def __init__(self):
K.set_learning_phase(0)
args = Args(os.environ['GPU'],
float(os.environ['GPU_MEMORY_FRACTION']),
os.environ['DATA_SET'], int(os.environ['BATCH_SIZE']))
global graph
graph = tf.get_default_graph()
self.params = get_spectralnet_config(args)
ktf.set_session(get_session(args.gpu_memory_fraction))
self.batch_size = args.batch_size
self.batch_sizes = {
'Unlabeled': self.batch_size,
'Labeled': self.batch_size,
'Orthonorm': self.batch_size,
}
n_clusters = self.params['n_clusters']
y_labeled_onehot = np.empty((0, n_clusters))
# spectralnet has three inputs -- they are defined here
input_shape = [n_clusters]
inputs = {
'Unlabeled': Input(shape=input_shape, name='UnlabeledInput'),
'Labeled': Input(shape=input_shape, name='LabeledInput'),
'Orthonorm': Input(shape=input_shape, name='OrthonormInput'),
}
# Load Spectral net
y_true = tf.placeholder(tf.float32,
shape=(None, n_clusters),
name='y_true')
spectralnet_model_path = os.path.join(self.params['model_path'],
'spectral_net')
self.spectral_net = networks.SpectralNet(inputs,
self.params['arch'],
self.params.get('spec_reg'),
y_true,
y_labeled_onehot,
n_clusters,
self.params['affinity'],
self.params['scale_nbr'],
self.params['n_nbrs'],
self.batch_sizes,
spectralnet_model_path,
siamese_net=None,
train=False,
x_train=None)
self.clustering_algo = joblib.load(
os.path.join(self.params['model_path'], 'spectral_net',
'clustering_aglo.sav'))
from core.util import get_session
# add directories in src/ to path
# sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__),'..')))
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=str, help='gpu number to use', default='')
parser.add_argument('--gpu_memory_fraction',
type=float,
help='gpu percentage to use',
default='1.0')
parser.add_argument('--dset', type=str, help='dataset to use', default='mnist')
args = parser.parse_args()
params = get_autoencoder_config(args)
K.set_session(get_session(args.gpu_memory_fraction))
data = load_spectral_data(params['data_path'], args.dset)
def get_reconstruction_mse(x):
x_embedded = ae.predict_embedding(x)
x_recon = ae.predict_reconstruction(x_embedded)
return np.mean(np.square(x - x_recon))
# RUN Train
x_train = data['spectral']['train_and_test'][0]
x_test = data['spectral']['train_and_test'][2]
json_path = 'pretrain_weights/ae_{}.json'.format(args.dset)
weights_path = '{}/ae_{}_weights.h5'.format(params['model_path'], args.dset)