def fit(self, X, sess, learning_rate=0.05, inner_iteration = 50,
iteration=20, batch_size=40, verbose=False):
## The first layer must be the input layer, so they should have same sizes.
assert X.shape[1] == self.layers_sizes[0]
## initialize L, S, mu(shrinkage operator)
self.L = np.zeros(X.shape)
self.S = np.zeros(X.shape)
if verbose:
print ("X shape: ", X.shape)
print ("L shape: ", self.L.shape)
print ("S shape: ", self.S.shape)
for it in range(iteration):
if verbose:
print ("Out iteration: " , it)
## alternating project, first project to L
self.L = np.array(X - self.S,dtype=float)
## Using L to train the auto-encoder
self.SAE.fit(self.L, sess = sess,
iteration = inner_iteration,
learning_rate = learning_rate,
batch_size = batch_size,
verbose = verbose)
## get optmized L
self.L = self.SAE.getRecon(X = self.L, sess = sess)
## alternating project, now project to S and shrink S
self.S = SHR.l21shrink(self.lambda_, (X - self.L).T).T
return self.L, self.S
def test_shrinkage():
# Take the data from the data forlder of RobustAutoencoder repo ->
x_data = np.random.randn(1000, 500) + 1
# Take the data from the data forlder of RobustAutoencoder repo <-
# import other shrinkage ->
re_path = os.path.abspath(os.path.join(
'../RobustAutoencoder/model/')) # path is relative to notebook path.
if re_path not in sys.path:
sys.path.append(re_path)
from shrink import l21shrink as other21
from shrink import l1shrink as other1
# import other shrinkage <-
# compare shrinking ->
eps = np.linspace(0, 2, 20)
test21_fail = False
for ee in eps:
other_m = other21.l21shrink(ee, x_data)
my_m = l21shrink(ee, x_data)
print("Test shrink21 fails: ", np.any(other_m != my_m))
test21_fail = np.any(other_m != my_m)
if test21_fail:
break
test1_fail = False
for ee in eps:
other_m = other1.shrink(ee, x_data.ravel())
my_m = shrink(ee, x_data.ravel())
print("Test shrink1 fails: ", np.any(other_m != my_m))
test1_fail = np.any(other_m != my_m)
if test1_fail:
break
def fit(self,
X,
sess,
learning_rate=0.15,
inner_iteration=50,
iteration=20,
batch_size=133,
re_init=False,
verbose=False):
## The first layer must be the input layer, so they should have same sizes.
assert X.shape[1] == self.layers_sizes[0]
## initialize L, S
self.L = np.zeros(X.shape)
self.S = np.zeros(X.shape)
##LS0 = self.L + self.S
## To estimate the size of input X
if verbose:
print "X shape: ", X.shape
print "L shape: ", self.L.shape
print "S shape: ", self.S.shape
for it in xrange(iteration):
if verbose:
print "Out iteration: ", it
## alternating project, first project to L
self.L = X - self.S
## Using L to train the auto-encoder
self.AE.fit(self.L,
sess=sess,
iteration=inner_iteration,
learning_rate=learning_rate,
batch_size=batch_size,
init=re_init,
verbose=verbose)
## get optmized L
self.L = self.AE.getRecon(X=self.L, sess=sess)
## alternating project, now project to S and shrink S
self.S = SHR.l21shrink(self.lambda_, (X - self.L).T).T
return self.L, self.S
