benjamesbabala/gentle_tensorflow

Gentle introduction to Tensorflow

Gentlest Tensorflow

Goal

Tensorflow (TF) is Google’s attempt to put the power of Deep Learning into the hands of developers around the world. It comes with a beginner & an advanced tutorial, as well as a course on Udacity. However, the materials attempt to introduce both ML and TF concurrently to solve a multi-feature problem — character recognition, which albeit interesting, unnecessarily convolutes understanding. Gentlest Tensorflow attempts to overcome that by showing how to do linear regression for a single feature problem, and expand from there.

Code

All the code are in /code directory:

• 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