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TensorFlow

Tools​

  • Torch
  • Caffe
  • Theano
  • TensorFlow

TensorFlow​

High-level neural network library for deep learning

TensorBoard: Visualizing Learning​

Can be used to visualize TensorFlow graph, plot quantitative metrics about the execution of the graph, and show additional data like images that pass through it.

http://projector.tensorflow.org

https://www.i-programmer.info/news/105/13559.html

MNIST Architecture​

Hands-on TensorBoard (TensorFlow Dev Summit 2017)

Installation​

https://www.tensorflow.org/install/install_mac

Getting Started​

https://www.tensorflow.org/get_started/get_started

API​

  1. TensorFlow Core (Lowest level API for complete programming control)

  2. Higher level APIs (Ex - tf.estimator helps manage data sets, estimators, training and inference)

TensorFlow Core​

  1. Tensors

The central unit of data in TensorFlow is the tensor. A tensor consists of a set of primitive values shaped into an array of any number of dimensions. A tensor's rank is its number of dimensions. Here are some examples of tensors:

3 # a rank 0 tensor; a scalar with shape []
[1., 2., 3.] # a rank 1 tensor; a vector with shape [3]
[[1., 2., 3.], [4., 5., 6.]] # a rank 2 tensor; a matrix with shape [2, 3]
[[[1., 2., 3.]], [[7., 8., 9.]]] # a rank 3 tensor with shape [2, 1, 3]

1.1. The Computational Graph​

A computational graph is a series of TensorFlow operations arranged into a graph of nodes.

TensorFlow Model Serving​

  • Contains gRPC and HTTP endpoints
  • Performs model versioning without changing any client code
  • Schedules grouping individual inference requests into batches for joint execution
  • Optimizes inference time for minimal latency
  • Supports many servables (a servable is either a model or a task for serving the data that goes along with your model):
    • TensorFlow models
    • Embeddings
    • Vocabulary lookup tables
    • Feature transformations
    • Non-TensorFlow-based models
  • Is capable of canarying and A/B testing

https://dzone.com/articles/machine-learning-and-real-time-analytics-in-apache

Tensorflow Extended​

TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines

https://www.tensorflow.org/tfx

GitHub - tensorflow/tfx: TFX is an end-to-end platform for deploying production ML pipelines

Commands​

tensorboard --logdir=path/to/log-directory

Others​