77 lines
1.5 KiB
Markdown
77 lines
1.5 KiB
Markdown
## Basic Layers
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These core layers form the foundation of almost all neural networks.
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```@docs
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Chain
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Dense
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```
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## Convolution and Pooling Layers
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These layers are used to build convolutional neural networks (CNNs).
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```@docs
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Conv
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MaxPool
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MeanPool
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DepthwiseConv
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ConvTranspose
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```
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## Recurrent Layers
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Much like the core layers above, but can be used to process sequence data (as well as other kinds of structured data).
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```@docs
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RNN
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LSTM
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GRU
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Flux.Recur
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```
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## Other General Purpose Layers
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These are marginally more obscure than the Basic Layers.
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But incontrast to the layers described in the other sections are not readily grouped around a paparticular purpose (e.g. CNNs or RNNs).
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```@docs
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MaxOut
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```
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# Normalisation & Regularisation
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These layers don't affect the structure of the network but may improve training times or reduce overfitting.
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```@docs
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Flux.testmode!
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BatchNorm
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Dropout
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LayerNorm
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```
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## Activation Functions
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Non-linearities that go between layers of your model. Most of these functions are defined in [NNlib](https://github.com/FluxML/NNlib.jl) but are available by default in Flux.
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Note that, unless otherwise stated, activation functions operate on scalars. To apply them to an array you can call `σ.(xs)`, `relu.(xs)` and so on.
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```@docs
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σ
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relu
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leakyrelu
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elu
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swish
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```
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## Normalisation & Regularisation
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These layers don't affect the structure of the network but may improve training times or reduce overfitting.
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```@docs
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Flux.testmode!
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BatchNorm
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Dropout
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AlphaDropout
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LayerNorm
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```
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