Commit 27d3fcc6 authored by Alexis Brenon's avatar Alexis Brenon
Browse files

馃摑 Update network doc

parent a824d147
--- A module to build neural network from a descrption. --- A module to build neural network from a @{NetworkDescription|description}.
-- --
-- An example of description can be found in this file: @{Inference.lua} -- An example of description can be found in this file: @{Inference.lua}
-- @module network -- @module network
...@@ -10,6 +10,35 @@ local module = require('arcades.utils.package_loader')(...) ...@@ -10,6 +10,35 @@ local module = require('arcades.utils.package_loader')(...)
local torch = require('torch') local torch = require('torch')
local nn = require('nn') local nn = require('nn')
--- Data Types
-- @section data-types
--- Description of a neural networks.
-- @tfield table input_size Size of the input @{torch.Tensor/tensor.md/|Tensor} `{d, w, h}`
-- @tfield {ConvolutionDescription,...} conv_layers List of @{nn.Convolution/convolution.md#nn.SpatialModules/|convolution} layers
-- @tfield {number,...} fc_layers List of width of @{nn.Linear/simple.md#nn.Linear/} fully connected layers
-- @tfield string nl_layer Name of the @{nn.Transfert/transfer.md/|transfert function} to use
-- @tfield table output_size Size of the output @{torch.Tensor/tensor.md/|Tensor} `{w, h}`
-- @table NetworkDescription
--- Description of a convolutionnal layer.
-- @tfield int n_filters Number of filters to use
-- @tfield DimenTable field_size Size of the receptive field
-- @tfield DimenTable stride Size of the strides
-- @tfield DimenTable zero_padding Size of the paddings
-- @table ConvolutionDescription
--- Description of a 2D dimension.
-- @tfield int width
-- @tfield int height
-- @table DimenTable
--- Public Methods
-- @section public-methods
--- Build a network.
-- @tparam NetworkDescription args Description of the network to build
-- @treturn nn.Sequential Built @{nn.Module/containers.md#nn.Sequential/|network}
function module.create_network(args) function module.create_network(args)
local net = nn.Sequential() local net = nn.Sequential()
......
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