fruit_360
statistics-fruit_360/fruit-360
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flowchart TD 0["ℹ️ Input Dataset\ntest_y_10.tsv"]; style 0 stroke:#2c3143,stroke-width:4px; 1["ℹ️ Input Dataset\ntrain_y_10.tsv"]; style 1 stroke:#2c3143,stroke-width:4px; 2["ℹ️ Input Dataset\ntrain_X_10.tsv"]; style 2 stroke:#2c3143,stroke-width:4px; 3["ℹ️ Input Dataset\ntest_X_10.tsv"]; style 3 stroke:#2c3143,stroke-width:4px; 4["Create a deep learning model architecture"]; 5["Advanced Cut"]; 0 -->|output| 5; 6["Advanced Cut"]; 1 -->|output| 6; 7["Create deep learning model"]; 4 -->|outfile| 7; 8["To categorical"]; 6 -->|output| 8; 9["Deep learning training and evaluation"]; 7 -->|outfile| 9; 2 -->|output| 9; 8 -->|outfile| 9; 10["Model Prediction"]; 9 -->|outfile_object| 10; 9 -->|outfile_weights| 10; 3 -->|output| 10; 11["Machine Learning Visualization Extension"]; 10 -->|outfile_predict| 11; 5 -->|output| 11;
Inputs
Input | Label |
---|---|
Input dataset | test_y_10.tsv |
Input dataset | train_y_10.tsv |
Input dataset | train_X_10.tsv |
Input dataset | test_X_10.tsv |
Outputs
From | Output | Label |
---|---|---|
Input dataset | test_y_10.tsv | |
Input dataset | train_y_10.tsv | |
Input dataset | train_X_10.tsv | |
Input dataset | test_X_10.tsv | |
toolshed.g2.bx.psu.edu/repos/bgruening/keras_model_config/keras_model_config/0.5.0 | Create a deep learning model architecture | |
toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_cut_tool/1.1.0 | Advanced Cut | |
toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_cut_tool/1.1.0 | Advanced Cut | |
toolshed.g2.bx.psu.edu/repos/bgruening/keras_model_builder/keras_model_builder/0.5.0 | Create deep learning model | |
toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_to_categorical/sklearn_to_categorical/1.0.8.3 | To categorical | |
toolshed.g2.bx.psu.edu/repos/bgruening/keras_train_and_eval/keras_train_and_eval/1.0.8.3 | Deep learning training and evaluation | |
toolshed.g2.bx.psu.edu/repos/bgruening/model_prediction/model_prediction/1.0.8.3 | Model Prediction | |
toolshed.g2.bx.psu.edu/repos/bgruening/ml_visualization_ex/ml_visualization_ex/1.0.8.3 | Machine Learning Visualization Extension |
Tools
To use these workflows in Galaxy you can either click the links to download the workflows, or you can right-click and copy the link to the workflow which can be used in the Galaxy form to import workflows.
Importing into Galaxy
Below are the instructions for importing these workflows directly into your Galaxy server of choice to start using them!Hands-on: Importing a workflow
- Click on Workflow on the top menu bar of Galaxy. You will see a list of all your workflows.
- Click on galaxy-upload Import at the top-right of the screen
- Provide your workflow
- Option 1: Paste the URL of the workflow into the box labelled “Archived Workflow URL”
- Option 2: Upload the workflow file in the box labelled “Archived Workflow File”
- Click the Import workflow button
Below is a short video demonstrating how to import a workflow from GitHub using this procedure:
Video: Importing a workflow from URL
Version History
Version | Commit | Time | Comments |
---|---|---|---|
5 | e5dd4d574 | 2021-12-11 18:41:59 | Fixed an error in WF. Must cut test label data to get labels only |
4 | 4b60e0407 | 2021-12-01 15:17:56 | Added Creator section to WF |
3 | 6dd9ffea1 | 2021-11-19 19:42:45 | Adde title and tag to WF |
2 | d6c5afc69 | 2021-11-19 01:25:51 | Wrote parts of fruit image classification tutorial. |
1 | 7d52835fb | 2021-11-17 13:38:55 | Added the tutorial directory. Revising copied files now. |
For Admins
Installing the workflow tools
wget https://training.galaxyproject.org/training-material/topics/statistics/tutorials/fruit_360/workflows/fruit_360.ga -O workflow.ga workflow-to-tools -w workflow.ga -o tools.yaml shed-tools install -g GALAXY -a API_KEY -t tools.yaml workflow-install -g GALAXY -a API_KEY -w workflow.ga --publish-workflows