacks to apply during prediction. See [callbacks](/api_docs/python/tf/keras/callbacks).

max_queue_size: int (default=10)

Used for generator or keras.utils.Sequence input only. Maximum size for the generator queue. If unspecified, max_queue_size will default to 10.

workers: int (default=1)

Used for generator or keras.utils.Sequence input only. Maximum number of processes to spin up when using process-based threading. If unspecified, workers will default to 1.

use_multiprocessing: bool (default=False)

Used for generator or keras.utils.Sequence input only. If True, use process-based threading. If unspecified, use_multiprocessing will default to False. Note that because this implementation relies on multiprocessing, you should not pass non-picklable arguments to the generator as they can’t be passed easily to children processes.

Returns

y_predarray

Numpy array(s) of predictions.

predict_disc(X)

Not used.

predict_task(X)

Return predictions of the task on the encoded features.

Parameters

Xarray

input data

Returns

y_taskarray

predictions of task network

save_weights(filepath, overwrite=True, save_format=None, options=None)

Saves all layer weights.

Either saves in HDF5 or in TensorFlow format based on the save_format argument.

When saving in HDF5 format, the weight file has:

• layer_names (attribute), a list of strings

  (ordered names of model layers).

• For every layer, a group named layer.name

  • For every such layer group, a group attribute weight_names,

    a list of strings (ordered names of weights tensor of the layer).

  • For every weight in the layer, a dataset

    storing the weight value, named after the weight tensor.

When saving in TensorFlow format, all objects referenced by the network are saved in the same format as tf.train.Checkpoint, including any Layer instances or Optimizer instances assigned to object attributes. For networks constructed from inputs and outputs using tf.keras.Model(inputs, outputs), Layer instances used by the network are tracked/saved automatically. For user-defined classes which inherit from tf.keras.Model, Layer instances must be assigned to object attributes, typically in the constructor. See the documentation of tf.train.Checkpoint and tf.keras.Model for details.

While the formats are the same, do not mix save_weights and tf.train.Checkpoint. Checkpoints saved by Model.save_weights should be loaded using Model.load_weights. Checkpoints saved using tf.train.Checkpoint.save should be restored using the corresponding tf.train.Checkpoint.restore. Prefer tf.train.Checkpoint over save_weights for training checkpoints.

The TensorFlow format matches objects and variables by starting at a root object, self for save_weights, and greedily matching attribute names. For Model.save this is the Model, and for Checkpoint.save this is the Checkpoint even if the Checkpoint has a model attached. This means saving a tf.keras.Model using save_weights and loading into a tf.train.Checkpoint with a Model attached (or vice versa) will not match the Model’s variables. See the [guide to training checkpoints]( https://www.tensorflow.org/guide/checkpoint) for details on the TensorFlow format.

Args:

filepath: String or PathLike, path to the file to save the weights

to. When saving in TensorFlow format, this is the prefix used for checkpoint files (multiple files are generated). Note that the ‘.h5’ suffix causes weights to be saved in HDF5 format.

overwrite: Whether to silently overwrite any existing file at the

target location, or provide the user with a manual prompt.

save_format: Either ‘tf’ or ‘h5’. A filepath ending in ‘.h5’ or

‘.keras’ will default to HDF5 if save_format is None. Otherwise, None becomes ‘tf’. Defaults to None.

options: Optional tf.train.CheckpointOptions object that specifies

options for saving weights.

Raises:

ImportError: If h5py is not available when attempting to save in

HDF5 format.

score(X, y, sample_weight=None)

Return the evaluation of the model on X, y.

Call evaluate on tensorflow Model.

Parameters

Xarray

input data

yarray

output data

sample_weightarray (default=None)

Sample weights

Returns

scorefloat

Score.

set_params(**params)

Set the parameters of this estimator.

Parameters

**paramsdict

Estimator parameters.

Returns

selfestimator instance

Estimator instance.

transform(X)

Return X

Parameters

Xarray

input data

Returns

X_encarray

predictions of encoder network

unsupervised_score(Xs, Xt)

Return unsupervised score.

The normalized discrepancy distance is computed between the reweighted/transformed source input data and the target input data.

Parameters

Xsarray

Source input data.

Xtarray

Source input data.

Returns

scorefloat

Unsupervised score.

Examples

Multi-Fidelity

Toy Regression