gluonts.nursery.tsbench.src.tsbench.gluonts.callbacks package#
- class gluonts.nursery.tsbench.src.tsbench.gluonts.callbacks.Callback[source]#
Bases:
object
A stripped-down callback which is focused on batches rather than epochs.
- on_network_initialization_end(network: mxnet.gluon.block.HybridBlock) None [source]#
Hook called once the network is initialized.
- on_train_batch_end(network: mxnet.gluon.block.HybridBlock, time_elapsed: float) None [source]#
Hook called after every training batch.
- class gluonts.nursery.tsbench.src.tsbench.gluonts.callbacks.CallbackList(callbacks: List[gluonts.nursery.tsbench.src.tsbench.gluonts.callbacks.base.Callback])[source]#
Bases:
gluonts.nursery.tsbench.src.tsbench.gluonts.callbacks.base.Callback
Wrapper class for a list of callbacks.
- on_network_initialization_end(network: mxnet.gluon.block.HybridBlock) None [source]#
Hook called once the network is initialized.
- on_train_batch_end(network: mxnet.gluon.block.HybridBlock, time_elapsed: float) None [source]#
Hook called after every training batch.
- class gluonts.nursery.tsbench.src.tsbench.gluonts.callbacks.LearningRateScheduleCallback(milestones: List[float], decay: float = 0.5)[source]#
Bases:
gluonts.nursery.tsbench.src.tsbench.gluonts.callbacks.base.Callback
The learning rate schedule callback decreases the learning rate by a predefined factor after each of the provided milestones (after x seconds during training).
- class gluonts.nursery.tsbench.src.tsbench.gluonts.callbacks.ModelSaverCallback(directory: pathlib.Path, milestones: List[float])[source]#
Bases:
gluonts.nursery.tsbench.src.tsbench.gluonts.callbacks.base.Callback
The model saver callback saves the model during training at exponential frequency.
- network#
The network that was trained. Not available prior to training.
- saved_parameters#
The parameters saved for the different milestones. Should only be accessed after training has finished and should not be modified.
- training_times#
The training times in seconds for the different milestones.
- num_gradient_updates#
The number of gradient updates for the different milestones.
- on_network_initialization_end(network: mxnet.gluon.block.HybridBlock) None [source]#
Hook called once the network is initialized.
- class gluonts.nursery.tsbench.src.tsbench.gluonts.callbacks.ParameterCountCallback[source]#
Bases:
gluonts.nursery.tsbench.src.tsbench.gluonts.callbacks.base.Callback
This callback allows counting model parameters during training.
- num_parameters#
The number of parameters of the model. This attribute should only be accessed after training.