def test_neg_cross_entropy_colwise():
X, Y = T.matrix(), T.matrix()
X.tag.test_value = test_X > 0.2
Y.tag.test_value = test_Y
dist = cat_ce(X, Y).sum(axis=0)
f = theano.function([X, Y], dist, mode='FAST_COMPILE')
res = f(test_X, test_Y)
correct = np.allclose(res, [[0.49795728, 0.48932523, 0.61908916]])
assert correct, 'cat_ce loss colwise not working'
def test_neg_cross_entropy_rowwise():
X, Y = T.matrix(), T.matrix()
X.tag.test_value = test_X > 0.2
Y.tag.test_value = test_Y
dist = cat_ce(X, Y).sum(axis=1)
f = theano.function([X, Y], dist, mode='FAST_COMPILE')
res = f(test_X, test_Y)
correct = np.allclose(res, [0.85798192, 0.74838975])
assert correct, 'cat_ce loss rowwise not working'
def test_neg_cross_entropy():
X, Y = T.matrix(), T.matrix()
X.tag.test_value = test_X > 0.2
Y.tag.test_value = test_Y
dist = cat_ce(X, Y).sum()
f = theano.function([X, Y], dist, mode='FAST_COMPILE')
res = f(test_X, test_Y)
correct = np.allclose(res, 1.6063716678910529)
assert correct, 'cat_ce loss not working'
def test_neg_cross_entropy():
X, Y = T.matrix(), T.matrix()
X.tag.test_value = test_X > 0.2
Y.tag.test_value = test_Y
dist = cat_ce(X, Y).sum()
f = theano.function([X, Y], dist, mode='FAST_COMPILE')
res = f(test_X, test_Y)
correct = np.allclose(res, 1.6063716678910529)
assert correct, 'cat_ce loss not working'
def test_neg_cross_entropy_colwise():
X, Y = T.matrix(), T.matrix()
X.tag.test_value = test_X > 0.2
Y.tag.test_value = test_Y
dist = cat_ce(X, Y).sum(axis=0)
f = theano.function([X, Y], dist, mode='FAST_COMPILE')
res = f(test_X, test_Y)
correct = np.allclose(res, [[0.49795728, 0.48932523, 0.61908916]])
assert correct, 'cat_ce loss colwise not working'
def test_neg_cross_entropy_rowwise():
X, Y = T.matrix(), T.matrix()
X.tag.test_value = test_X > 0.2
Y.tag.test_value = test_Y
dist = cat_ce(X, Y).sum(axis=1)
f = theano.function([X, Y], dist, mode='FAST_COMPILE')
res = f(test_X, test_Y)
correct = np.allclose(res, [0.85798192, 0.74838975])
assert correct, 'cat_ce loss rowwise not working'
