tmp_a_test = resize(np.load('stl-data/c'+cat_a+'_test.npy'))/255. tmp_b_test = resize(np.load('stl-data/c'+cat_b+'_test.npy'))/255. n_train = np.shape(tmp_a_train)[3] n_test = np.shape(tmp_a_test)[3] print('==> preprocessing data') a_train = np.zeros((96,96,n_train)) b_train = np.zeros((96,96,n_train)) a_test = np.zeros((96,96,n_test)) b_test = np.zeros((96,96,n_test)) for i in range(n_train): a_train[:,:,i] = hl.rgb2gray(tmp_a_train[:,:,:,i]) b_train[:,:,i] = hl.rgb2gray(tmp_b_train[:,:,:,i]) for i in range(n_test): a_test[:,:,i] = hl.rgb2gray(tmp_a_test[:,:,:,i]) b_test[:,:,i] = hl.rgb2gray(tmp_b_test[:,:,:,i]) print('==> mean centering data') pop_mean = np.mean(np.concatenate((a_train,b_train),axis=2)) a_train = a_train - pop_mean b_train = b_train - pop_mean a_test = a_test - pop_mean b_test = b_test - pop_mean pop_std = np.std(np.concatenate((a_train,b_train),axis=2))
unlabeled = np.load('processed_data.npy') else: print('==> Loading data') f = h5py.File('/scratch/mad573/stl10/unlabeled.mat') u = f['X'][()] temp = np.reshape(u, (3,96,96,100000)) temp = np.swapaxes(temp,0,2) unlabeled = np.zeros((96,96,100000)) print('==> Preprocessing data') for i in range(100000): unlabeled[:,:,i] = hl.rgb2gray(temp[:,:,:,i]) if np.max(unlabeled[:,:,i])>1: unlabeled[:,:,i] = unlabeled[:,:,i]/255 np.save('processed_data.npy',unlabeled) print('==> mean centering data') pop_mean = np.mean(unlabeled) unlabeled = unlabeled - pop_mean pop_std = np.std(unlabeled) unlabeled = unlabeled/pop_std #plt.imshow(unlabeled[:,:,0], cmap=plt.get_cmap('gray'))