tfp.math.ode.Solver

Base class for an ODE solver.

tfp.math.ode.Solver(
    make_adjoint_solver_fn, validate_args, name
)

Attributes
`name`

Attributes

name

Methods

`solve`

View source

solve(
    ode_fn,
    initial_time,
    initial_state,
    solution_times,
    jacobian_fn=None,
    jacobian_sparsity=None,
    batch_ndims=None,
    previous_solver_internal_state=None,
    constants=None
)

Solves an initial value problem.

An initial value problem consists of a system of ODEs and an initial condition:

dy/dt(t) = ode_fn(t, y(t), **constants)
y(initial_time) = initial_state

Here, t (also called time) is a scalar float Tensor and y(t) (also called the state at time t) is an N-D float or complex Tensor. constants is are values that are constant with respect to time. Passing the constants here rather than just closing over them in ode_fn is only necessary if you want gradients with respect to these values.

Example

The ODE dy/dt(t) = dot(A, y(t)) is solved below.

t_init, t0, t1 = 0., 0.5, 1.
y_init = tf.constant([1., 1.], dtype=tf.float64)
A = tf.constant([[-1., -2.], [-3., -4.]], dtype=tf.float64)

def ode_fn(t, y):
  return tf.linalg.matvec(A, y)

results = tfp.math.ode.BDF().solve(ode_fn, t_init, y_init,
                                   solution_times=[t0, t1])
y0 = results.states[0]  # == dot(matrix_exp(A * t0), y_init)
y1 = results.states[1]  # == dot(matrix_exp(A * t1), y_init)

If the exact solution times are not important, it can be much more efficient to let the solver choose them using solution_times=tfp.math.ode.ChosenBySolver(final_time=1.). This yields the state at various times between t_init and final_time, in which case results.states[i] is the state at time results.times[i].

Gradients

The gradients are computed using the adjoint sensitivity method described in [Chen et al. (2018)][1].

grad = tf.gradients(y1, y0) # == dot(e, J)
# J is the Jacobian of y1 with respect to y0. In this case, J = exp(A * t1).
# e = [1, ..., 1] is the row vector of ones.

This is not capable of computing gradients with respect to values closed over by ode_fn, e.g., in the example above:

def ode_fn(t, y):
  return tf.linalg.matvec(A, y)

with tf.GradientTape() as tape:
  tape.watch(A)
  results = tfp.math.ode.BDF().solve(ode_fn, t_init, y_init,
                                     solution_times=[t0, t1])
tape.gradient(results.states, A)  # Undefined!

There are two options to get the gradients flowing through these values:

Use tf.Variable for these values.
Pass the values in explicitly using the constants argument:

def ode_fn(t, y, A):
  return tf.linalg.matvec(A, y)

with tf.GradientTape() as tape:
  tape.watch(A)
  results = tfp.math.ode.BDF().solve(ode_fn, t_init, y_init,
                                     solution_times=[t0, t1],
                                     constants={'A': A})
tape.gradient(results.states, A)  # Fine.

By default, this uses the same solver for the augmented ODE. This can be controlled via make_adjoint_solver_fn.

References

[1]: Chen, Tian Qi, et al. "Neural ordinary differential equations." Advances in Neural Information Processing Systems. 2018.

Args
`ode_fn`	Function of the form `ode_fn(t, y, **constants)`. The input `t` is a scalar float `Tensor`. The input `y` and output are both `Tensor`s with the same shape and `dtype` as `initial_state`. `constants` is are values that are constant with respect to time. Passing the constants here rather than just closing over them in `ode_fn` is only necessary if you want gradients with respect to these values.
`initial_time`	Scalar float `Tensor` specifying the initial time.
`initial_state`	N-D float or complex `Tensor` specifying the initial state. The `dtype` of `initial_state` must be complex for problems with complex-valued states (even if the initial state is real).
`solution_times`	1-D float `Tensor` specifying a list of times. The solver stores the computed state at each of these times in the returned `Results` object. Must satisfy `initial_time <= solution_times[0]` and `solution_times[i] < solution_times[i+1]`. Alternatively, the user can pass `tfp.math.ode.ChosenBySolver(final_time)` where `final_time` is a scalar float `Tensor` satisfying `initial_time < final_time`. Doing so requests that the solver automatically choose suitable times up to and including `final_time` at which to store the computed state.
`jacobian_fn`	Optional function of the form `jacobian_fn(t, y)`. The input `t` is a scalar float `Tensor`. The input `y` has the same shape and `dtype` as `initial_state`. The output is a 2N-D `Tensor` whose shape is `initial_state.shape + initial_state.shape` and whose `dtype` is the same as `initial_state`. In particular, the `(i1, ..., iN, j1, ..., jN)`-th entry of `jacobian_fn(t, y)` is the derivative of the `(i1, ..., iN)`-th entry of `ode_fn(t, y)` with respect to the `(j1, ..., jN)`-th entry of `y`. If this argument is left unspecified, the solver automatically computes the Jacobian if and when it is needed. Default value: `None`.
`jacobian_sparsity`	Optional 2N-D boolean `Tensor` whose shape is `initial_state.shape + initial_state.shape` specifying the sparsity pattern of the Jacobian. This argument is ignored if `jacobian_fn` is specified. Default value: `None`.
`batch_ndims`	Optional nonnegative integer. When specified, the first `batch_ndims` dimensions of `initial_state` are batch dimensions. Default value: `None`.
`previous_solver_internal_state`	Optional solver-specific argument used to warm-start this invocation of `solve`. Default value: `None`.
`constants`	Optional dictionary with string keys and values being (possibly nested) float `Tensor`s. These represent values that are constant with respect to time. Specifying these here allows the adjoint sentitivity method to compute gradients of the results with respect to these values.

Returns
Object of type `Results`.