Tensorboard¶
This module contains Tensorboard monitor interface
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class
neural_pipeline.builtin.monitors.tensorboard.
TensorboardMonitor
(fsm: neural_pipeline.utils.fsm.FileStructManager, is_continue: bool, network_name: str = None)[source]¶ 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
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update_losses
(losses: {}) → None[source]¶ Update monitor
Parameters: losses – losses values with keys ‘train’ and ‘validation’
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update_metrics
(metrics: {}) → None[source]¶ Update monitor
Parameters: metrics – metrics dict with keys ‘metrics’ and ‘groups’
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update_scalar
(name: str, value: float, epoch_idx: int = None) → None[source]¶ 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
Matplotlib¶
This module contains Matplotlib monitor interface
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class
neural_pipeline.builtin.monitors.mpl.
MPLMonitor
[source]¶ This monitor show all data in Matplotlib plots
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realtime
(is_realtime: bool) → neural_pipeline.builtin.monitors.mpl.MPLMonitor[source]¶ Is need to show data updates in realtime
Parameters: is_realtime – is need realtime Returns: self object
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