Start training

Now we can just start training process:

trainer.train()

That’s all. Console output will look like that:

Epoch: [1]; train: [0.004141, 1.046422, 3.884116]; validation: [0.002027, 0.304710, 2.673034]

Epoch: [2]; train: [0.000519, 0.249564, 4.938250]; validation: [0.000459, 0.200972, 2.594026]

Epoch: [3]; train: [0.000182, 0.180328, 5.218509]; validation: [0.000135, 0.155546, 2.512275]

train: 31%|███ | 4651/15000 [00:31<01:07, 154.06it/s, loss=[0.154871]]

First 3 lines is standard output of ConsoleMonitor. This monitor included for MonitorHub by default. Every line show loss values of correspondence stage in format [min, mean, max] values.

Last line build by tqdm and outcomes from TrainStage and ValidationStage. This output show current mean value of metrics on training stage.

Add Tensorboard monitor

For getting most useful information about training we can connect Tensorboard.

For do it we need before training connect builtin TensorboardMonitor to Trainer:

from neural_pipeline.builtin.monitors.tensorboard import TensorboardMonitor

trainer.monitor_hub.add_monitor(TensorboardMonitor(fsm, is_continue=False))

Now Tensorboard output will looks like: