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Sequential
Sequential
is a type of networks and it is very similar to the Sequential
in Keras. It can be added with multiple layers and trained with an optimizer. Its variable type is a mutable constructor.
To create a new sequential model, a variable has to be initialized:
Sequential()
Squential
uses its property add_layer
to add layer. It autofills the proper size of input data (input_shape
) automatically. However, you can still override them just by giving in the key arguments.
(model::Sequential, layer::Any; args...)
model
: self reference
layer
: a type of layer
args
: parameters of the layer for initialization
If the model is used for prediction but not training, it requires to be initialized with an extra function before activated.
Sequential.initialize(model::Sequential, mini_batch::Int64)
model
: self reference
mini_batch
: the batch size of input data
Sequential.activate(model::Sequential, data::Array{Float32})
model
: self reference
data
: input data
return
: output data
Sequential
model can be saved and loaded by using the tools save_Sequential
and load_Sequential
.
save_Sequential(model::Sequential, path::String)
model
: target sequential model
path
: path
load_Sequential(path::String)
path
: path of the target model
return
: target model
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