def testOnlyClusterInitialization(dataset_name, arch, epochs):
'''
Train an autoencoder defined by architecture arch and trains it with the dataset defined
:param dataset_name: Name of the dataset with which the network will be trained [MNIST, COIL20]
:param arch: Architecture of the network as a dictionary. Specification for architecture can be found in readme.md
:param epochs: Number of train epochs
:return: None - (side effect) saves the latent space and params of trained network in an appropriate location in saved_params folder
'''
rootLogger.info("Loading dataset")
dataset = DatasetHelper(dataset_name)
dataset.loadDataset()
rootLogger.info("Done loading dataset")
rootLogger.info("Creating network")
dcjc = DCJC(arch)
rootLogger.info("Done creating network")
rootLogger.info("Starting training")
dcjc.pretrainWithData(dataset, epochs, False)
def testOnlyClusterImprovement(dataset_name, arch, epochs, method):
'''
Use an initialized autoencoder and train it along with clustering loss. Assumed that pretrained autoencoder params
are available, i.e. testOnlyClusterInitialization has been run already with the given params
:param dataset_name: Name of the dataset with which the network will be trained [MNIST, COIL20]
:param arch: Architecture of the network as a dictionary. Specification for architecture can be found in readme.md
:param epochs: Number of train epochs
:param method: Can be KM or KLD - depending on whether the clustering loss is KLDivergence loss between the current KMeans distribution(Q) and a more desired one(Q^2), or if the clustering loss is just the Kmeans loss
:return: None - (side effect) saves latent space and params of the trained network
'''
rootLogger.info("Loading dataset")
dataset = DatasetHelper(dataset_name)
dataset.loadDataset()
rootLogger.info("Done loading dataset")
rootLogger.info("Creating network")
dcjc = DCJC(arch)
rootLogger.info("Starting cluster improvement")
if method == 'KM':
dcjc.doClusteringWithKMeansLoss(dataset, epochs)
elif method == 'KLD':
dcjc.doClusteringWithKLdivLoss(dataset, True, epochs)