def load(self, name):
modelDir = f"./models/{name}"
layerDir = [
dir for dir in os.listdir(modelDir)
if os.path.isdir(os.path.join(modelDir, dir))
]
layerDir.sort(key=lambda x: int(x.strip("layer")))
for dir in layerDir:
layerFolder = os.path.join(modelDir, dir)
if "dense.json" in os.listdir(layerFolder):
# this is a dense layer
newLayer = Dense()
newLayer.load(layerFolder)
self.layers.append(newLayer)
def load(self, name):
modelDir = f"./models/{name}"
self.encoder = MLP()
self.decoder = MLP()
self.decoder.loss = MSE()
# load encoder and decoder
for name, model in [("encoder", self.encoder),
("decoder", self.decoder)]:
layerDir = [
dir for dir in os.listdir(modelDir)
if os.path.isdir(os.path.join(modelDir, dir)) and name in dir
]
layerDir.sort(key=lambda x: int(x.strip(f"{name}_layer")))
for dir in layerDir:
layerFolder = os.path.join(modelDir, dir)
if "dense.json" in os.listdir(layerFolder):
# this is a dense layer
newLayer = Dense()
newLayer.load(layerFolder)
model.layers.append(newLayer)
# load aditional information about sampler
with open(f"{modelDir}/sampler.json", "r") as file:
data = json.load(file)
inputDim = data["inputDim"]
outputDim = data["outputDim"]
self.sampler = Sampler(inputDim, outputDim)
# load mean and logvar layer
self.sampler.mean = Dense()
self.sampler.mean.load(os.path.join(modelDir, f"sampler_mean"))
self.sampler.logVar = Dense()
self.sampler.logVar.load(os.path.join(modelDir, f"sampler_logvar"))
self.layers = self.encoder.layers + [
self.sampler.mean, self.sampler.logVar
] + self.decoder.layers
def load(self, name):
modelDir = f"./models/{name}"
# load generator and discriminator
for name, model in [("generator", self.generator),
("discriminator", self.discriminator)]:
layerDir = [
dir for dir in os.listdir(modelDir)
if os.path.isdir(os.path.join(modelDir, dir)) and name in dir
]
layerDir.sort(key=lambda x: int(x.strip(f"{name}_layer")))
for dir in layerDir:
layerFolder = os.path.join(modelDir, dir)
if "dense.json" in os.listdir(layerFolder):
# this is a dense layer
newLayer = Dense()
newLayer.load(layerFolder)
model.layers.append(newLayer)
self.layers = self.generator.layers + self.discriminator.layers
