From bfe8469abcab65a8bed43e549e42e5d3a5cfec14 Mon Sep 17 00:00:00 2001 From: Determinant Date: Tue, 19 Apr 2016 16:03:58 +0800 Subject: add doc for nerv.Network; remove the generated HTML doc --- TODO.rst | 6 + nerv/.gitignore | 1 + nerv/config.ld | 4 +- nerv/doc/lua/index.html | 71 ------ nerv/doc/lua/ldoc_pale.css | 304 ----------------------- nerv/doc/lua/modules/layer.affine.html | 145 ----------- nerv/doc/lua/modules/matrix.html | 409 ------------------------------- nerv/doc/lua/modules/nerv.html | 428 --------------------------------- nerv/matrix/init.lua | 9 +- nerv/nn/network.lua | 80 +++++- 10 files changed, 85 insertions(+), 1372 deletions(-) create mode 100644 TODO.rst delete mode 100644 nerv/doc/lua/index.html delete mode 100644 nerv/doc/lua/ldoc_pale.css delete mode 100644 nerv/doc/lua/modules/layer.affine.html delete mode 100644 nerv/doc/lua/modules/matrix.html delete mode 100644 nerv/doc/lua/modules/nerv.html diff --git a/TODO.rst b/TODO.rst new file mode 100644 index 0000000..baeb7ba --- /dev/null +++ b/TODO.rst @@ -0,0 +1,6 @@ +TODO List +--------- + +- NERV user manual +- NERV overview and introduction +- C header file dependency detection in Makefiles diff --git a/nerv/.gitignore b/nerv/.gitignore index 567609b..6b2f08b 100644 --- a/nerv/.gitignore +++ b/nerv/.gitignore @@ -1 +1,2 @@ build/ +doc/lua/ diff --git a/nerv/config.ld b/nerv/config.ld index f60b53f..2cee84a 100644 --- a/nerv/config.ld +++ b/nerv/config.ld @@ -5,4 +5,6 @@ backtick_references = true file = {'./', exclude = {'./lib/luaT'}} dir = 'doc/lua' style = '!pale' -kind_names = {topic = 'Manual',script = 'Programs'} +format = 'markdown' +kind_names = {topic = 'Manual', script = 'Programs'} +backtick_references = false diff --git a/nerv/doc/lua/index.html b/nerv/doc/lua/index.html deleted file mode 100644 index 56212cc..0000000 --- a/nerv/doc/lua/index.html +++ /dev/null @@ -1,71 +0,0 @@ - - - - - NERV documentation - - - - -

- -

- - -

- - - - - - -

- - -

A Lua-based toolkit for high-performance deep learning

- -

Modules

- - - - - - - - - - - - - -

nerv	NERV: a Lua-based toolkit for high-performance deep learning.
layer.affine	Parameter and layer classes related to linear transform.
matrix	Implements a fraction of matrix operations (methods) in Lua, while - others are implemented in C extension.

