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Basic Layers
These core layers form the foundation of almost all neural networks.
Chain
Dense
Convolution and Pooling Layers
These layers are used to build convolutional neural networks (CNNs).
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).
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).
Maxout
SkipConnection
Normalisation & Regularisation
These layers don't affect the structure of the network but may improve training times or reduce overfitting.
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.
Flux.testmode!
trainmode!
Cost Functions
Flux.mae
Flux.mse
Flux.msle
Flux.huber_loss
Flux.crossentropy
Flux.logitcrossentropy
Flux.binarycrossentropy
Flux.logitbinarycrossentropy
Flux.kldivergence
Flux.poisson
Flux.hinge
Flux.squared_hinge
Flux.dice_coeff_loss
Flux.tversky_loss