https://github.com/GPflow/GPflow
Tip revision: 964cfeeb98d02f9a6356e00beb59819aa7414158 authored by Nicolas Durrande on 11 March 2020, 13:24:49 UTC
Update gpflow/kernels/stationaries.py
Update gpflow/kernels/stationaries.py
Tip revision: 964cfee
scipy.py
from typing import Callable, Iterator, List, Tuple, Union, Optional
import numpy as np
import scipy.optimize
import tensorflow as tf
from scipy.optimize import OptimizeResult
__all__ = ["Scipy"]
Loss = tf.Tensor
Variables = Tuple[tf.Variable]
StepCallback = Callable[[int, Variables, List[tf.Tensor]], None]
LossClosure = Callable[..., tf.Tensor]
class Scipy:
def minimize(
self,
closure: LossClosure,
variables: Variables,
method: Optional[str] = "L-BFGS-B",
step_callback: Optional[StepCallback] = None,
jit: bool = True,
**scipy_kwargs,
) -> OptimizeResult:
"""
Minimize is a wrapper around the `scipy.optimize.minimize` function
handling the packing and unpacking of a list of shaped variables on the
TensorFlow side vs. the flat numpy array required on the Scipy side.
Args:
closure: A closure that re-evaluates the model, returning the loss
to be minimized.
variables: The list (tuple) of variables to be optimized
(typically `model.trainable_variables`)
method: The type of solver to use in SciPy. Defaults to "L-BFGS-B".
step_callback: If not None, a callable that gets called once after
each optimisation step. The callabe is passed the arguments
`step`, `variables`, and `values`. `step` is the optimisation
step counter. `variables` is the list of trainable variables as
above, and `values` is the corresponding list of tensors of
matching shape that contains their value at this optimisation
step.
jit: If True, wraps the evaluation function (the passed `closure` as
well as its gradient computation) inside a `tf.function()`,
which will improve optimization speed in most cases.
scipy_kwargs: Arguments passed through to `scipy.optimize.minimize`
Returns:
The optimization result represented as a scipy ``OptimizeResult``
object. See the Scipy documentation for description of attributes.
"""
if not callable(closure):
raise TypeError("Callable object expected.") # pragma: no cover
initial_params = self.initial_parameters(variables)
func = self.eval_func(closure, variables, jit=jit)
if step_callback is not None:
if "callback" in scipy_kwargs:
raise ValueError("Callback passed both via `step_callback` and `callback`")
callback = self.callback_func(variables, step_callback)
scipy_kwargs.update(dict(callback=callback))
return scipy.optimize.minimize(
func, initial_params, jac=True, method=method, **scipy_kwargs
)
@classmethod
def initial_parameters(cls, variables):
return cls.pack_tensors(variables)
@classmethod
def eval_func(cls, closure: LossClosure, variables: Variables, jit: bool = True):
def _tf_eval(x: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:
values = cls.unpack_tensors(variables, x)
cls.assign_tensors(variables, values)
loss, grads = _compute_loss_and_gradients(closure, variables)
return loss, cls.pack_tensors(grads)
if jit:
_tf_eval = tf.function(_tf_eval)
def _eval(x):
loss, grad = _tf_eval(tf.convert_to_tensor(x))
return loss.numpy().astype(np.float64), grad.numpy().astype(np.float64)
return _eval
@classmethod
def callback_func(cls, variables: Variables, step_callback: StepCallback):
step = 0 # type: int
def _callback(x):
nonlocal step
values = cls.unpack_tensors(variables, x)
step_callback(step=step, variables=variables, values=values)
step += 1
return _callback
@staticmethod
def pack_tensors(tensors: Iterator[tf.Tensor]) -> tf.Tensor:
flats = [tf.reshape(tensor, (-1,)) for tensor in tensors]
tensors_vector = tf.concat(flats, axis=0)
return tensors_vector
@staticmethod
def unpack_tensors(to_tensors: Iterator[tf.Tensor], from_vector: tf.Tensor) -> List[tf.Tensor]:
s = 0
values = []
for tensor in to_tensors:
shape = tf.shape(tensor)
tensor_size = tf.reduce_prod(shape)
tensor_vector = tf.cast(from_vector[s : s + tensor_size], tensor.dtype)
tensor_vector = tf.reshape(tensor_vector, shape)
values.append(tensor_vector)
s += tensor_size
return values
@staticmethod
def assign_tensors(to_tensors: Iterator[tf.Variable], values: Iterator[tf.Tensor]):
for tensor, tensor_vector in zip(to_tensors, values):
tensor.assign(tensor_vector)
def _compute_loss_and_gradients(loss_cb: LossClosure, variables: Variables):
with tf.GradientTape() as tape:
loss = loss_cb()
grads = tape.gradient(loss, variables)
return loss, grads