agd.AutomaticDifferentiation

This package implements automatic differentiation (AD) methods, in the following flavors:

  • Dense, Sparse, and Reverse (experimental) modes
  • First and second order differentiation
  • CPU and GPU support, using numpy and cupy. The AD types implement numpy's overloading mechanisms, and one should be able to use them as drop in replacement for numpy arrays in many contexts.

Main submodules:

  • Dense : first order, forward AD with dense storage.
  • Dense2 : second order, forward AD with dense storage.
  • Sparse : first order, forward AD with sparse storage.
  • Sparse2 : second order, forward AD with sparse storage.
  • Reverse, Reverse2 (experimental) : first and second order, reverse AD.
  • Optimization : basic Newton method implemented using AD

Main functions:

  • asarray, array: turn a list/tuple of arrays into a larger array.
  • is_ad : test whether a variable embeds AD information.
  • remove_ad : remove AD information
  • simplify_ad : compress the AD information, of Sparse and Sparse2 types.
  • apply : apply a function to some arguments, using specified AD tricks.
  • isndarray : returns true for numpy, cupy, and AD types.
  • cupy_friendly : helper function for CPU/GPU generic programming.
 1# Copyright 2020 Jean-Marie Mirebeau, University Paris-Sud, CNRS, University Paris-Saclay
 2# Distributed WITHOUT ANY WARRANTY. Licensed under the Apache License, Version 2.0, see http://www.apache.org/licenses/LICENSE-2.0
 3
 4"""
 5This package implements automatic differentiation (AD) methods, in the following flavors:
 6- Dense, Sparse, and Reverse (experimental) modes
 7- First and second order differentiation
 8- CPU and GPU support, using numpy and cupy.
 9The AD types implement numpy's overloading mechanisms, and one should be able to use them
10as drop in replacement for numpy arrays in many contexts.
11
12Main submodules:
13- Dense : first order, forward AD with dense storage.
14- Dense2 : second order, forward AD with dense storage.
15- Sparse : first order, forward AD with sparse storage.
16- Sparse2 : second order, forward AD with sparse storage.
17- Reverse, Reverse2 (experimental) : first and second order, reverse AD.
18- Optimization : basic Newton method implemented using AD
19
20Main functions:
21- asarray, array: turn a list/tuple of arrays into a larger array.
22- is_ad : test whether a variable embeds AD information.
23- remove_ad : remove AD information
24- simplify_ad : compress the AD information, of Sparse and Sparse2 types.
25- apply : apply a function to some arguments, using specified AD tricks.
26- isndarray : returns true for numpy, cupy, and AD types.
27- cupy_friendly : helper function for CPU/GPU generic programming.
28"""
29
30
31from . import functional
32#from . import Base # No need to import, but this level in hierarchy
33from . import cupy_support
34from . import cupy_generic
35from . import ad_generic
36from . import misc
37from . import Dense
38from . import Sparse
39from . import Reverse
40from . import Dense2
41from . import Sparse2
42from . import Reverse2
43from . import Optimization
44from . import ad_specific
45
46from .ad_generic import array,asarray,is_ad,remove_ad,common_cast,min_argmin, \
47	max_argmax,disassociate,associate,apply_linear_mapping,apply_linear_inverse,precision
48
49from .ad_specific import simplify_ad,apply,compose
50from .cupy_generic import isndarray,cupy_friendly
51
52class DeliberateNotebookError(Exception):
53	def __init__(self,message):
54		super(DeliberateNotebookError,self).__init__(message)
class DeliberateNotebookError(builtins.Exception): 
53class DeliberateNotebookError(Exception):
54	def __init__(self,message):
55		super(DeliberateNotebookError,self).__init__(message)

Common base class for all non-exit exceptions.

DeliberateNotebookError(message) 
54	def __init__(self,message):
55		super(DeliberateNotebookError,self).__init__(message)
Inherited Members
builtins.BaseException
with_traceback
add_note
args