TensorFlow vs Keras vs PyTorch -Overview

TensorFlow vs Keras vs PyTorch

	TensorFlow	Keras	PyTorch
API level	high / low	high	low
Speed	very fast	slower	very fast
Dataset	large	small	large
Debugging	hard	easy/less frequent	easy
Most commonly used	Image		NLP / Computer Vision
company	google	google	facebook

API

• API : application programming interface
  • Low : more detailed. And allows you to have more detailed control to manipulate functions
  • High: more simply , easier

Keras

• Keras: wait for tensorflow to finish its implementation then starts its own implementation

Dataflow

• Dataflow

  • Advantage:

    • Parallelism
      ->easy for the system to identify operations executed in parallel
    • Distributed execution
      • -> it is possible for TensorFlow to partition our program across multiple CPU/GPU as all tensors(variables) are immutable (can NOT update the content, and only new tensor will be created).

  • Process:

    1. Define the dataflow graph
    2. Create a TensorFlow session and Run

  • terms

    • node: units of computation
    • edges: data consumed/produced by a computation
    • tensor: variable e.g., x and y
    • operator: mathematical operator e.g., + and /

https://www.i2tutorials.com/how-do-you-build-computational-graph-in-tensorflow/