parser.add_argument('--visualize', default=False, action='store_true')
args = parser.parse_args()
print(args)
optimizer = 'adam'
from datasets import load_mnist, load_mnist_test, load_usps, load_pendigits, load_fashion, load_har
label_names = None
if args.dataset == 'mnist':
x, y = load_mnist()
elif args.dataset == 'mnist-test':
x, y = load_mnist_test()
elif args.dataset == 'usps':
x, y = load_usps()
elif args.dataset == 'pendigits':
x, y = load_pendigits()
elif args.dataset == 'fashion':
x, y, label_names = load_fashion()
elif args.dataset == 'har':
x, y, label_names = load_har()
shape = [x.shape[-1], 500, 500, 2000, args.n_clusters]
autoencoder = autoencoder(shape)
hidden = autoencoder.get_layer(name='encoder_%d' % (len(shape) - 2)).output
encoder = Model(inputs=autoencoder.input, outputs=hidden)
pretrain_time = time()
# Pretrain autoencoders before clustering
if args.ae_weights is None:
parser.add_argument('--update_interval', default=1, type=int)
parser.add_argument('--tol', default=0.001, type=float)
parser.add_argument('--ae_weights', default=None)
parser.add_argument('--save_dir', default='results/idec')
args = parser.parse_args()
print(args)
# load dataset
optimizer = 'adam' # SGD(lr=0.01, momentum=0.99)
from datasets import load_mnist, load_reuters, load_usps, load_pendigits, load_mydata
if args.dataset == 'mnist': # recommends: n_clusters=10, update_interval=140
x, y = load_mnist()
elif args.dataset == 'usps': # recommends: n_clusters=10, update_interval=30
x, y = load_usps('data/usps')
elif args.dataset == 'pendigits':
x, y = load_pendigits('data/pendigits')
elif args.dataset == 'reutersidf10k': # recommends: n_clusters=4, update_interval=20
x, y = load_reuters('data/reuters')
elif args.dataset == 'mydata': # recommends: n_clusters=4, update_interval=20
x, y = load_mydata(path='./data/mydata')
if args.update_interval == 0: # one epoch
args.update_interval = int(x.shape[0] / args.batch_size)
# Define IDEC model
idec = IDEC(dims=[x.shape[-1], 500, 500, 2000, 18],
n_clusters=args.n_clusters)
#plot_model(idec.model, to_file='idec_model.png', show_shapes=True)
idec.model.summary()
t0 = time()