def RDAE_DAE_l21RDAE_compressUser(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 = l21RDAE.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.T,img_shape=inputsize, tile_shape=(10, 10),tile_spacing=(1, 1))).save(r"l21S.png")
Image.fromarray(I.tile_raster_images(X=l21R.T,img_shape=inputsize, tile_shape=(10, 10),tile_spacing=(1, 1))).save(r"l21R.png")
Image.fromarray(I.tile_raster_images(X=l21L.T,img_shape=inputsize, tile_shape=(10, 10),tile_spacing=(1, 1))).save(r"l21L.png")
l21H.dump(r"l21H.pkl")
np.array(rael21.errors).dump(r"l21error.pkl")
l21S.dump("l21S.pkl")
with tf.Graph().as_default():
with tf.Session() as sess:
rae = RDAE.RDAE(sess = sess, lambda_ = lamda * 10, layers_sizes = layers)
rL, rS = rae.fit(X = X ,sess = sess, learning_rate = learning_rate, batch_size = batch_size, inner_iteration = inner, iteration = outer, verbose=True)
rR = rae.getRecon(X, sess)
rH = rae.transform(X, sess)
Image.fromarray(I.tile_raster_images(X=rR.T,img_shape=inputsize, tile_shape=(10, 10),tile_spacing=(1, 1))).save(r"rR.png")
Image.fromarray(I.tile_raster_images(X=rS.T,img_shape=inputsize, tile_shape=(10, 10),tile_spacing=(1, 1))).save(r"rS.png")
Image.fromarray(I.tile_raster_images(X=rL.T,img_shape=inputsize, tile_shape=(10, 10),tile_spacing=(1, 1))).save(r"rL.png")
rH.dump(r"rH.pkl")
np.array(rae.errors).dump(r"RDAEerror.pkl")
rS.dump("rS.pkl")
os.chdir("../")
def tune_l21RDAE(x,
lam_list=[50],
learn_rates=[0.1],
inner=150,
outter=10,
batch_size=133):
with tf.Session() as sess:
rae = l21RDAE.RobustL21Autoencoder(sess=sess,
lambda_=lam_list[0],
layers_sizes=[784, 400, 255, 100])
L, S = rae.fit(x,
sess=sess,
inner_iteration=inner,
iteration=outter,
batch_size=batch_size,
verbose=True)
ae = Deep_Autoencoder(sess=sess, input_dim_list=[784, 400, 225, 100])
error = ae.fit(x,
sess=sess,
learning_rate=learn_rates[0],
iteration=inner * outer,
batch_size=batch_size,
verbose=True)
return rae.errors, error
def compare_RDAE_DAE_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.Session() as sess:
ae = DAE.Deep_Autoencoder(sess=sess, input_dim_list=layers)
error = ae.fit(X=X,
sess=sess,
learning_rate=learning_rate,
iteration=inner * outer,
batch_size=batch_size,
verbose=True)
dR = ae.getRecon(X=X, sess=sess)
dH = ae.transform(X, sess)
Image.fromarray(
I.tile_raster_images(X=dR,
img_shape=inputsize,
tile_shape=(10, 10),
tile_spacing=(1, 1))).save(r"dR.png")
dH.dump("dH.pkl")
np.array(error).dump(r"DAEerror.pkl")
rael21 = l21RDAE.RobustL21Autoencoder(sess=sess,
lambda_=lamda,
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")
l21H.dump(r"l21H.pkl")
np.array(rael21.errors).dump(r"l21error.pkl")
rae = RDAE.RDAE(sess=sess, lambda_=lamda * 10, layers_sizes=layers)
rL, rS = rae.fit(X=X,
sess=sess,
learning_rate=learning_rate,
batch_size=batch_size,
inner_iteration=inner,
iteration=outer,
verbose=True)
rR = rae.getRecon(X, sess)
rH = rae.transform(X, sess)
Image.fromarray(
I.tile_raster_images(X=rR,
img_shape=inputsize,
tile_shape=(10, 10),
tile_spacing=(1, 1))).save(r"rR.png")
Image.fromarray(
I.tile_raster_images(X=rS,
img_shape=inputsize,
tile_shape=(10, 10),
tile_spacing=(1, 1))).save(r"rS.png")
Image.fromarray(
I.tile_raster_images(X=rL,
img_shape=inputsize,
tile_shape=(10, 10),
tile_spacing=(1, 1))).save(r"rL.png")
rH.dump(r"rH.pkl")
np.array(rae.errors).dump(r"RDAEerror.pkl")
os.chdir("../")
