ml-iris

Machine learning for Iris dataset

• Uses TensorFlow to create a Logistic Regression classifier
• Full ML pipeline with tensorflow backend

Imports raw data from csv, randomizes data, preprocesses data, splits data, trains model, tests model, saves model

Uses TensorBoard to visualize the results

Dependencies

• TensorFlow
  • Backend for training and model visualization
• NumPy
  • Data preprocessing and manipulation
• Pandas
  • Data preprocessing and manipulation
• Matplotlib
  • Data visualization backend
• Seaborn
  • Data visualization helper

Usage

python iris.py [-h] [--visual] [--learning_rate LEARNING_RATE]
               [--filename FILENAME] [--stddev STDDEV]

Train/test iris dataset using logistic regression

optional arguments:
 -h, --help            show this help message and exit
 --load                load model rather than train
 --visual              plot data and features prior to load/test
 --learning_rate LEARNING_RATE
                       learning rate for GradientDescentOptimizer
 --filename FILENAME   file to store/load model to/from
 --stddev STDDEV       standard deviation for random_normal init values

Train a new model

• Save to default filename, use default hyperparameters

python iris.py

Test a model

• Load from default filename

python iris.py --load

Custom training

• Use custom hyperparameters, save to custom file, show features before training

python iris.py --visual --learning_rate 0.1 --filename my_model --stddev 0.5

Custom testing

• Load from custom file

python iris.py --load --filename my_model

Changelog

• 0.2.1

  • Rework tensorboard and model saving directory

• 0.2.0

  • Add TensorBoard support
  • Show validation set accuracy during training
  • Implement tf.name_scope for organization of variables

• 0.1.2

  • Added command line arguments for learning rate and filename
  • Allow save/restore from any file

• 0.1.1

  • Changed command line argument implementation to use argparse

• 0.1.0

  • Initial version