aeftimia

Keybase proof

I hereby claim:

• I am aeftimia on github.
• I am aeftimia (https://keybase.io/aeftimia) on keybase.
• I have a public key whose fingerprint is 4D49 0581 C078 51D8 61C6 5214 5B2B 8E5D 30B9 A0F2

To claim this, I am signing this object:

aeftimia

Keybase proof

I hereby claim:

• I am aeftimia on github.
• I am aeftimia (https://keybase.io/aeftimia) on keybase.
• I have a public key whose fingerprint is 59D0 61B3 4E9E A994 4370 AD00 A3F8 514D 5563 3C57

To claim this, I am signing this object:

aeftimia

Verifying my Blockstack ID is secured with the address 1JZXzQS1q1KJ1qVu5SWoHCi2RmeQxbgAwQ

aeftimia

import numpy
import tensorflow

from keras.constraints import Constraint
from tensorflow.linalg import expm


class Orthogonal(Constraint):
    """Orthogonal weight constraint.
    Constrains the weights incident to each hidden unit

aeftimia

import numpy
import tensorflow

from tensorflow.linalg import expm


def orthogonalize(w, axis=None, orthonormal=False):
    """Orthogonal weight constraint.
    Constrains the weights incident to each hidden unit
    to be orthogonal when there are more inputs than hidden units.

aeftimia

import numpy

from keras.datasets import cifar10
from sklearn import preprocessing
from sklearn.tree import DecisionTreeClassifier

(X_train, y_train), (X_test, y_test) = cifar10.load_data()
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')

aeftimia

import numpy
import scipy
import scipy.spatial
import scipy.optimize

def constraint(alen, blen):
    return numpy.eye(alen)[..., numpy.newaxis] * numpy.ones(blen)[numpy.newaxis, numpy.newaxis]

def emd(a, b, a_weights=None, b_weights=None):
    if a_weights is None: