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10 changes: 5 additions & 5 deletions doc/containers.md
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# Containers #
Complex neural networks are easily built using container classes:

* [Container](#nn.Container) : abstract class inherited by containers ;
* [Sequential](#nn.Sequential) : plugs layers in a feed-forward fully connected manner ;
* [Parallel](#nn.Parallel) : applies its `ith` child module to the `ith` slice of the input Tensor ;
* [Concat](#nn.Concat) : concatenates in one layer several modules along dimension `dim` ;
* [DepthConcat](#nn.DepthConcat) : like Concat, but adds zero-padding when non-`dim` sizes don't match;
* [Container](#nn.Container) : abstract class inherited by containers ;
* [Sequential](#nn.Sequential) : plugs layers in a feed-forward fully connected manner ;
* [Parallel](#nn.Parallel) : applies its `ith` child module to the `ith` slice of the input Tensor ;
* [Concat](#nn.Concat) : concatenates in one layer several modules along dimension `dim` ;
* [DepthConcat](#nn.DepthConcat) : like Concat, but adds zero-padding when non-`dim` sizes don't match;

See also the [Table Containers](#nn.TableContainers) for manipulating tables of [Tensors](https://github.com/torch/torch7/blob/master/doc/tensor.md).

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40 changes: 20 additions & 20 deletions doc/convolution.md
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A convolution is an integral that expresses the amount of overlap of one function `g` as it is shifted over another function `f`. It therefore "blends" one function with another. The neural network package supports convolution, pooling, subsampling and other relevant facilities. These are divided base on the dimensionality of the input and output [Tensors](https://github.com/torch/torch7/blob/master/doc/tensor.md#tensor):

* [Temporal Modules](#nn.TemporalModules) apply to sequences with a one-dimensional relationship
* [Temporal Modules](#nn.TemporalModules) apply to sequences with a one-dimensional relationship
(e.g. sequences of words, phonemes and letters. Strings of some kind).
* [TemporalConvolution](#nn.TemporalConvolution) : a 1D convolution over an input sequence ;
* [TemporalSubSampling](#nn.TemporalSubSampling) : a 1D sub-sampling over an input sequence ;
* [TemporalMaxPooling](#nn.TemporalMaxPooling) : a 1D max-pooling operation over an input sequence ;
* [LookupTable](#nn.LookupTable) : a convolution of width `1`, commonly used for word embeddings ;
* [Spatial Modules](#nn.SpatialModules) apply to inputs with two-dimensional relationships (e.g. images):
* [SpatialConvolution](#nn.SpatialConvolution) : a 2D convolution over an input image ;
* [SpatialSubSampling](#nn.SpatialSubSampling) : a 2D sub-sampling over an input image ;
* [SpatialMaxPooling](#nn.SpatialMaxPooling) : a 2D max-pooling operation over an input image ;
* [SpatialAveragePooling](#nn.SpatialAveragePooling) : a 2D average-pooling operation over an input image ;
* [SpatialAdaptiveMaxPooling](#nn.SpatialAdaptiveMaxPooling) : a 2D max-pooling operation which adapts its parameters dynamically such that the output is of fixed size ;
* [SpatialLPPooling](#nn.SpatialLPPooling) : computes the `p` norm in a convolutional manner on a set of input images ;
* [SpatialConvolutionMap](#nn.SpatialConvolutionMap) : a 2D convolution that uses a generic connection table ;
* [SpatialZeroPadding](#nn.SpatialZeroPadding) : padds a feature map with specified number of zeros ;
* [SpatialSubtractiveNormalization](#nn.SpatialSubtractiveNormalization) : a spatial subtraction operation on a series of 2D inputs using
* [SpatialBatchNormalization](#nn.SpatialBatchNormalization): mean/std normalization over the mini-batch inputs and pixels, with an optional affine transform that follows
* [TemporalConvolution](#nn.TemporalConvolution) : a 1D convolution over an input sequence ;
* [TemporalSubSampling](#nn.TemporalSubSampling) : a 1D sub-sampling over an input sequence ;
* [TemporalMaxPooling](#nn.TemporalMaxPooling) : a 1D max-pooling operation over an input sequence ;
* [LookupTable](#nn.LookupTable) : a convolution of width `1`, commonly used for word embeddings ;
* [Spatial Modules](#nn.SpatialModules) apply to inputs with two-dimensional relationships (e.g. images):
* [SpatialConvolution](#nn.SpatialConvolution) : a 2D convolution over an input image ;
* [SpatialSubSampling](#nn.SpatialSubSampling) : a 2D sub-sampling over an input image ;
* [SpatialMaxPooling](#nn.SpatialMaxPooling) : a 2D max-pooling operation over an input image ;
* [SpatialAveragePooling](#nn.SpatialAveragePooling) : a 2D average-pooling operation over an input image ;
* [SpatialAdaptiveMaxPooling](#nn.SpatialAdaptiveMaxPooling) : a 2D max-pooling operation which adapts its parameters dynamically such that the output is of fixed size ;
* [SpatialLPPooling](#nn.SpatialLPPooling) : computes the `p` norm in a convolutional manner on a set of input images ;
* [SpatialConvolutionMap](#nn.SpatialConvolutionMap) : a 2D convolution that uses a generic connection table ;
* [SpatialZeroPadding](#nn.SpatialZeroPadding) : padds a feature map with specified number of zeros ;
* [SpatialSubtractiveNormalization](#nn.SpatialSubtractiveNormalization) : a spatial subtraction operation on a series of 2D inputs using
* [SpatialBatchNormalization](#nn.SpatialBatchNormalization): mean/std normalization over the mini-batch inputs and pixels, with an optional affine transform that follows
a kernel for computing the weighted average in a neighborhood ;
* [Volumetric Modules](#nn.VolumetricModules) apply to inputs with three-dimensional relationships (e.g. videos) :
* [VolumetricConvolution](#nn.VolumetricConvolution) : a 3D convolution over an input video (a sequence of images) ;
* [VolumetricMaxPooling](#nn.VolumetricMaxPooling) : a 3D max-pooling operation over an input video.
* [VolumetricAveragePooling](#nn.VolumetricAveragePooling) : a 3D average-pooling operation over an input video.
* [Volumetric Modules](#nn.VolumetricModules) apply to inputs with three-dimensional relationships (e.g. videos) :
* [VolumetricConvolution](#nn.VolumetricConvolution) : a 3D convolution over an input video (a sequence of images) ;
* [VolumetricMaxPooling](#nn.VolumetricMaxPooling) : a 3D max-pooling operation over an input video.
* [VolumetricAveragePooling](#nn.VolumetricAveragePooling) : a 3D average-pooling operation over an input video.

<a name="nn.TemporalModules"></a>
## Temporal Modules ##
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38 changes: 19 additions & 19 deletions doc/criterion.md
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[`Criterions`](#nn.Criterion) are helpful to train a neural network. Given an input and a
target, they compute a gradient according to a given loss function.

* Classification criterions:
* [`BCECriterion`](#nn.BCECriterion): binary cross-entropy (two-class version of [`ClassNLLCriterion`](#nn.ClassNLLCriterion));
* [`ClassNLLCriterion`](#nn.ClassNLLCriterion): negative log-likelihood for [`LogSoftMax`](transfer.md#nn.LogSoftMax) (multi-class);
* [`CrossEntropyCriterion`](#nn.CrossEntropyCriterion): combines [`LogSoftMax`](transfer.md#nn.LogSoftMax) and [`ClassNLLCriterion`](#nn.ClassNLLCriterion);
* [`MarginCriterion`](#nn.MarginCriterion): two class margin-based loss;
* [`MultiMarginCriterion`](#nn.MultiMarginCriterion): multi-class margin-based loss;
* [`MultiLabelMarginCriterion`](#nn.MultiLabelMarginCriterion): multi-class multi-classification margin-based loss;
* Regression criterions:
* [`AbsCriterion`](#nn.AbsCriterion): measures the mean absolute value of the element-wise difference between input;
* [`MSECriterion`](#nn.MSECriterion): mean square error (a classic);
* [`DistKLDivCriterion`](#nn.DistKLDivCriterion): Kullback–Leibler divergence (for fitting continuous probability distributions);
* Embedding criterions (measuring whether two inputs are similar or dissimilar):
* [`HingeEmbeddingCriterion`](#nn.HingeEmbeddingCriterion): takes a distance as input;
* [`L1HingeEmbeddingCriterion`](#nn.L1HingeEmbeddingCriterion): L1 distance between two inputs;
* [`CosineEmbeddingCriterion`](#nn.CosineEmbeddingCriterion): cosine distance between two inputs;
* Miscelaneus criterions:
* [`MultiCriterion`](#nn.MultiCriterion) : a weighted sum of other criterions each applied to the same input and target;
* [`ParallelCriterion`](#nn.ParallelCriterion) : a weighted sum of other criterions each applied to a different input and target;
* [`MarginRankingCriterion`](#nn.MarginRankingCriterion): ranks two inputs;
* Classification criterions:
* [`BCECriterion`](#nn.BCECriterion): binary cross-entropy (two-class version of [`ClassNLLCriterion`](#nn.ClassNLLCriterion));
* [`ClassNLLCriterion`](#nn.ClassNLLCriterion): negative log-likelihood for [`LogSoftMax`](transfer.md#nn.LogSoftMax) (multi-class);
* [`CrossEntropyCriterion`](#nn.CrossEntropyCriterion): combines [`LogSoftMax`](transfer.md#nn.LogSoftMax) and [`ClassNLLCriterion`](#nn.ClassNLLCriterion);
* [`MarginCriterion`](#nn.MarginCriterion): two class margin-based loss;
* [`MultiMarginCriterion`](#nn.MultiMarginCriterion): multi-class margin-based loss;
* [`MultiLabelMarginCriterion`](#nn.MultiLabelMarginCriterion): multi-class multi-classification margin-based loss;
* Regression criterions:
* [`AbsCriterion`](#nn.AbsCriterion): measures the mean absolute value of the element-wise difference between input;
* [`MSECriterion`](#nn.MSECriterion): mean square error (a classic);
* [`DistKLDivCriterion`](#nn.DistKLDivCriterion): Kullback–Leibler divergence (for fitting continuous probability distributions);
* Embedding criterions (measuring whether two inputs are similar or dissimilar):
* [`HingeEmbeddingCriterion`](#nn.HingeEmbeddingCriterion): takes a distance as input;
* [`L1HingeEmbeddingCriterion`](#nn.L1HingeEmbeddingCriterion): L1 distance between two inputs;
* [`CosineEmbeddingCriterion`](#nn.CosineEmbeddingCriterion): cosine distance between two inputs;
* Miscelaneus criterions:
* [`MultiCriterion`](#nn.MultiCriterion) : a weighted sum of other criterions each applied to the same input and target;
* [`ParallelCriterion`](#nn.ParallelCriterion) : a weighted sum of other criterions each applied to a different input and target;
* [`MarginRankingCriterion`](#nn.MarginRankingCriterion): ranks two inputs;

<a name="nn.Criterion"></a>
## Criterion ##
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68 changes: 34 additions & 34 deletions doc/simple.md
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# Simple layers #
Simple Modules are used for various tasks like adapting Tensor methods and providing affine transformations :

* Parameterized Modules :
* [Linear](#nn.Linear) : a linear transformation ;
* [SparseLinear](#nn.SparseLinear) : a linear transformation with sparse inputs ;
* [Add](#nn.Add) : adds a bias term to the incoming data ;
* [Mul](#nn.Mul) : multiply a single scalar factor to the incoming data ;
* [CMul](#nn.CMul) : a component-wise multiplication to the incoming data ;
* [CDiv](#nn.CDiv) : a component-wise division to the incoming data ;
* [Euclidean](#nn.Euclidean) : the euclidean distance of the input to `k` mean centers ;
* [WeightedEuclidean](#nn.WeightedEuclidean) : similar to [Euclidean](#nn.Euclidean), but additionally learns a diagonal covariance matrix ;
* Modules that adapt basic Tensor methods :
* [Copy](#nn.Copy) : a [copy](https://github.com/torch/torch7/blob/master/doc/tensor.md#torch.Tensor.copy) of the input with [type](https://github.com/torch/torch7/blob/master/doc/tensor.md#tensor-or-string-typetype) casting ;
* [Narrow](#nn.Narrow) : a [narrow](https://github.com/torch/torch7/blob/master/doc/tensor.md#tensor-narrowdim-index-size) operation over a given dimension ;
* [Replicate](#nn.Replicate) : [repeats](https://github.com/torch/torch7/blob/master/doc/tensor.md#tensor-repeattensorresult-sizes) input `n` times along its first dimension ;
* [Reshape](#nn.Reshape) : a [reshape](https://github.com/torch/torch7/blob/master/doc/maths.md#res-torchreshaperes-x-m-n) of the inputs ;
* [View](#nn.View) : a [view](https://github.com/torch/torch7/blob/master/doc/tensor.md#result-viewresult-tensor-sizes) of the inputs ;
* [Select](#nn.Select) : a [select](https://github.com/torch/torch7/blob/master/doc/tensor.md#tensor-selectdim-index) over a given dimension ;
* Modules that adapt mathematical Tensor methods :
* [Max](#nn.Max) : a [max](https://github.com/torch/torch7/blob/master/doc/maths.md#torch.max) operation over a given dimension ;
* [Min](#nn.Min) : a [min](https://github.com/torch/torch7/blob/master/doc/maths.md#torchminresval-resind-x) operation over a given dimension ;
* [Mean](#nn.Mean) : a [mean](https://github.com/torch/torch7/blob/master/doc/maths.md#res-torchmeanres-x-dim) operation over a given dimension ;
* [Sum](#nn.Sum) : a [sum](https://github.com/torch/torch7/blob/master/doc/maths.md#res-torchsumres-x) operation over a given dimension ;
* [Exp](#nn.Exp) : an element-wise [exp](https://github.com/torch/torch7/blob/master/doc/maths.md#res-torchexpres-x) operation ;
* [Abs](#nn.Abs) : an element-wise [abs](https://github.com/torch/torch7/blob/master/doc/maths.md#res-torchabsres-x) operation ;
* [Power](#nn.Power) : an element-wise [pow](https://github.com/torch/torch7/blob/master/doc/maths.md#res-torchpowres-x) operation ;
* [Square](#nn.Square) : an element-wise square operation ;
* [Sqrt](#nn.Sqrt) : an element-wise [sqrt](https://github.com/torch/torch7/blob/master/doc/maths.md#res-torchsqrtres-x) operation ;
* [MM](#nn.MM) : matrix-matrix multiplication (also supports batches of matrices) ;
* Miscellaneous Modules :
* [BatchNormalization](#nn.BatchNormalization) - mean/std normalization over the mini-batch inputs (with an optional affine transform) ;
* [Identity](#nn.Identity) : forward input as-is to output (useful with [ParallelTable](table.md#nn.ParallelTable));
* [Dropout](#nn.Dropout) : masks parts of the `input` using binary samples from a [bernoulli](http://en.wikipedia.org/wiki/Bernoulli_distribution) distribution ;
* [SpatialDropout](#nn.SpatialDropout) : Same as Dropout but for spatial inputs where adjacent pixels are strongly correlated ;
* [Padding](#nn.Padding) : adds padding to a dimension ;
* [L1Penalty](#nn.L1Penalty) : adds an L1 penalty to an input (for sparsity);
* Parameterized Modules :
* [Linear](#nn.Linear) : a linear transformation ;
* [SparseLinear](#nn.SparseLinear) : a linear transformation with sparse inputs ;
* [Add](#nn.Add) : adds a bias term to the incoming data ;
* [Mul](#nn.Mul) : multiply a single scalar factor to the incoming data ;
* [CMul](#nn.CMul) : a component-wise multiplication to the incoming data ;
* [CDiv](#nn.CDiv) : a component-wise division to the incoming data ;
* [Euclidean](#nn.Euclidean) : the euclidean distance of the input to `k` mean centers ;
* [WeightedEuclidean](#nn.WeightedEuclidean) : similar to [Euclidean](#nn.Euclidean), but additionally learns a diagonal covariance matrix ;
* Modules that adapt basic Tensor methods :
* [Copy](#nn.Copy) : a [copy](https://github.com/torch/torch7/blob/master/doc/tensor.md#torch.Tensor.copy) of the input with [type](https://github.com/torch/torch7/blob/master/doc/tensor.md#tensor-or-string-typetype) casting ;
* [Narrow](#nn.Narrow) : a [narrow](https://github.com/torch/torch7/blob/master/doc/tensor.md#tensor-narrowdim-index-size) operation over a given dimension ;
* [Replicate](#nn.Replicate) : [repeats](https://github.com/torch/torch7/blob/master/doc/tensor.md#tensor-repeattensorresult-sizes) input `n` times along its first dimension ;
* [Reshape](#nn.Reshape) : a [reshape](https://github.com/torch/torch7/blob/master/doc/maths.md#res-torchreshaperes-x-m-n) of the inputs ;
* [View](#nn.View) : a [view](https://github.com/torch/torch7/blob/master/doc/tensor.md#result-viewresult-tensor-sizes) of the inputs ;
* [Select](#nn.Select) : a [select](https://github.com/torch/torch7/blob/master/doc/tensor.md#tensor-selectdim-index) over a given dimension ;
* Modules that adapt mathematical Tensor methods :
* [Max](#nn.Max) : a [max](https://github.com/torch/torch7/blob/master/doc/maths.md#torch.max) operation over a given dimension ;
* [Min](#nn.Min) : a [min](https://github.com/torch/torch7/blob/master/doc/maths.md#torchminresval-resind-x) operation over a given dimension ;
* [Mean](#nn.Mean) : a [mean](https://github.com/torch/torch7/blob/master/doc/maths.md#res-torchmeanres-x-dim) operation over a given dimension ;
* [Sum](#nn.Sum) : a [sum](https://github.com/torch/torch7/blob/master/doc/maths.md#res-torchsumres-x) operation over a given dimension ;
* [Exp](#nn.Exp) : an element-wise [exp](https://github.com/torch/torch7/blob/master/doc/maths.md#res-torchexpres-x) operation ;
* [Abs](#nn.Abs) : an element-wise [abs](https://github.com/torch/torch7/blob/master/doc/maths.md#res-torchabsres-x) operation ;
* [Power](#nn.Power) : an element-wise [pow](https://github.com/torch/torch7/blob/master/doc/maths.md#res-torchpowres-x) operation ;
* [Square](#nn.Square) : an element-wise square operation ;
* [Sqrt](#nn.Sqrt) : an element-wise [sqrt](https://github.com/torch/torch7/blob/master/doc/maths.md#res-torchsqrtres-x) operation ;
* [MM](#nn.MM) : matrix-matrix multiplication (also supports batches of matrices) ;
* Miscellaneous Modules :
* [BatchNormalization](#nn.BatchNormalization) - mean/std normalization over the mini-batch inputs (with an optional affine transform) ;
* [Identity](#nn.Identity) : forward input as-is to output (useful with [ParallelTable](table.md#nn.ParallelTable));
* [Dropout](#nn.Dropout) : masks parts of the `input` using binary samples from a [bernoulli](http://en.wikipedia.org/wiki/Bernoulli_distribution) distribution ;
* [SpatialDropout](#nn.SpatialDropout) : Same as Dropout but for spatial inputs where adjacent pixels are strongly correlated ;
* [Padding](#nn.Padding) : adds padding to a dimension ;
* [L1Penalty](#nn.L1Penalty) : adds an L1 penalty to an input (for sparsity);

<a name="nn.Linear"></a>
## Linear ##
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