def l21RDAE(X, layers, lamda, folder, learning_rate = 0.15, inner = 100, outer = 10, batch_size = 133,inputsize = (28,28)):
if not os.path.isdir(folder):
os.makedirs(folder)
os.chdir(folder)
with tf.Graph().as_default():
with tf.Session() as sess:
rael21 = l21RDA.RobustL21Autoencoder(sess = sess, lambda_= lamda*X.shape[0], layers_sizes=layers)
l21L, l21S = rael21.fit(X = X, sess = sess, inner_iteration = inner, iteration = outer, batch_size = batch_size, learning_rate = learning_rate, verbose = True)
l21R = rael21.getRecon(X = X, sess = sess)
l21H = rael21.transform(X, sess)
Image.fromarray(I.tile_raster_images(X=l21S,img_shape=inputsize, tile_shape=(10, 10),tile_spacing=(1, 1))).save(r"l21S.png")
Image.fromarray(I.tile_raster_images(X=l21R,img_shape=inputsize, tile_shape=(10, 10),tile_spacing=(1, 1))).save(r"l21R.png")
Image.fromarray(I.tile_raster_images(X=l21L,img_shape=inputsize, tile_shape=(10, 10),tile_spacing=(1, 1))).save(r"l21L.png")
l21S.dump("l21S.npk")
os.chdir("../")
def experiment_frame():
X = np.load(r"./data/data.npk")
inner = 50
outer = 20
lamda_list = np.arange(0.00035, 0.0015, 0.0001)
layers = [784, 400, 200] ## S trans
folder = r"OutlierDetectionResult"
if not os.path.isdir(folder):
os.makedirs(folder)
os.chdir(folder)
image_X = Image.fromarray(
I.tile_raster_images(X=X,
img_shape=(28, 28),
tile_shape=(10, 10),
tile_spacing=(1, 1)))
image_X.save(r"X.png")
for lam in lamda_list:
folder = "lam" + str(lam)
l21RDAE(X=X,
layers=layers,
lamda=lam,
folder=folder,
learning_rate=0.001,
inner=inner,
outer=outer,
batch_size=133,
re_init=True,
inputsize=(28, 28))
os.chdir("../")