# # @title :probabilistic/prob_cifar/train_resnet_avb_pf.py # @author :ch # @contact :[email protected] # @created :01/30/2020 # @version :1.0 # @python_version :3.6.9 """ Train implicit posterior via AVB for prior-focused CIFAR-10/100 with Resnet-32 ------------------------------------------------------------------------------ The script :mod:`probabilistic.prob_cifar.train_resnet_avb_pf` is used to run a probabilistic CL experiment on CIFAR using a Resnet-32 (:class:`mnets.resnet.ResNet`) and Adversarial-Variational-Bayes (AVB) as method to learn a single posterior for all tasks sequentially. At the moment, it simply takes care of providing the correct command-line arguments and default values to the end user. Afterwards, it will simply call: :mod:`probabilistic.prob_cifar.train_avb`. """ # Do not delete the following import for all executable scripts! import __init__ # pylint: disable=unused-import from probabilistic.prob_mnist import train_args from probabilistic.prob_cifar import train_avb if __name__ == '__main__': config = train_args.parse_cmd_arguments(mode='cifar_resnet_avb_pf') train_avb.run(config, experiment='cifar_resnet_avb_pf')
# limitations under the License. # # @title :probabilistic/prob_mnist/train_split_avb_pf.py # @author :ch # @contact :[email protected] # @created :01/30/2020 # @version :1.0 # @python_version :3.6.9 """ Train implicit posterior via AVB for prior-focused SplitMNIST ---------------------------------------------------------------- The script :mod:`probabilistic.prob_mnist.train_split_avb_pf` is used to run experiments on SplitMNIST. At the moment, it simply takes care of providing the correct command-line arguments and default values to the end user while simply calling: :mod:`probabilistic.prob_cifar.train_avb`, which will train a single posterior for all tasks sequentially using the prior-focused CL approach (i.e., the posterior of the previous task becomes the prior of the current task). """ # Do not delete the following import for all executable scripts! import __init__ # pylint: disable=unused-import from probabilistic.prob_mnist import train_args from probabilistic.prob_cifar import train_avb if __name__ == '__main__': config = train_args.parse_cmd_arguments(mode='split_mnist_avb_pf') train_avb.run(config, experiment='split_mnist_avb_pf')
-----------------------------------------------------------------
In this script, we train a target network via variational inference, where the
variational family is NOT restricted to a set of Gaussian distributions with
diagonal covariance matrix (as in
:mod:`probabilistic.prob_mnist.train_bbb`).
For the training we use an implicit method, the training method for this case
is described in
Shi, Jiaxin, Shengyang Sun, and Jun Zhu. "A spectral approach to gradient
estimation for implicit distributions." ICML, 2018.
https://arxiv.org/abs/1806.02925
Specifically, we use a hypernetwork to output the weights for the target
network of each task in a continual learning setup, where tasks are presented
sequentially and forgetting of previous tasks is prevented by the
regularizer proposed in
https://arxiv.org/abs/1906.00695
"""
# Do not delete the following import for all executable scripts!
import __init__ # pylint: disable=unused-import
from probabilistic.prob_mnist import train_args
from probabilistic.prob_cifar import train_avb
if __name__ == '__main__':
config = train_args.parse_cmd_arguments(mode='split_mnist_ssge')
train_avb.run(config, experiment='split_mnist_ssge')
---------------------------------------------------------------------------
In this script, we train a target network via variational inference, where the
variational family is NOT restricted to a set of Gaussian distributions with
diagonal covariance matrix (as in
:mod:`probabilistic.prob_mnist.train_bbb`).
For the training we use an implicit method, the training method for this case
is described in
Shi, Jiaxin, Shengyang Sun, and Jun Zhu. "A spectral approach to gradient
estimation for implicit distributions." ICML, 2018.
https://arxiv.org/abs/1806.02925
Specifically, we use a hypernetwork to output the weights for the target
network of each task in a continual learning setup, where tasks are presented
sequentially and forgetting of previous tasks is prevented by the
regularizer proposed in
https://arxiv.org/abs/1906.00695
"""
