Tensorboard

This module contains Tensorboard monitor interface

class neural_pipeline.builtin.monitors.tensorboard.TensorboardMonitor(fsm: neural_pipeline.utils.fsm.FileStructManager, is_continue: bool, network_name: str = None)

Class, that manage metrics end events monitoring. It worked with tensorboard. Monitor get metrics after epoch ends and visualise it. Metrics may be float or np.array values. If metric is np.array - it will be shown as histogram and scalars (scalar plots contains mean valuse from array).

Parameters:

• fsm – file structure manager
• is_continue – is data processor continue training
• network_name – network name

update_losses(losses: {}) → None

Update monitor

Parameters:losses – losses values with keys ‘train’ and ‘validation’

update_metrics(metrics: {}) → None

Update monitor

Parameters:metrics – metrics dict with keys ‘metrics’ and ‘groups’

update_scalar(name: str, value: float, epoch_idx: int = None) → None

Update scalar on tensorboard

Parameters:

• name – the classic tag for TensorboardX
• value – scalar value
• epoch_idx – epoch idx. If doesn’t set - use last epoch idx stored in this class

visualize_model(model: neural_pipeline.data_processor.model.Model, tensor) → None

Visualize model graph

Parameters:

• model – torch.nn.Module object
• tensor – dummy input for trace model

write_to_txt_log(text: str, tag: str = None) → None

Write to txt log

Parameters:

• text – text that will be writed
• tag – tag

Matplotlib

This module contains Matplotlib monitor interface

class neural_pipeline.builtin.monitors.mpl.MPLMonitor

This monitor show all data in Matplotlib plots

realtime(is_realtime: bool) → neural_pipeline.builtin.monitors.mpl.MPLMonitor

Is need to show data updates in realtime

Parameters:is_realtime – is need realtime Returns:self object

update_losses(losses: {})

Update losses on monitor

Parameters:losses – losses values dict with keys is names of stages in train pipeline (e.g. [train, validation])

update_metrics(metrics: {}) → None

Update metrics on monitor

Parameters:metrics – metrics dict with keys ‘metrics’ and ‘groups’