Comments (8)
@duhaime Added skip_first
option (e.g. plot_losses = PlotLossesCallback(skip_first=2)
) with 6c88783. Should work with versions 0.4.0+
.
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@duhaime Added
skip_first
option (e.g.plot_losses = PlotLossesCallback(skip_first=2)
) with 6c88783. Should work with versions0.4.0+
.
When I try using skip_first
, I get __init__() got an unexpected keyword argument 'skip_first'
. I've tried all of these:
from livelossplot import PlotLossesKerasTF
from livelossplot.inputs.tf_keras import PlotLossesCallback
from livelossplot import PlotLosses
However, initializing gives the error above.
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Some of them might be beyond the scope of this package.
- add some light grid lines in the plot.
- allow us to add customized horizontal and vertical bars in the plot. It is very common for users to study if their model outperforms the baseline model and how fast their model converges.
- A countdown timer.
- live GPU utilization.
- maxima and minima
BTW, I attempted to plot the loss curve at batch-level granularity. I found it is easy to do so when training, but kinda difficult to do it in evaluation stage. Can you provide an example? I'm using PyTorch.
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add some light grid lines in the plot.
Use can set matplotlib style https://matplotlib.org/tutorials/introductory/customizing.html. For more/different styles, use https://seaborn.pydata.org/tutorial/aesthetics.html#seaborn-figure-styles.
allow us to add customized horizontal and vertical bars in the plot. It is very common for users to study if their model outperforms the baseline model and how fast their model converges.
Interesting idea. I will consider that.
A countdown timer.
Not sure. It only works if it knows the total number of batches/epochs.
live GPU utilization
You can add this data with in the update
. Though, you would need to use some library getting this data (I am not sure how to do it).
maxima and minima
Was thinking about it.
BTW, I attempted to plot the loss curve at batch-level granularity.
I don't recommend that. Plot-drawing is a costly step, and this thing is likely to severely impact performance. For evaluation - I am not sure if you want that. These plots work well for the same x axis for both train and eval.
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Hi @stared thanks for this great project!
One nice feature would be if one could pass the PlotLossesKeras constructor a parameter skip
that would skip the first n
frames. If a cost function drops multiple orders of magnitude, even the log scaled y axis can make it harder to see the learning rate. Skipping the first n frames would allow one to get around this problem...
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@duhaime A good idea, struggled with the same thing myself.
My idea was to specify some "rescaling" options, e.g. ignoring the first n
or taking the last n
. If you want to implement that, I would be more than happy to accept a Pull Request.
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The skip_first feature seemed removed after 0.5 release.
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The skip_first feature seemed removed after 0.5 release.
Is there a new feature added that does the same thing?
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Related Issues (20)
- KeyError : 'metrics' Please Help HOT 7
- How to plot live loss XGBOOST training? HOT 1
- Bug with latest keras HOT 3
- Bokeh vis not working in Colab HOT 2
- Can't pass arguments to PlotLossesKerasTF (figsize, fig_path) HOT 1
- Custom Message (Batch Size, etc) HOT 2
- How to plot multiple validation sets HOT 4
- Setup outputs as str HOT 6
- Make xlabel customizable HOT 4
- Sudden error that wasn't happen before - index 0 is out of bounds for axis 0 with size 0 HOT 1
- Positioning of legends HOT 1
- sliding start of epochs HOT 2
- WARNING : tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time HOT 2
- PlotLossesKeras stops if tensorflow.keras is used without installing traditional keras HOT 2
- No plots showing for utils.Sequence Generator model HOT 3
- how to plot by batches?
- 'Plot2d' object has no attribute 'set_output_mode' HOT 1
- Ability to pass `figsize` for matplotlib plots
- ImageDataGenerator incompatible
- Training pauses on epoch end and won't continue until window is closed
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