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GAN
GAN
is Generative Adversarial Network.
GAN
has to be initialized with a noise generator.
GAN(noise_generator)
GAN
uses add_Glayer
for Generator and add_Dlayer
for Discriminator to add layers. Since the layers are all added to the same array, it has to be finished with all the layers in the generator first then the discriminator. Just like Sequential
, it sets the size of input data for each layer automatically.
GAN.add_Glayer(model::GAN, layer::Any;args...)
GAN.add_Dlayer(model::GAN, layer::Any;args...)
model
: self reference
layer
: a type of layer
args
: parameters of the layer for initialization
If the model is used for generation but not training, it requires to be initialized with an extra function before activated.
GAN.initialize(model::GAN, mini_batch::Int64)
model
: self reference
mini_batch
: the batch size of input data
GAN.activate_Generator(model::GAN)
model
: self reference
return
: output data
GAN
model can be saved and loaded by using the tools save_GAN and load_GAN
.
save_GAN(model::GAN, path::String)
model
: self reference
path
: path
load_GAN(path::String, noise_generator::Any)
path
: path of the target model
noise_generator
: a noise generator
return
: target model
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