# Do not delete the following import for all executable scripts!
import __init__ # pylint: disable=unused-import
from probabilistic.prob_mnist import train_args
from probabilistic.prob_cifar import train_avb
if __name__ == '__main__':
config = train_args.parse_cmd_arguments(mode='cifar_resnet_ssge')
train_avb.run(config, experiment='cifar_resnet_ssge')
# Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # @title :probabilistic/prob_mnist/train_perm_avb_pf.py # @author :ch # @contact :[email protected] # @created :01/30/2020 # @version :1.0 # @python_version :3.6.9 """ Train implicit posterior via AVB for prior-focused PermutedMNIST ---------------------------------------------------------------- The script :mod:`probabilistic.prob_mnist.train_perm_avb_pf` is used to run experiments on PermutedMNIST. It's role is analogous to the one of the script :mod:`probabilistic.prob_mnist.train_split_avb_pf`. """ # Do not delete the following import for all executable scripts! import __init__ # pylint: disable=unused-import from probabilistic.prob_mnist import train_args from probabilistic.prob_cifar import train_avb if __name__ == '__main__': config = train_args.parse_cmd_arguments(mode='perm_mnist_avb_pf') train_avb.run(config, experiment='perm_mnist_avb_pf')
# limitations under the License. # # @title :probabilistic/prob_cifar/train_zenke_avb_pf.py # @author :ch # @contact :[email protected] # @created :01/30/2020 # @version :1.0 # @python_version :3.6.9 """ Train implicit posterior via AVB for prior-focused CIFAR-10/100 with ZenkeNet ----------------------------------------------------------------------------- The script :mod:`probabilistic.prob_cifar.train_resnet_avb_pf` is used to run a probabilistic CL experiment on CIFAR using a ZenkeNet (:class:`mnets.zenkenet.ZenkeNet`) and Adversarial-Variational-Bayes (AVB) as method to learn a single posterior for all tasks sequentially. At the moment, it simply takes care of providing the correct command-line arguments and default values to the end user. Afterwards, it will simply call: :mod:`probabilistic.prob_cifar.train_avb`. """ # Do not delete the following import for all executable scripts! import __init__ # pylint: disable=unused-import from probabilistic.prob_mnist import train_args from probabilistic.prob_cifar import train_avb if __name__ == '__main__': config = train_args.parse_cmd_arguments(mode='cifar_zenke_avb_pf') train_avb.run(config, experiment='cifar_zenke_avb_pf')
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # @title :probabilistic/prob_gmm/train_gmm_ssge_pf.py # @author :ch # @contact :[email protected] # @created :03/10/2020 # @version :1.0 # @python_version :3.6.10 """ Train implicit posterior via SSGE for prior-focused CL with GMM tasks --------------------------------------------------------------------- The script :mod:`probabilistic.prob_gmm.train_gmm_ssge_pf` is used to run a probabilistic CL experiment on a toy classification problem using synthetic data (:class:`data.special.GMMData`). Spectral Stein Gradient Estimator (SSGE) is used to learn a single posterior for all tasks sequentially. """ # Do not delete the following import for all executable scripts! import __init__ # pylint: disable=unused-import from probabilistic.prob_cifar import train_avb from probabilistic.prob_mnist import train_args if __name__ == '__main__': config = train_args.parse_cmd_arguments(mode='gmm_ssge_pf') train_avb.run(config, experiment='gmm_ssge_pf')
-----------------------------------------------------------------
In this script, we train a target network via variational inference, where the
variational family is NOT restricted to a set of Gaussian distributions with
diagonal covariance matrix (as in
:mod:`probabilistic.prob_mnist.train_bbb`).
For the training we use an implicit method, the training method for this case
is described in
Shi, Jiaxin, Shengyang Sun, and Jun Zhu. "A spectral approach to gradient
estimation for implicit distributions." ICML, 2018.
https://arxiv.org/abs/1806.02925
Specifically, we use a hypernetwork to output the weights for the target
network of each task in a continual learning setup, where tasks are presented
sequentially and forgetting of previous tasks is prevented by the
regularizer proposed in
https://arxiv.org/abs/1906.00695
"""
# Do not delete the following import for all executable scripts!
import __init__ # pylint: disable=unused-import
from probabilistic.prob_mnist import train_args
from probabilistic.prob_cifar import train_avb
if __name__ == '__main__':
config = train_args.parse_cmd_arguments(mode='perm_mnist_ssge')
train_avb.run(config, experiment='perm_mnist_ssge')
# @title :probabilistic/prob_gmm/train_gmm_avb.py # @author :ch # @contact :[email protected] # @created :03/10/2020 # @version :1.0 # @python_version :3.6.10 """ Train implicit per-task posteriors for GMM tasks with AVB --------------------------------------------------------- The script :mod:`probabilistic.prob_gmm.train_gmm_avb` is used to run a probabilistic CL experiment on a toy classification problem using synthetic data (:class:`data.special.GMMData`). Adversarial-Variational-Bayes (AVB) is used to learn task-specific weight posteriors. At the moment, the script simply takes care of providing the correct command-line arguments and default values to the end user. Afterwards, it will simply call: :mod:`probabilistic.prob_mnist.train_avb`. See :ref:`prob-gmm-avb-readme-reference-label` for usage instructions. """ # Do not delete the following import for all executable scripts! import __init__ # pylint: disable=unused-import from probabilistic.prob_cifar import train_avb from probabilistic.prob_mnist import train_args if __name__ == '__main__': config = train_args.parse_cmd_arguments(mode='gmm_avb') train_avb.run(config, experiment='gmm_avb')