What is TensorFlow?

Small overview of open source software library for numerical computation using data flow graphs
24 August 2017   545

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

Main features:

  • The main library is suitable for a wide range of machine learning techniques, and not just for in-depth training.
  • Linear algebra and other insides are clearly visible from the outside.
  • In addition to the basic functionality of machine learning, TensorFlow also includes its own logging system, its own interactive log visualizer and even a powerful data delivery architecture.
  • The TensorFlow performance model differs from the scicit-learn of the Python language and from most tools in R.
     

Check official website for more info.

What is YAPF?

A formatter for Python files, developed by Google team
30 October 2017   451

What is YAPF?

Most of the current formatters for Python --- e.g., autopep8, and pep8ify --- are made to remove lint errors from code. This has some obvious limitations. For instance, code that conforms to the PEP 8 guidelines may not be reformatted. But it doesn't mean that the code looks good.

YAPF takes a different approach. It's based off of 'clang-format', developed by Daniel Jasper. In essence, the algorithm takes the code and reformats it to the best formatting that conforms to the style guide, even if the original code didn't violate the style guide. The idea is also similar to the 'gofmt' tool for the Go programming language: end all holy wars about formatting - if the whole codebase of a project is simply piped through YAPF whenever modifications are made, the style remains consistent throughout the project and there's no point arguing about style in every code review.

The ultimate goal is that the code YAPF produces is as good as the code that a programmer would write if they were following the style guide. It takes away some of the drudgery of maintaining your code.

Code examples

YAPF takes this code:

x = {  'a':37,'b':42,

'c':927}

y = 'hello ''world'
z = 'hello '+'world'
a = 'hello {}'.format('world')
class foo  (     object  ):
  def f    (self   ):
    return       37*-+2
  def g(self, x,y=42):
      return y
def f  (   a ) :
  return      37+-+a[42-x :  y**3]

and reformat it into:

x = {'a': 37, 'b': 42, 'c': 927}

y = 'hello ' 'world'
z = 'hello ' + 'world'
a = 'hello {}'.format('world')


class foo(object):
    def f(self):
        return 37 * -+2

    def g(self, x, y=42):
        return y


def f(a):
    return 37 + -+a[42 - x:y**3]

See GitHub for more information.