- -

-generated by LDoc 1.4.3 -Last updated 2016-01-15 14:56:30 -

- - diff --git a/nerv/doc/lua/ldoc_pale.css b/nerv/doc/lua/ldoc_pale.css deleted file mode 100644 index b071110..0000000 --- a/nerv/doc/lua/ldoc_pale.css +++ /dev/null @@ -1,304 +0,0 @@ -/* BEGIN RESET - -Copyright (c) 2010, Yahoo! Inc. All rights reserved. -Code licensed under the BSD License: -http://developer.yahoo.com/yui/license.html -version: 2.8.2r1 -*/ -html { - color: #000; - background: #FFF; -} -body,div,dl,dt,dd,ul,ol,li,h1,h2,h3,h4,h5,h6,pre,code,form,fieldset,legend,input,button,textarea,p,blockquote,th,td { - margin: 0; - padding: 0; -} -table { - border-collapse: collapse; - border-spacing: 0; -} -fieldset,img { - border: 0; -} -address,caption,cite,code,dfn,em,strong,th,var,optgroup { - font-style: inherit; - font-weight: inherit; -} -del,ins { - text-decoration: none; -} -li { - list-style: disc; - margin-left: 20px; -} -caption,th { - text-align: left; -} -h1,h2,h3,h4,h5,h6 { - font-size: 100%; - font-weight: bold; -} -q:before,q:after { - content: ''; -} -abbr,acronym { - border: 0; - font-variant: normal; -} -sup { - vertical-align: baseline; -} -sub { - vertical-align: baseline; -} -legend { - color: #000; -} -input,button,textarea,select,optgroup,option { - font-family: inherit; - font-size: inherit; - font-style: inherit; - font-weight: inherit; -} -input,button,textarea,select {*font-size:100%; -} -/* END RESET */ - -body { - margin-left: 1em; - margin-right: 1em; - font-family: arial, helvetica, geneva, sans-serif; - background-color: #ffffff; margin: 0px; -} - -code, tt { font-family: monospace; font-size: 1.1em; } -span.parameter { font-family:monospace; } -span.parameter:after { content:":"; } -span.types:before { content:"("; } -span.types:after { content:")"; } -.type { font-weight: bold; font-style:italic } - -body, p, td, th { font-size: .95em; line-height: 1.2em;} - -p, ul { margin: 10px 0 0 0px;} - -strong { font-weight: bold;} - -em { font-style: italic;} - -h1 { - font-size: 1.5em; - margin: 0 0 20px 0; -} -h2, h3, h4 { margin: 15px 0 10px 0; } -h2 { font-size: 1.25em; } -h3 { font-size: 1.15em; } -h4 { font-size: 1.06em; } - -a:link { font-weight: bold; color: #004080; text-decoration: none; } -a:visited { font-weight: bold; color: #006699; text-decoration: none; } -a:link:hover { text-decoration: underline; } - -hr { - color:#cccccc; - background: #00007f; - height: 1px; -} - -blockquote { margin-left: 3em; } - -ul { list-style-type: disc; } - -p.name { - font-family: "Andale Mono", monospace; - padding-top: 1em; -} - -pre { - background-color: rgb(245, 245, 245); - border: 1px solid #C0C0C0; /* silver */ - padding: 10px; - margin: 10px 0 10px 0; - overflow: auto; - font-family: "Andale Mono", monospace; -} - -pre.example { - font-size: .85em; -} - -table.index { border: 1px #00007f; } -table.index td { text-align: left; vertical-align: top; } - -#container { - margin-left: 1em; - margin-right: 1em; - background-color: #ffffff; -} - -#product { - text-align: center; - border-bottom: 1px solid #cccccc; - background-color: #ffffff; -} - -#product big { - font-size: 2em; -} - -#main { - background-color:#FFFFFF; // #f0f0f0; - //border-left: 2px solid #cccccc; -} - -#navigation { - float: left; - width: 14em; - vertical-align: top; - background-color:#FFFFFF; // #f0f0f0; - border-right: 2px solid #cccccc; - overflow: visible; -} - -#navigation h2 { - background-color:#FFFFFF;//:#e7e7e7; - font-size:1.1em; - color:#000000; - text-align: left; - padding:0.2em; - //border-top:1px solid #dddddd; - border-bottom:1px solid #dddddd; -} - -#navigation ul -{ - font-size:1em; - list-style-type: none; - margin: 1px 1px 10px 1px; -} - -#navigation li { - text-indent: -1em; - display: block; - margin: 3px 0px 0px 22px; -} - -#navigation li li a { - margin: 0px 3px 0px -1em; -} - -#content { - margin-left: 14em; - padding: 1em; - width: 700px; - border-left: 2px solid #cccccc; - // border-right: 2px solid #cccccc; - background-color: #ffffff; -} - -#about { - clear: both; - padding: 5px; - border-top: 2px solid #cccccc; - background-color: #ffffff; -} - -@media print { - body { - font: 12pt "Times New Roman", "TimeNR", Times, serif; - } - a { font-weight: bold; color: #004080; text-decoration: underline; } - - #main { - background-color: #ffffff; - border-left: 0px; - } - - #container { - margin-left: 2%; - margin-right: 2%; - background-color: #ffffff; - } - - #content { - padding: 1em; - background-color: #ffffff; - } - - #navigation { - display: none; - } - pre.example { - font-family: "Andale Mono", monospace; - font-size: 10pt; - page-break-inside: avoid; - } -} - -table.module_list { - border-width: 1px; - border-style: solid; - border-color: #cccccc; - border-collapse: collapse; -} -table.module_list td { - border-width: 1px; - padding: 3px; - border-style: solid; - border-color: #cccccc; -} -table.module_list td.name { background-color: #f0f0f0; ; min-width: 200px; } -table.module_list td.summary { width: 100%; } - -table.function_list { - border-width: 1px; - border-style: solid; - border-color: #cccccc; - border-collapse: collapse; -} -table.function_list td { - border-width: 1px; - padding: 3px; - border-style: solid; - border-color: #cccccc; -} -table.function_list td.name { background-color: #f6f6ff; ; min-width: 200px; } -table.function_list td.summary { width: 100%; } - -dl.table dt, dl.function dt {border-top: 1px solid #ccc; padding-top: 1em;} -dl.table dd, dl.function dd {padding-bottom: 1em; margin: 10px 0 0 20px;} -dl.table h3, dl.function h3 {font-size: .95em;} - -ul.nowrap { - overflow:auto; - whitespace:nowrap; -} - -/* stop sublists from having initial vertical space */ -ul ul { margin-top: 0px; } -ol ul { margin-top: 0px; } -ol ol { margin-top: 0px; } -ul ol { margin-top: 0px; } - -/* make the target distinct; helps when we're navigating to a function */ -a:target + * { - background-color: #FF9; -} - - -/* styles for prettification of source */ -pre .comment { color: #558817; } -pre .constant { color: #a8660d; } -pre .escape { color: #844631; } -pre .keyword { color: #aa5050; font-weight: bold; } -pre .library { color: #0e7c6b; } -pre .marker { color: #512b1e; background: #fedc56; font-weight: bold; } -pre .string { color: #8080ff; } -pre .number { color: #f8660d; } -pre .operator { color: #2239a8; font-weight: bold; } -pre .preprocessor, pre .prepro { color: #a33243; } -pre .global { color: #800080; } -pre .user-keyword { color: #800080; } -pre .prompt { color: #558817; } -pre .url { color: #272fc2; text-decoration: underline; } - diff --git a/nerv/doc/lua/modules/layer.affine.html b/nerv/doc/lua/modules/layer.affine.html deleted file mode 100644 index 817fc37..0000000 --- a/nerv/doc/lua/modules/layer.affine.html +++ /dev/null @@ -1,145 +0,0 @@ - - - - - NERV documentation - - - - -

