diff --git a/Project/advanced_approach/README.assets/1532696294043.png b/Project/advanced_approach/README.assets/1532696294043.png new file mode 100644 index 0000000..ebe6a5a Binary files /dev/null and b/Project/advanced_approach/README.assets/1532696294043.png differ diff --git a/Project/advanced_approach/README.assets/1532696345413.png b/Project/advanced_approach/README.assets/1532696345413.png new file mode 100644 index 0000000..ad58f72 Binary files /dev/null and b/Project/advanced_approach/README.assets/1532696345413.png differ diff --git a/Project/advanced_approach/README.assets/1532696365808.png b/Project/advanced_approach/README.assets/1532696365808.png new file mode 100644 index 0000000..4b29f4f Binary files /dev/null and b/Project/advanced_approach/README.assets/1532696365808.png differ diff --git a/Project/advanced_approach/README.assets/1532696382476.png b/Project/advanced_approach/README.assets/1532696382476.png new file mode 100644 index 0000000..7396b44 Binary files /dev/null and b/Project/advanced_approach/README.assets/1532696382476.png differ diff --git a/Project/advanced_approach/README.md b/Project/advanced_approach/README.md index b5a0a07..e56a296 100644 --- a/Project/advanced_approach/README.md +++ b/Project/advanced_approach/README.md @@ -36,27 +36,29 @@ In short: the user interface for methods provided in [twitter_learning.py](#twit ### load datasets - +![1532696294043](README.assets/1532696294043.png) + + * setup the root folder for the json files containing our twitter samples * then you can set the range of files that will be loaded. Also you can setup more preprocessing steps, like top-emoji usage filtering or only load samples containing a specific set of emojis (given as string) ### create/save/load classifier - +![1532696345413](README.assets/1532696345413.png) * create a new keras pipeline including a vectorizer. you can setup the number of neurons and the activation function per layer (the last layer will be automatically adjusted to fit the label-dimension). if doc2vec is deactivated, tfidf is used. In the image above is the configuration we used for our final classifier. * you can also save a trained classifier and load it later again by a .pipeline configuration file (either by selecting them in the file selector box or by give the full path to the `clf_file` box) ### train classifier - +![1532696365808](README.assets/1532696365808.png) * if sample data and classifier are loaded, the classifier can be trained here ### Test single predictions - +![1532696382476](README.assets/1532696382476.png) * in the playground tab you can predict single sentences and get the nearest emoji in sentiment space of a given emoji set. Also you can plot the predictions of samples with given labels (as emoji) of the validation set of the currently loaded twitter data