With Rasa Open Source 1.9, we added support for TensorBoard 2. TensorBoard provides visualizations and tooling for machine learning experiments. In Rasa Open Source 1.9 we use TensorBoard to visualize training metrics of our in-house built machine learning models on top of Tensorflow 2. Visualizing training metrics will help you to understand if your model has trained properly. You can, for example, see if your model needs more epochs or if a validation set is showing different behavior from the train set.
How to enable TensorBoard?
To enable TensorBoard you can now add a setting for a model in your
config.yml file. You can set this option for EmbeddingIntentClassifier, DIETClassifier, ResponseSelector or TEDPolicy. If a valid directory is provided, the training metrics will be written to that directory during training. By default we write the training metrics after every epoch but this is a setting you can tune. You can also specify a number of datapoints to be kept seperately for evaluation during training.
Let’s take a look at an example
The following config was used to train Sara.
language: en pipeline: - name: WhitespaceTokenizer - name: CRFEntityExtractor - name: CountVectorsFeaturizer OOV_token: "oov" token_pattern: (?u)\b\w+\b - name: CountVectorsFeaturizer analyzer: "char_wb" min_ngram: 1 max_ngram: 4 - name: DIETClassifier epochs: 200 evaluate_on_number_of_examples: 0 evaluate_every_number_of_epochs: 5 tensorboard_log_directory: "./tensorboard" tensorboard_log_level: "epoch" policies: - name: TEDPolicy max_history: 10 epochs: 200 evaluate_on_number_of_examples: 0 evaluate_every_number_of_epochs: 5 tensorboard_log_directory: "./tensorboard" tensorboard_log_level: "epoch" max_history: 6 - name: AugmentedMemoizationPolicy core_threshold: 0.3 name: TwoStageFallbackPolicy nlu_threshold: 0.8 - name: FormPolicy - name: MappingPolicy
Let's note a few things here.
- Note that all the tensorboard settings are part of the
TEDPolicycomponent. In particular, note the setting for the tensorboard log directory.
- Note that the
DIETClassifierstill has settings for the algorithm, independant of tensorboard. In particular, we will train for 200 epochs. Same goes for
- We specified that we want to evaluate our model every 5 epochs on a validation dataset that has 0 examples. The
evaluate_on_number_of_examplesparameter allows you to remove a random subset of examples from the training data to be used as a validation dataset. This parameter should be used with care. A larger validation set means that we have less data for training which can be bad for performance. This parameter is meant for playing around, not for production, which is why we've set it to zero here.
- We can change the log level to
minibatchif we want to zoom in.
After you trained your model, for example via
rasa train, all metrics are written to the provided directory. The directory will contain a subdirectory with the model name and another subdirectory with a timestamp. This allows you to reuse the same directory for multiple models and runs. To start TensorBoard execute the following command:
tensorboard --logdir <path-to-directory>
You can now open a browser at http://localhost:6006/ to see the training metrics. It should look something like this;
The orange curve corresponds to the hold out validation dataset and the blue curve shows the metrics for the training data (see legend on the left). If you use, for example the
DIETClassifier, you will see plots for the following metrics:
i is short for intent,
e for entities, and
t_loss shows the total loss. If you want to know more about what this means in terms of our DIET model, check out our youtube channel.
Let us know
TensorBoard is a new feature in Rasa and we might add further support for it. Until then we would love to hear your feedback and ideas of what else we could add to TensorBoard. You can leave feedback on our forum.