Sn = torch.LongTensor(Sn).cuda()
Sn_train = torch.LongTensor(Sn_train).cuda()
Sn_test = torch.LongTensor(Sn_test).cuda()
# Model building
model = CPMNet_Works(device, view_num, trainData.num_examples,
testData.num_examples, layer_size, args.lsd_dim,
learning_rate, args.lamb).cuda()
# train
gt1 = trainData.labels.reshape(trainData.num_examples)
gt1 = gt1.reshape([gt1.shape[0], 1])
gt1 = torch.LongTensor(gt1)
class_num = (torch.max(gt1) - torch.min(gt1) + 1).cpu()
batch_size = torch.tensor(gt1.shape[0])
label_onehot = (torch.zeros(batch_size, class_num).scatter_(
1, gt1 - 1,
1)) # gt1 begin from 1 so we need to set the minimum of it to 0
H_train = model.train(trainData.data, Sn_train, label_onehot, gt1,
epoch[0])
# test
gt2 = testData.labels.reshape(testData.num_examples)
gt2 = gt2.reshape([gt2.shape[0], 1])
gt2 = torch.LongTensor(gt2)
H_test = model.test(testData.data, Sn_test, epoch[1])
label_pre = classfiy.ave(H_train, H_test, label_onehot.cuda(),
testData.num_examples)
print('Accuracy on the test set is {:.4f}'.format(
accuracy_score(testData.labels, label_pre)))
layer_size = [[350, outdim_size[i]] for i in range(view_num)]
# set parameter
epoch = [args.epochs_train, args.epochs_test]
learning_rate = [0.01, 0.01]
# Randomly generated missing matrix
Sn = get_sn(view_num, trainData.num_examples + testData.num_examples,
args.missing_rate)
Sn_train = Sn[np.arange(trainData.num_examples)]
Sn_test = Sn[np.arange(testData.num_examples) + trainData.num_examples]
# Model building
model = CPMNets(view_num,
trainData.num_examples,
testData.num_examples,
layer_size,
lsd_dim=args.lsd_dim,
learning_rate=learning_rate,
lamb=args.lamb)
# train
model.train(trainData.data,
Sn_train,
trainData.labels.reshape(trainData.num_examples),
epoch[0],
step=[10, 10])
H_train = model.get_h_train()
# test
model.test(testData.data, Sn_test,
testData.labels.reshape(testData.num_examples), epoch[1])
H_test = model.get_h_test()
label_pre = classfiy.ave(H_train, H_test, trainData.labels)
print('Accuracy on the test set is {:.4f}'.format(
accuracy_score(testData.labels, label_pre)))