def test_Rop_numpy():
"""check sparsemax Rop, aginst numpy Rop"""
z = np.random.uniform(low=-3, high=3, size=(100, 10))
w = np.random.normal(size=(10, 10))
logits = tf.placeholder(tf.float64, name='z')
weights = tf.constant(w, name='w')
sparsemax = kernel.sparsemax(logits)
# tensorflow uses the chainrule forward (left to right), meaning that:
# $dS(z)*w/d(z) = dS(z)*w/dS(z) * dS(z)/dz = Rop(S)(z, Rop(*)(w, 1))$
# Thus to test the Rop for sparsemax correctly a weight matrix is
# multiplied. This causes the grad (v) in the Rop to be $dS(z)*w/dS(z)$.
sparsemax_transform = tf.matmul(sparsemax, weights)
sparsemax_transform_grad = tf.gradients(sparsemax_transform, [logits])[0]
with tf.Session() as sess:
# chain rule
grad = np.dot(np.ones_like(z), w.T)
# Construct S(z)
properbility = sparsemax.eval({logits: z})
support = properbility > 0
# Calculate \hat{v}, which will be a vector (scalar for each z)
v_hat = np.sum(grad * support, axis=1) / np.sum(support, axis=1)
# Calculates J(z) * v
numpy_grad = support * (grad - v_hat[:, np.newaxis])
np.testing.assert_almost_equal(
sparsemax_transform_grad.eval({logits: z}),
numpy_grad
)
def test_Rop_estimated():
"""check sparsemax-loss Rop, aginst estimated Rop"""
z = np.random.uniform(low=-3, high=3, size=(100, 10))
w = np.random.normal(1)
q = np.zeros((100, 10))
q[np.arange(0, 100), np.random.randint(0, 10, size=100)] = 1
logits = tf.placeholder(tf.float64, name='z')
labels = tf.constant(q, name='q')
weight = tf.constant(w, name='w', dtype=tf.float64)
sparsemax = kernel.sparsemax(logits)
loss = kernel.sparsemax_loss(logits, sparsemax, labels)
loss_transform = loss * weight
with tf.Session() as sess:
# https://www.tensorflow.org/versions/r0.8/api_docs/python/test.html
analytical, numerical = tf.test.compute_gradient(logits,
z.shape,
loss_transform,
(100, ),
x_init_value=z,
delta=1e-9)
np.testing.assert_almost_equal(analytical, numerical, decimal=4)
def sparsemax_loss(z, q):
logits = tf.placeholder(tf.float64, name='z')
labels = tf.placeholder(tf.float64, name='q')
sparsemax = kernel.sparsemax(logits)
loss = kernel.sparsemax_loss(logits, sparsemax, labels)
with tf.Session() as sess:
return loss.eval({logits: z, labels: q})
def test_Rop_numpy():
"""check sparsemax-loss Rop, aginst numpy Rop"""
z = np.random.uniform(low=-3, high=3, size=(5, 3))
q = np.zeros((5, 3))
q[np.arange(0, 5), np.random.randint(0, 3, size=5)] = 1
logits = tf.placeholder(tf.float64, name='z')
labels = tf.placeholder(tf.float64, name='q')
sparsemax = kernel.sparsemax(logits)
loss = kernel.sparsemax_loss(logits, sparsemax, labels)
grad = tf.gradients(loss, [logits])[0]
with tf.Session() as sess:
np.testing.assert_array_equal(grad.eval({
logits: z,
labels: q
}), -q + sparsemax.eval({logits: z}))
def test_Rop_estimated():
"""check sparsemax Rop, aginst estimated Rop"""
z = np.random.uniform(low=-3, high=3, size=(100, 10))
w = np.random.normal(size=(10, 10))
logits = tf.placeholder(tf.float64, name='z')
weights = tf.constant(w, name='w')
sparsemax = kernel.sparsemax(logits)
sparsemax_transform = tf.matmul(sparsemax, weights)
with tf.Session() as sess:
# https://www.tensorflow.org/versions/r0.8/api_docs/python/test.html
analytical, numerical = tf.test.compute_gradient(
logits, z.shape,
sparsemax_transform, z.shape,
x_init_value=z, delta=1e-9
)
np.testing.assert_almost_equal(
analytical,
numerical,
decimal=4
)
def test_Rop_numpy():
"""check sparsemax-loss Rop, aginst numpy Rop"""
z = np.random.uniform(low=-3, high=3, size=(100, 10))
w = np.random.normal(size=(100, 1))
q = np.zeros((100, 10))
q[np.arange(0, 100), np.random.randint(0, 10, size=100)] = 1
logits = tf.placeholder(tf.float64, name='z')
labels = tf.constant(q, name='q')
weights = tf.constant(w, name='w')
sparsemax = kernel.sparsemax(logits)
loss = kernel.sparsemax_loss(logits, sparsemax, labels)
loss_transform = tf.expand_dims(loss, 1) * weights
loss_transform_grad = tf.gradients(loss_transform, [logits])[0]
with tf.Session() as sess:
# chain rule
grad = np.ones_like(w) * w
np.testing.assert_array_equal(
loss_transform_grad.eval({logits: z}),
grad * (-q + sparsemax.eval({logits: z})))
def sparsemax(z):
logits = tf.placeholder(tf.float64, name='z')
sparsemax = kernel.sparsemax(logits)
with tf.Session() as sess:
return sparsemax.eval({logits: z})
