ndugal6 / TensorFlow

TensorFlow

##Goal

Google's Tensorflow provides an introduction which dives into ML and TF together to solve a multi-feature problem — character recognition, which convolutes understanding. This attempts to overcome that by showing how to do linear regression for a single feature problem, and expand from there.

Cheatsheet

• Linear regression: single feature, single scalar outcome
• Linear regression: multi-feature, single scalar outcome
• Logistic regression: multi-feature, multi-class outcome

Code

• linear_regression_one_feature.py
  • ML with linear regression for a single feature
    • Example: predict house price from house size (single feature)
• linear_regression_one_feature_with_tensorboard.py
  • Add visualization for 'ML for single feature' with Tensorboard
    • Use tf.scalar_summary, tf.histogram_summary to collect data for variables that we want to visualize
    • Use scope to collapse TF network graph in to expandable/collapsible black boxes to faciliate visualization
• linear_regression_one_feature_using_mini_batch_with_tensorboard.py
  • Perform 'stochastic/mini-batch/batch' Gradient Descent with TF
  • The CUSTOMIZABLE section contains all the configurations that we can tweak, e.g., batch size, etc.
• linear_regression_multi_feature_using_mini_batch_without_matrix_with_tensorboard.py
  • ML with linear regrssion for 2 features without using 'matrix'
  • Create additional tf.Variable, tf.placeholder for each feature
  • IMPORTANT: This is a messy way to do ML with multiple features. This is provided as an explanation of multi-feature concept.
• linear_regression_multi_feature_using_mini_batch_with_tensorboard.py
  • ML with linear regrssion for 2 features
  • Expanding existing W (tf.Variable) in matrix 'height', and existing x (tf.placeholder) in matrix 'width' to accomodate each feature