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'))
