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<a name='optim.logger'></a>
# Logger
`optim` provides also logging and live plotting capabilities via the `optim.Logger()` function.
Live logging is essential to monitor the *network accuracy* and *cost function* during training and testing, for spotting *under-* and *over-fitting*, for *early stopping* or just for monitoring the health of the current optimisation task.
## Logging data
Let walk through an example to see how it works.
We start with initialising our logger connected to a text file `accuracy.log`.
```lua
logger = optim.Logger('accuracy.log')
```
We can decide to log on it, for example, *training* and *testing accuracies*.
```lua
logger:setNames{'Training acc.', 'Test acc.'}
```
And now we can populate our logger randomly.
```lua
for i = 1, 10 do
trainAcc = math.random(0, 100)
testAcc = math.random(0, 100)
logger:add{trainAcc, testAcc}
end
```
We can `cat` `accuracy.log` and see what's in it.
```
Training acc. Test acc.
7.0000e+01 5.9000e+01
7.6000e+01 8.0000e+00
6.6000e+01 3.4000e+01
7.4000e+01 4.3000e+01
5.7000e+01 1.1000e+01
5.0000e+00 9.8000e+01
7.1000e+01 1.7000e+01
9.8000e+01 2.7000e+01
3.5000e+01 4.7000e+01
6.8000e+01 5.8000e+01
```
## Visualising logs
OK, cool, but how can we actually see what's going on?
To have a better grasp of what's happening, we can plot our curves.
We need first to specify the plotting style, choosing from:
* `.` for dots
* `+` for points
* `-` for lines
* `+-` for points and lines
* `~` for using smoothed lines with cubic interpolation
* `|` for using boxes
* custom string, one can also pass custom strings to use full capability of gnuplot.
```lua
logger:style{'+-', '+-'}
logger:plot()
```
If we'd like an interactive visualisation, we can put the `logger:plot()` instruction within the `for` loop, and the chart will be updated at every iteration.
In case we'd like to prevent `gnuplot` to display the plots, we can set the option `logger:display(false)`.
In this way, plots will be saved but not displayed.
To restore the normal behaviour, use `logger:display(true)`.
We can set a logarithmic *y* axis with `logger:setlogscale(true)` and reset it with `logger:setlogscale(false)`.
|
