TensorFlow

Notes

Google Cloud Data Engineering Certification Course

Building TensorFlow Models: GCDEC/Building_Tensorflow/Notes

Deploying TensorFlow Models: GCDEC/Deploying_Tensorflow/Notes

Engineering TensorFlow Models: GCDEC/Engineering_Tensorflow/Notes

Usage and Components

Command Line Arguments

TensorFlow/Command Line Args - notes on using command line arguments in TensorFlow models

Projector

The embedding projector site is a page that uses (?) TensorFlow to create lower-dimensional visualizations from higher-dimensional data. There are several visualization options, including tSNE and PCA.

Link: http://projector.tensorflow.org/

The Influence of Google Technologies

An interesting blog post that highlights the influence of Google Technologies on TensorFlow: http://planspace.org/20170313-tensorflow_use_of_google_technologies/

This includes:

• gflags
• apputils (now abseil)
• bazel
• protobuf
• grpc
• gemmlowp
• gfile

Github

Other Repositories

Official Repositories

• https://github.com/tensorflow/tensorflow - official TensorFlow repository

• https://github.com/tensorflow/models - models and examples built with TensorFlow
  • contains models referenced by TensorFlow tutorials in the documentation: https://www.tensorflow.org/tutorials/
  • a partial list of other models: imagenet, adversarial networks, OCR, autoencoders, compression, im2txt, inception, optimizer, GPU, streetview, video prediction

• https://github.com/GoogleCloudPlatform/training-data-analyst - models and examples for the TensorFlow section of the Google Cloud Data Engineering training
  • relevant code is in courses/machine_learning/
  • contains sequential buildup of a simple tensorflow example into a more complex tensorflow example in courses/machine_learning/tensorflow
  • contains example bundled app for training/predictions using Cloud ML Engine in courses/machine_learning/cloudmle

• https://github.com/GoogleCloudPlatform/cloudml-samples - examples that demonstrate how to use Cloud ML Engine; this demonstrates multiple techniques:
  • training a Keras model using Cloud ML Engine
  • using "canned" TensorFlow estimator
  • using custom TensorFlow estimator
  • using low-level TensorFlow API
  • both linear and deep models
  • wide and deep network

• https://github.com/tensorflow/nmt - machine translation tutorial using TensorFlow

• https://github.com/tensorflow/k8s - tools for running TensorFlow using Kubernetes

• https://github.com/tensorflow/agents - agents provides examples of doing batch reinforcement learning in TensorFlow using agents

• https://github.com/tensorflow/tensorboard - TensorBoard is a visualization tool for TensorFlow

• https://github.com/tensorflow/transform - a library for doing transforms on input data for preprocessing (e.g., with Apache Beam)
  • getting started guide: https://github.com/tensorflow/transform/blob/master/getting_started.md
  • import the library as import tensorflow_transform as tft
  • define transforms as x_centered = x - tft.mean(x), where x is a Tensor object
  • the preprocessing function is passed Tensors in batches, not individually; operations/functions are applied to single tensors at a time; these operations are then "broadcast" to the entire tensor

• https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/learn/python/learn - previously "skflow", this is a simplified, high-level interface to TensorFlow for learning
  • documented here: https://www.tensorflow.org/api_docs/python/tf/contrib/learn
  • also provides interfaces to Pandas dataframes (yay!)

Other Repositories

• https://github.com/pdsmith1223/rntn - recurrent neural networks and recursive neural tensor networks

• https://github.com/ajschumacher/mapreduce_with_tensorflow - an unusual use of TensorFlow to perform MapReduce computations