def prepare_model(opt):
if opt.dataset == 'mnist':
feat_layer = ndf.MNISTFeatureLayer(opt.feat_dropout)
elif opt.dataset == 'adult':
feat_layer = ndf.UCIAdultFeatureLayer(opt.feat_dropout)
elif opt.dataset == 'letter':
feat_layer = ndf.UCILetterFeatureLayer(opt.feat_dropout)
elif opt.dataset == 'yeast':
feat_layer = ndf.UCIYeastFeatureLayer(opt.feat_dropout)
else:
raise NotImplementedError
forest = ndf.Forest(n_tree=opt.n_tree,
tree_depth=opt.tree_depth,
n_in_feature=feat_layer.get_out_feature_size(),
tree_feature_rate=opt.tree_feature_rate,
n_class=opt.n_class,
jointly_training=opt.jointly_training)
model = ndf.NeuralDecisionForest(feat_layer, forest)
if opt.cuda:
model = model.cuda()
else:
model = model.cpu()
return model
def prepare_model(opt):
if opt.dataset == 'mnist':
feat_layer = ndf.MNISTFeatureLayer(opt.feat_dropout)
elif opt.dataset == 'adult':
feat_layer = ndf.UCIAdultFeatureLayer(opt.feat_dropout)
elif opt.dataset == 'letter':
feat_layer = ndf.UCILetterFeatureLayer(opt.feat_dropout)
elif opt.dataset == 'yeast':
feat_layer = ndf.UCIYeastFeatureLayer(opt.feat_dropout)
elif opt.dataset == 'gisette':
feat_layer = ndf.UCIGisetteFeatureLayer(opt.feat_dropout)
elif opt.dataset == 'arrhythmia':
feat_layer = ndf.UCIArrhythmiaFeatureLayer(opt.feat_dropout)
elif opt.dataset == 'cardiotocography':
feat_layer = ndf.UCICardiotocographyFeatureLayer(opt.feat_dropout)
elif opt.dataset == 'breastcancer':
feat_layer = ndf.UCIBreastcancerFeatureLayer(opt.feat_dropout)
elif opt.dataset == 'nomao':
feat_layer = ndf.UCINomaoFeatureLayer(opt.feat_dropout)
elif opt.dataset == 'mutiplefeatures':
feat_layer = ndf.UCIMutiplefeaturesFeatureLayer(opt.feat_dropout)
elif opt.dataset == 'madelon':
feat_layer = ndf.UCIMadelonFeatureLayer(opt.feat_dropout)
elif opt.dataset == 'secom':
feat_layer = ndf.UCISecomFeatureLayer(opt.feat_dropout)
elif opt.dataset == 'isolet5':
feat_layer = ndf.UCIIsolet5FeatureLayer(opt.feat_dropout)
else:
raise NotImplementedError
forest = ndf.Forest(n_tree=opt.n_tree, tree_depth=opt.tree_depth, n_in_feature=feat_layer.get_out_feature_size(),
tree_feature_rate=opt.tree_feature_rate, n_class=opt.n_class,
jointly_training=opt.jointly_training)
model = ndf.NeuralDecisionForest(feat_layer, forest)
if opt.cuda:
model = model.cuda()
else:
model = model.cpu()
return model