def test_grad_no_grad(self):
class CosGradModel(tf.keras.Model):
def call(self, inputs, **kwargs):
t, x = inputs
with tf.GradientTape() as g:
g.watch(x)
y = tf.sin(t * x)
dy = g.gradient(y, x)
return dy
class CosModel(tf.keras.Model):
def call(self, inputs, **kwargs):
t, x = inputs
y = t * tf.cos(t * x)
return y
t_vec = np.linspace(0, 1.0, 40)
ode = NeuralODE(CosModel(), t=t_vec, solver=neural_ode.rk4_step)
x0 = tf.to_float([1.0])
cos_xN = ode.forward(x0)
x0_rec, dLdx0, _ = ode.backward(cos_xN, cos_xN)
ode = NeuralODE(CosGradModel(), t=t_vec, solver=neural_ode.rk4_step)
cos_grad_xN = ode.forward(x0)
x0_grad_rec, dLdx0_grad, _ = ode.backward(cos_grad_xN, cos_grad_xN)
self.assertAllClose(cos_grad_xN, cos_xN)
self.assertAllClose(x0_grad_rec, x0_rec)
self.assertAllClose(x0, x0_rec)
self.assertAllClose(dLdx0, dLdx0_grad)
ode = NeuralODE(CosModel(), t=t_vec, solver=neural_ode.rk4_step)
with tf.GradientTape() as g:
g.watch(x0)
cos_xN = ode.forward(x0)
loss = 0.5 * cos_xN ** 2
dLdx0_exact = g.gradient(loss, [x0])
self.assertAllClose(dLdx0_exact, dLdx0_grad)
# build static graph
ode = NeuralODE(CosGradModel(), t=t_vec, solver=neural_ode.rk4_step)
ode = neural_ode.defun_neural_ode(ode)
cos_grad_xN = ode.forward(x0)
x0_grad_rec, dLdx0_grad, _ = ode.backward(cos_grad_xN, cos_grad_xN)
self.assertAllClose(dLdx0_grad, dLdx0_exact)
self.assertAllClose(x0_grad_rec, x0)
def test_net_with_inner_gradient(self):
tf.set_random_seed(1234)
t_vec = np.linspace(0, 1.0, 20)
model = NNGradientModule()
ode = NeuralODE(model, t=t_vec, solver=neural_ode.rk4_step)
xy0 = tf.random_normal(shape=[12, 2 * 3])
xyN = ode.forward(xy0)
with tf.GradientTape() as g:
g.watch(xyN)
loss = xyN ** 2
dLoss = g.gradient(loss, xyN)
xy0_rec, dLdxy0, dLdW = ode.backward(xyN, dLoss)
self.assertAllClose(xy0_rec, xy0)
with tf.GradientTape() as g:
g.watch(xy0)
xyN = ode.forward(xy0)
loss = xyN ** 2
dLdxy0_exact, *dLdW_exact = g.gradient(loss, [xy0, *model.weights])
self.assertAllClose(dLdxy0_exact, dLdxy0)
self.assertAllClose(dLdW_exact, dLdW)
def test_backward_none(self):
tf.set_random_seed(1234)
t_grid = np.linspace(0, 1.0, 15)
x0 = tf.random_normal(shape=[7, 3])
ode = NeuralODE(NNModuleTimeDependent(), t=t_grid)
x0_rec, *_ = ode.backward(ode.forward(x0))
self.assertAllClose(x0_rec, x0)
def test_nn_forward_backward(self):
tf.set_random_seed(1234)
t_vec = np.linspace(0, 1.0, 20)
model = NNModule()
ode = NeuralODE(model, t=t_vec, solver=neural_ode.rk4_step)
x0 = tf.random_normal(shape=[12, 3])
xN = ode.forward(x0)
dLoss = 2 * xN # explicit gradient od x**2
x0_rec, dLdx0, dLdW = ode.backward(xN, dLoss)
self.assertAllClose(x0_rec.numpy(), x0.numpy())
with tf.GradientTape() as g:
g.watch(x0)
xN = ode.forward(x0)
loss = xN ** 2
dLdx0_exact, *dLdW_exact = g.gradient(loss, [x0, *model.weights])
self.assertAllClose(dLdx0_exact, dLdx0)
self.assertAllClose(dLdW_exact, dLdW)
def test_backprop(self):
t_max = 1
t_grid = np.linspace(0, t_max, 40)
ode = NeuralODE(SineDumpingModel(), t=t_grid,
solver=neural_ode.rk4_step)
x0 = tf.to_float([0])
xN = ode.forward(x0)
with tf.GradientTape() as g:
g.watch(xN)
loss = xN ** 2
dLoss = g.gradient(loss, xN)
x0_rec, dLdx0, dLdW = ode.backward(xN, dLoss)
self.assertAllClose(x0_rec.numpy(), x0)
self.assertEqual(dLdW, [])
with tf.GradientTape() as g:
g.watch(x0)
xN = ode.forward(x0)
loss = xN ** 2
dLdx0_exact = g.gradient(loss, x0)
self.assertAllClose(dLdx0_exact, dLdx0)
