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Snippets
ML Operations
morphingdesign/pythonLib
Library of Python scripts for tools and operations in various software used in my workflows. - morphingdesign/pythonLib
github.com
Production Protocol
Conventions
2D Matrix: capitalize variable names
- Case: features
1D Vector: lowercase variable names
- Case: labels
TensorFlow
Sequential Model Considerations
Train / Test Sets
Decrease the test size for smaller overall data sets. (~25%) otherwise aim for ~33%.
Batch Size
If not specified, the default batch size is 32. When training model with verbose, output displays batch number rather than each training feature.
tf.keras.Sequential | TensorFlow Core v2.4.1
Sequential groups a linear stack of layers into a tf.keras.Model.
www.tensorflow.org
Resources
Training Outline
Example workflow using the Iris classification problem.
Custom training: walkthrough | TensorFlow Core
This guide uses machine learning to categorize Iris flowers by species. It uses TensorFlow to: Build a model, Train this model on example data, and Use the model to make predictions about unknown data. This guide uses these high-level TensorFlow concepts: This tutorial is structured like many TensorFlow programs: Import and parse the dataset.
www.tensorflow.org
Troubleshooting (TF1)
Uninitialized Variable
Errors with this notification could be a result of the global variable initializer not being run or being run before a variable is created.
init=tf.global_variables_initializer()On This Page
- Snippets
- ML Operations
- Production Protocol
- Conventions
- TensorFlow
- Sequential Model Considerations
- Train / Test Sets
- Batch Size
- Resources
- Training Outline
- Troubleshooting (TF1)
- Uninitialized Variable