Intro_To_FNN_v1_0_10_0 1.0
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This workflows is part of the tutorial Deep Learning (Part 1) - Feedforward neural networks (FNN), available in the GTN
Features
- Includes Galaxy Workflow Tests
Thanks to...
Workflow Author(s): Kaivan Kamali
Tutorial Author(s): Kaivan Kamali
Inputs
| ID | Name | Description | Type |
|---|---|---|---|
| X_test | X_test | n/a |
|
| X_train | X_train | n/a |
|
| y_test | y_test | n/a |
|
| y_train | y_train | n/a |
|
Steps
| ID | Name | Description |
|---|---|---|
| 4 | Create a deep learning model architecture | toolshed.g2.bx.psu.edu/repos/bgruening/keras_model_config/keras_model_config/1.0.10.0 |
| 5 | Create deep learning model | toolshed.g2.bx.psu.edu/repos/bgruening/keras_model_builder/keras_model_builder/1.0.10.0 |
| 6 | Deep learning training and evaluation | toolshed.g2.bx.psu.edu/repos/bgruening/keras_train_and_eval/keras_train_and_eval/1.0.10.0 |
| 7 | Model Prediction | toolshed.g2.bx.psu.edu/repos/bgruening/model_prediction/model_prediction/1.0.10.0 |
| 8 | Plot actual vs predicted curves and residual plots | toolshed.g2.bx.psu.edu/repos/bgruening/plotly_regression_performance_plots/plotly_regression_performance_plots/0.1 |
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Views: 235 Downloads: 35 Runs: 0
Created: 2nd Jun 2025 at 10:59
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