- -

- - -

- - - - - - -

- -

Module `layer.affine`

Parameter and layer classes related to linear transform.

- - -

Class nerv.MatrixParam

- - - - - -

MatrixParam:read (handle)

Read from a file handle.

Class nerv.AffineLayer

- - - - - -

AffineLayer:__init (id, global_conf, layer_conf)

The constructor.

- -
-
- - -

Class nerv.MatrixParam

- -

- A parameter that consists of a single matrix -

- - MatrixParam:read (handle) -

- Read from a file handle. - - -

Parameters:

handle - the file handle -

- - - - - -

Class nerv.AffineLayer

- -

- A fully-connected linear transform layer. -

- - AffineLayer:__init (id, global_conf, layer_conf) -

- The constructor. - - -

Parameters:

id - -
global_conf - -
layer_conf - -

- - - - - -

- - -

-generated by LDoc 1.4.3 -Last updated 2016-01-15 14:56:30 -

- - diff --git a/nerv/doc/lua/modules/matrix.html b/nerv/doc/lua/modules/matrix.html deleted file mode 100644 index 2840e59..0000000 --- a/nerv/doc/lua/modules/matrix.html +++ /dev/null @@ -1,409 +0,0 @@ - - - - - NERV documentation - - - - -

- -

- - -

- - - - - - -

- -

Module `matrix`

Implements a fraction of matrix operations (methods) in Lua, while - others are implemented in C extension.

Info:

Author: Ted Yin

- - -

Class nerv.Matrix

- - - - - - - - - - - - - - - - - - - - - - - - - -

nerv.Matrix:__tostring__ ()	Convert the matrix object to a string.
nerv.Matrix:generate (gen)	Assign each element in a matrix using the value returned by a callback `gen`.
nerv.Matrix:create (nrow, ncol)	Create a fresh new matrix of the same matrix type (as `self`).
nerv.Matrix:__add__ (b)	Operator overloading of `+`.
nerv.Matrix:__sub__ (b)	Operator overloading of `-`.
nerv.Matrix:__mul__ (b)	Operator overloading of `*`.

