def testBijectorUnknownShape(self):
with self.test_session():
softmax = SoftmaxCentered()
self.assertEqual("softmax_centered", softmax.name)
x = array_ops.placeholder(shape=[2, None], dtype=dtypes.float32)
real_x = np.log([[2., 3, 4], [4., 8, 12]])
y = array_ops.placeholder(shape=[2, None], dtype=dtypes.float32)
real_y = [[0.2, 0.3, 0.4, 0.1], [0.16, 0.32, 0.48, 0.04]]
self.assertAllClose(real_y,
softmax.forward(x).eval(feed_dict={x: real_x}))
self.assertAllClose(real_x,
softmax.inverse(y).eval(feed_dict={y: real_y}))
self.assertAllClose(-np.sum(np.log(real_y), axis=1),
softmax.inverse_log_det_jacobian(
y,
event_ndims=1).eval(feed_dict={y: real_y}),
atol=0.,
rtol=1e-7)
self.assertAllClose(-softmax.inverse_log_det_jacobian(
y, event_ndims=1).eval(feed_dict={y: real_y}),
softmax.forward_log_det_jacobian(
x,
event_ndims=1).eval(feed_dict={x: real_x}),
atol=0.,
rtol=1e-7)
def testBijectorUnknownShape(self):
with self.test_session():
softmax = SoftmaxCentered()
self.assertEqual("softmax_centered", softmax.name)
x = array_ops.placeholder(shape=[2, None], dtype=dtypes.float32)
real_x = np.log([[2., 3, 4], [4., 8, 12]])
y = array_ops.placeholder(shape=[2, None], dtype=dtypes.float32)
real_y = [[0.2, 0.3, 0.4, 0.1], [0.16, 0.32, 0.48, 0.04]]
self.assertAllClose(real_y, softmax.forward(x).eval(
feed_dict={x: real_x}))
self.assertAllClose(real_x, softmax.inverse(y).eval(
feed_dict={y: real_y}))
self.assertAllClose(
-np.sum(np.log(real_y), axis=1),
softmax.inverse_log_det_jacobian(y).eval(
feed_dict={y: real_y}),
atol=0.,
rtol=1e-7)
self.assertAllClose(
-softmax.inverse_log_det_jacobian(y).eval(
feed_dict={y: real_y}),
softmax.forward_log_det_jacobian(x).eval(
feed_dict={x: real_x}),
atol=0.,
rtol=1e-7)
def testBijectorVector(self):
with self.test_session():
softmax = SoftmaxCentered(event_ndims=1)
self.assertEqual("softmax_centered", softmax.name)
x = np.log([[2., 3, 4], [4., 8, 12]])
y = [[0.2, 0.3, 0.4, 0.1], [0.16, 0.32, 0.48, 0.04]]
self.assertAllClose(y, softmax.forward(x).eval())
self.assertAllClose(x, softmax.inverse(y).eval())
self.assertAllClose(-np.sum(np.log(y), axis=1),
softmax.inverse_log_det_jacobian(y).eval(),
atol=0.,
rtol=1e-7)
self.assertAllClose(-softmax.inverse_log_det_jacobian(y).eval(),
softmax.forward_log_det_jacobian(x).eval(),
atol=0.,
rtol=1e-7)
def testBijectorScalar(self):
with self.test_session():
softmax = SoftmaxCentered() # scalar by default
self.assertEqual("softmax_centered", softmax.name)
x = np.log([[2., 3, 4], [4., 8, 12]])
y = [[[2. / 3, 1. / 3], [3. / 4, 1. / 4], [4. / 5, 1. / 5]],
[[4. / 5, 1. / 5], [8. / 9, 1. / 9], [12. / 13, 1. / 13]]]
self.assertAllClose(y, softmax.forward(x).eval())
self.assertAllClose(x, softmax.inverse(y).eval())
self.assertAllClose(-np.sum(np.log(y), axis=2),
softmax.inverse_log_det_jacobian(y).eval(),
atol=0.,
rtol=1e-7)
self.assertAllClose(-softmax.inverse_log_det_jacobian(y).eval(),
softmax.forward_log_det_jacobian(x).eval(),
atol=0.,
rtol=1e-7)
def testBijectorVector(self):
with self.test_session():
softmax = SoftmaxCentered(event_ndims=1)
self.assertEqual("softmax_centered", softmax.name)
x = np.log([[2., 3, 4], [4., 8, 12]])
y = [[0.2, 0.3, 0.4, 0.1], [0.16, 0.32, 0.48, 0.04]]
self.assertAllClose(y, softmax.forward(x).eval())
self.assertAllClose(x, softmax.inverse(y).eval())
self.assertAllClose(
-np.sum(np.log(y), axis=1),
softmax.inverse_log_det_jacobian(y).eval(),
atol=0.,
rtol=1e-7)
self.assertAllClose(
-softmax.inverse_log_det_jacobian(y).eval(),
softmax.forward_log_det_jacobian(x).eval(),
atol=0.,
rtol=1e-7)
def testBijectorScalar(self):
with self.test_session():
softmax = SoftmaxCentered() # scalar by default
self.assertEqual("softmax_centered", softmax.name)
x = np.log([[2., 3, 4],
[4., 8, 12]])
y = [[[2. / 3, 1. / 3],
[3. / 4, 1. / 4],
[4. / 5, 1. / 5]],
[[4. / 5, 1. / 5],
[8. / 9, 1. / 9],
[12. / 13, 1. / 13]]]
self.assertAllClose(y, softmax.forward(x).eval())
self.assertAllClose(x, softmax.inverse(y).eval())
self.assertAllClose(
-np.sum(np.log(y), axis=2),
softmax.inverse_log_det_jacobian(y).eval(),
atol=0.,
rtol=1e-7)
self.assertAllClose(
-softmax.inverse_log_det_jacobian(y).eval(),
softmax.forward_log_det_jacobian(x).eval(),
atol=0.,
rtol=1e-7)