70 lines
1.5 KiB
Markdown
70 lines
1.5 KiB
Markdown
## Basic Layers
|
|
|
|
These core layers form the foundation of almost all neural networks.
|
|
|
|
```@docs
|
|
Chain
|
|
Dense
|
|
```
|
|
|
|
## Convolution and Pooling Layers
|
|
|
|
These layers are used to build convolutional neural networks (CNNs).
|
|
|
|
```@docs
|
|
Conv
|
|
MaxPool
|
|
GlobalMaxPool
|
|
MeanPool
|
|
GlobalMeanPool
|
|
DepthwiseConv
|
|
ConvTranspose
|
|
CrossCor
|
|
flatten
|
|
```
|
|
|
|
## Recurrent Layers
|
|
|
|
Much like the core layers above, but can be used to process sequence data (as well as other kinds of structured data).
|
|
|
|
```@docs
|
|
RNN
|
|
LSTM
|
|
GRU
|
|
Flux.Recur
|
|
Flux.reset!
|
|
```
|
|
|
|
## Other General Purpose Layers
|
|
These are marginally more obscure than the Basic Layers.
|
|
But in contrast to the layers described in the other sections are not readily grouped around a particular purpose (e.g. CNNs or RNNs).
|
|
|
|
```@docs
|
|
Maxout
|
|
SkipConnection
|
|
```
|
|
|
|
|
|
## Normalisation & Regularisation
|
|
|
|
These layers don't affect the structure of the network but may improve training times or reduce overfitting.
|
|
|
|
```@docs
|
|
Flux.normalise
|
|
BatchNorm
|
|
Flux.dropout
|
|
Dropout
|
|
AlphaDropout
|
|
LayerNorm
|
|
InstanceNorm
|
|
GroupNorm
|
|
```
|
|
|
|
### Testmode
|
|
|
|
Many normalisation layers behave differently under training and inference (testing). By default, Flux will automatically determine when a layer evaluation is part of training or inference. Still, depending on your use case, it may be helpful to manually specify when these layers should be treated as being trained or not. For this, Flux provides `Flux.testmode!`. When called on a model (e.g. a layer or chain of layers), this function will place the model into the mode specified.
|
|
|
|
```@docs
|
|
Flux.testmode!
|
|
trainmode!
|
|
``` |