Class nerv.CuMatrixFloat

- - - - - - - - - -

nerv.CuMatrixFloat.new_from_host (mat)	Create a CUDA matrix copy of the host matrix (in memory)
nerv.CuMatrixFloat:new_to_host ()	Create a host matrix copy of the CUDA matrix

Class nerv.CuMatrixDouble

- - - - - - - - - -

nerv.CuMatrixDouble.new_from_host (mat)	Create a CUDA matrix copy of the host matrix (in memory)
nerv.CuMatrixDouble:new_to_host ()	Create a host matrix copy of the CUDA matrix

Class nerv.MMatrix

- - - - - - - - - - - - - -

nerv.MMatrix:copy_toh (b, ...)	A wrapper function for `copy_fromh`
nerv.MMatrix.print_profile ()	Print profiling info of host matrices
nerv.MMatrix.clear_profile ()	Clear profiling info of host matrices

- -
-
- - -

Class nerv.Matrix

- -

- The base class for all matrices. -

- - nerv.Matrix:__tostring__ () -

- Convert the matrix object to a string. - - - - - - - -

- - nerv.Matrix:generate (gen) -

- Assign each element in a matrix using the value returned by a callback gen. - - -

Parameters:

gen - the callback used to generated the values in the matrix, to which - the indices of row and column will be passed (e.g., gen(i, j)) -

- - - - - -

- - nerv.Matrix:create (nrow, ncol) -

- Create a fresh new matrix of the same matrix type (as self). - - -

Parameters:

nrow - optional, the number of rows in the created matrix if specified, - otherwise self:nrow() will be used -
ncol - optional, the number of columns in the created matrix if specified, - otherwise self:ncol() will be used -

- - - - - -

- - nerv.Matrix:__add__ (b) -

- Operator overloading of +. - - -

Parameters:

b - -

- - - - - -

- - nerv.Matrix:__sub__ (b) -

- Operator overloading of -. - - -

Parameters:

b - -

- - - - - -

- - nerv.Matrix:__mul__ (b) -

- Operator overloading of *. - - -

Parameters:

b - -

- - - - - -

Class nerv.CuMatrixFloat

- -

- CUDA float matrices -

- - nerv.CuMatrixFloat.new_from_host (mat) -

- Create a CUDA matrix copy of the host matrix (in memory) - - -

Parameters:

mat - the host matrix -

- - - - - -

- - nerv.CuMatrixFloat:new_to_host () -

- Create a host matrix copy of the CUDA matrix - - - - - - - -

Class nerv.CuMatrixDouble

- -

- CUDA double matrices -

- - nerv.CuMatrixDouble.new_from_host (mat) -

- Create a CUDA matrix copy of the host matrix (in memory) - - -

Parameters:

mat - the host matrix -

- - - - - -

- - nerv.CuMatrixDouble:new_to_host () -

- Create a host matrix copy of the CUDA matrix - - - - - - - -

Class nerv.MMatrix

- -

- The base class for all host (in-memory) matrices -

- - nerv.MMatrix:copy_toh (b, ...) -

- A wrapper function for copy_fromh - - -

Parameters:

b - -
... - -

- - - - - -

- - nerv.MMatrix.print_profile () -

- Print profiling info of host matrices - - - - - - - -

- - nerv.MMatrix.clear_profile () -

- Clear profiling info of host matrices - - - - - - - -

- - -

-generated by LDoc 1.4.3 -Last updated 2016-01-15 14:56:30 -

- - diff --git a/nerv/doc/lua/modules/nerv.html b/nerv/doc/lua/modules/nerv.html deleted file mode 100644 index 91baf9e..0000000 --- a/nerv/doc/lua/modules/nerv.html +++ /dev/null @@ -1,428 +0,0 @@ - - - - - NERV documentation - - - - -

- -

- - -

- - - - - - -

- -

Module `nerv`

NERV: a Lua-based toolkit for high-performance deep learning.

- This file contains misc utility functions of NERV and finally initializes - NERV by including init.lua of other basic modules.

Info:

Author: Ted Yin

- - -

Functions

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

error_method_not_implemented ()	Dummy function.
sprintf (fmt, ...)	Format a string just like sprintf in C.
printf (fmt, ...)	Print a formatted string to stdout.
error (fmt, ...)	Raise an global error with the formatted message.
info (fmt, ...)	Print a notification message that begins with "info" and a timestamp.
warning (fmt, ...)	Print a warning message that begins with "warning" and a timestamp.
class (tname, parenttname)	Create a class (Torch-compatible).
table.tostring (tbl)	Get the string representation of a table, which can be executed as a valid - piece of Lua code.
get_type (tname)	Get the class by name.
is_type (obj, tname)	Check if the object is of the certain class.
dirname (filename)	Strip last component from file name.
include (filename)	Include a script file (chunk) into the current script.

