batch_size = 256
np.random.seed(1234) # set seed
lv = 1e-2 # lambda_v/lambda_n in CDL
dir_save = 'cdl%d' % p
if not os.path.isdir(dir_save):
os.system('mkdir %s' % dir_save)
fp = open(dir_save + '/cdl.log', 'w')
print 'p%d: lambda_v/lambda_u/ratio/K: %f/%f/%f/%d' % (p, lambda_v,
lambda_u, lv, K)
fp.write('p%d: lambda_v/lambda_u/ratio/K: %f/%f/%f/%d\n' % \
(p,lambda_v,lambda_u,lv,K))
fp.close()
if is_dummy:
X = data.get_dummy_mult()
R = data.read_dummy_user()
else:
X = data.get_mult()
R = data.read_user()
# set to INFO to see less information during training
logging.basicConfig(level=logging.DEBUG)
#ae_model = AutoEncoderModel(mx.gpu(0), [784,500,500,2000,10], pt_dropout=0.2,
# internal_act='relu', output_act='relu')
ae_model = AutoEncoderModel(mx.cpu(2), [X.shape[1], 100, K],
pt_dropout=0.2,
internal_act='relu',
output_act='relu')
train_X = X
#ae_model.layerwise_pretrain(train_X, 256, 50000, 'sgd', l_rate=0.1, decay=0.0,
num_iter = 34000
batch_size = 256
np.random.seed(1234) # set seed
lv = 1e-2 # lambda_v/lambda_n in CDL
dir_save = 'cdl%d' % p
if not os.path.isdir(dir_save):
os.system('mkdir %s' % dir_save)
fp = open(dir_save+'/cdl.log','w')
print 'p%d: lambda_v/lambda_u/ratio/K: %f/%f/%f/%d' % (p,lambda_v,lambda_u,lv,K)
fp.write('p%d: lambda_v/lambda_u/ratio/K: %f/%f/%f/%d\n' % \
(p,lambda_v,lambda_u,lv,K))
fp.close()
if is_dummy:
X = data.get_dummy_mult()
R = data.read_dummy_user()
else:
X = data.get_mult()
R = data.read_user()
# set to INFO to see less information during training
logging.basicConfig(level=logging.DEBUG)
#ae_model = AutoEncoderModel(mx.gpu(0), [784,500,500,2000,10], pt_dropout=0.2,
# internal_act='relu', output_act='relu')
#mx.cpu() no param needed for cpu.
ae_model = AutoEncoderModel(mx.cpu(), [X.shape[1],100,K],
pt_dropout=0.2, internal_act='relu', output_act='relu')
train_X = X
#ae_model.layerwise_pretrain(train_X, 256, 50000, 'sgd', l_rate=0.1, decay=0.0,