- -
-
- - -

Functions

- -

- - error_method_not_implemented () -

- Dummy function. - Display a friendly error message when user attempts to invoke a - non-implemented function. - - - - - - - -

- - sprintf (fmt, ...) -

- Format a string just like sprintf in C. - - -

Parameters:

fmt - the format string -
... - args, the data to be formatted -

- -

tname - the class name -
parenttname - the parent class name (from which it inherits) -

- -

Returns:

- - the created class - - - - - -

- - table.tostring (tbl) -

- Get the string representation of a table, which can be executed as a valid - piece of Lua code. - - -

Parameters:

tbl - the table -

- -

Returns:

- - the string representation which will result in a Lua table entity - when evaluated - - - - - -

- - get_type (tname) -

- Get the class by name. - - -

Parameters:

tname - the name of the class -

- -

Returns:

- - the class entity - - - - - -

- - is_type (obj, tname) -

- Check if the object is of the certain class. - - -

Parameters:

obj - the object ("class instance") -
tname - the class name ("type name") -

- - - - - -

- - dirname (filename) -

- Strip last component from file name. - - -

Parameters:

filename - the path to a file -

- -

Returns:

- - the path to the containing directory - - - - - -

- - include (filename) -

- Include a script file (chunk) into the current script. - An analogy to #include in C. Note that the effect is the same as executing - dofile(filename) at the current line. - - -

Parameters:

filename - the path to a file -

- -

Returns:

- - all values returned by the chunk - - - - - -

- - -

-generated by LDoc 1.4.3 -Last updated 2016-01-15 14:56:30 -

- - diff --git a/nerv/matrix/init.lua b/nerv/matrix/init.lua index 722c780..5d0544a 100644 --- a/nerv/matrix/init.lua +++ b/nerv/matrix/init.lua @@ -37,10 +37,6 @@ function nerv.Matrix:__tostring__() return table.concat(strt) end ---- Assign each element in a matrix using the value returned by a callback `gen`. --- @param gen the callback used to generated the values in the matrix, to which --- the indices of row and column will be passed (e.g., `gen(i, j)`) - function nerv.Matrix:_generate(gen) if (self:dim() == 2) then for i = 0, self:nrow() - 1 do @@ -51,11 +47,14 @@ function nerv.Matrix:_generate(gen) end else for j = 0, self:ncol() - 1 do - self[j] = gen(j) + self[j] = gen(j) end end end +--- Assign each element in a matrix using the value returned by a callback `gen`. +-- @param gen the callback used to generated the values in the matrix, to which +-- the indices of row and column will be passed (e.g., `gen(i, j)`) function nerv.Matrix:generate(gen) local tmp if nerv.is_type(self, 'nerv.CuMatrixFloat') then diff --git a/nerv/nn/network.lua b/nerv/nn/network.lua index cf6a4d3..19fa9d3 100644 --- a/nerv/nn/network.lua +++ b/nerv/nn/network.lua @@ -1,5 +1,43 @@ +--- Implements the concept of computable but opaque networks built ("compiled") +-- from nested layers +-- @author Qi Liu +-- @author Ted Yin + +--- The class describing a computable but opaque network built from nested +-- layers. +-- @type nerv.Network + local network = nerv.class('nerv.Network') +--- The constructor. +-- @param id the identifier of the network (currently having no effects) +-- @param global_conf a table describing the computation state and providing +-- with some global settings +-- +-- The following fields in `global_conf` will be used: +-- +-- * `use_cpu`: whether to use CPU for the computation +-- * `mmat_type`: the class used for creating matrices in CPU computation +-- * `cumat_type` (if `use_cpu = false`): the class used for creating matrices +-- in GPU computation +-- +-- The following fields in `global_conf` will be altered: +-- +-- * `mask`: an array of `chunk_size` length containing column binary vectors +-- indicating whether each frame in a *batch matrix* (i.e. one matrix in a BPTT +-- chunk/"mini-batch") contains a valid data (1 indicates data, 0 indicates +-- holes) +-- +-- @param network_conf a table providing with settings dedicated for the +-- network. Available fields includes: +-- +-- * `network`: a `nerv.Layer` instance describing the structure of the network +-- to be compiled +-- * `clip`: a `number` value indicating the cliping threshold (i.e. preserve +-- the values within [-clip, +clip]) +-- * `nn_act_default`: a `number` value indicating the value used for filling +-- "holes" in activation values of a batch matrix (0 by default) + function network:__init(id, global_conf, network_conf) self.id = id self.network = network_conf.network @@ -150,6 +188,11 @@ function network:compile(layer) return socket end +--- Initialize the network for training. +-- To be called before all the epochs, will resolve the structure of the +-- network and allocate the memory for storing temporary values +-- @param batch_size The size of a batch matrix +-- @param chunk_size The size of a BPTT chunk function network:init(batch_size, chunk_size) self.batch_size = batch_size self.chunk_size = chunk_size @@ -167,6 +210,8 @@ function network:init(batch_size, chunk_size) end end +--- Initialize the internal state of the network for the new epoch. +-- To be called before each new epoch function network:epoch_init() self.timestamp = 0 for i = 1, #self.layers do @@ -457,15 +502,29 @@ function network:set_err_output(err_output) end end ---[[ - [info] is a table that contains information of current mini-batch. These fields must be contained: - [input], [output] : matrix array which stores the network input and output - [seq_length] : a table contains the length of every sequences - [new_seq]: a table contains the batch number of new sequences - [do_train]: a bool value indicates do train or not - if [do_train] is true, these fileds also must be contained: - [err_input], [err_output] : matrix array which stores the network err_input and err_output ---]] +--- Initialize the internal state of the network for the new mini-batch (a BPTT chunk). +-- To be called before each propagation/back-propagation +-- @param info a table containing information needed for the current mini-batch computation. The following fields must be supplied: +-- +-- * `input`: an array containing `chunk_size` number of row-major batch +-- matrices with `batch_size` rows +-- * `output`: similar to `input`, but the matrices have different number of +-- columns (depending on the width of the output, which is typically 1 for +-- criteria, i.e. single column indicating the error), used to hold the output of the network +-- * `seq_length` : a table containing the length (number of frames) of each sequence (utterance) +-- * `new_seq`: a table containing the indices of batch matrix rows that are the +-- first frames of a sequence +-- * `do_train`: a bool value indicating whether to update the network +-- +-- If `do_train` is true, two additional fields are required: +-- +-- * `err_input`: an array with the same structure as `output` but containg the initial +-- values for computing errors in back-propagation (when the width of the +-- output is 1, `gconf.mask` is typically used here to ignore the invalid +-- values produced by "holes" in the mini-batch). +-- * `err_output`: an array with the same structure as `input`. Although we +-- are mostly not interested in its values, just allocate this to unify +-- the computation and ease the implementation function network:mini_batch_init(info) self.info = info self:set_input(self.info.input) @@ -573,6 +632,7 @@ function network:mini_batch_init(info) end end +--- Perform a propagation. function network:propagate() for i = 1, #self.queue do local t, id = self.queue[i].chunk, self.queue[i].id @@ -590,6 +650,7 @@ function network:propagate() end end +--- Perform a backward propagation to calculate gradients used for update. function network:back_propagate() for i = #self.queue, 1, -1 do local t, id = self.queue[i].chunk, self.queue[i].id @@ -614,6 +675,7 @@ function network:back_propagate() end end +--- Update the parameters bound to each layer. function network:update() for i = 1, #self.layers do self.layers[i]:update() -- cgit v1.2.3

NERV

Modules

A Lua-based toolkit for high-performance deep learning

Modules

NERV

Contents

Modules

Module layer.affine

Class nerv.MatrixParam

Class nerv.AffineLayer

Class nerv.MatrixParam

Parameters:

Class nerv.AffineLayer

Parameters:

NERV

Contents

Modules

Module matrix

Info:

Class nerv.Matrix

Class nerv.CuMatrixFloat

Class nerv.CuMatrixDouble

Class nerv.MMatrix

Class nerv.Matrix

Parameters:

Parameters:

Parameters:

Parameters:

Parameters:

Class nerv.CuMatrixFloat

Parameters:

Class nerv.CuMatrixDouble

Parameters:

Class nerv.MMatrix

Parameters:

NERV

Contents

Modules

Module nerv

Info:

Functions

Functions

Parameters:

Returns:

Parameters:

Parameters:

Parameters:

Parameters:

Parameters:

Returns:

Parameters:

Returns:

Parameters:

Returns:

Parameters:

Parameters:

Returns:

Parameters:

Returns:

Module `layer.affine`

Module `matrix`

Module `nerv`