https://github.com/GPflow/GPflow
Tip revision: b819db324fb3c64cab4db52c8f618ab8ff0f5778 authored by st-- on 14 September 2020, 17:03:08 UTC
Merge pull request #1565 from GPflow/develop
Merge pull request #1565 from GPflow/develop
Tip revision: b819db3
scipy.py
# Copyright 2017-2020 The GPflow Contributors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Callable, Iterable, List, Optional, Sequence, Tuple, TypeVar, Union
import numpy as np
import scipy.optimize
import tensorflow as tf
from scipy.optimize import OptimizeResult
__all__ = ["Scipy"]
Variables = Iterable[tf.Variable] # deprecated
StepCallback = Callable[[int, Sequence[tf.Variable], Sequence[tf.Tensor]], None]
LossClosure = Callable[[], tf.Tensor]
class Scipy:
def minimize(
self,
closure: LossClosure,
variables: Sequence[tf.Variable],
method: Optional[str] = "L-BFGS-B",
step_callback: Optional[StepCallback] = None,
compile: 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 callable 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.
compile: 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`
Note that Scipy's minimize() takes a `callback` argument, but
you probably want to use our wrapper and pass in `step_callback`.
Returns:
The optimization result represented as a Scipy ``OptimizeResult``
object. See the Scipy documentation for description of attributes.
"""
if not callable(closure):
raise TypeError(
"The 'closure' argument is expected to be a callable object."
) # pragma: no cover
variables = tuple(variables)
if not all(isinstance(v, tf.Variable) for v in variables):
raise TypeError(
"The 'variables' argument is expected to only contain tf.Variable instances (use model.trainable_variables, not model.trainable_parameters)"
) # pragma: no cover
initial_params = self.initial_parameters(variables)
func = self.eval_func(closure, variables, compile=compile)
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: Sequence[tf.Variable]) -> tf.Tensor:
return cls.pack_tensors(variables)
@classmethod
def eval_func(
cls, closure: LossClosure, variables: Sequence[tf.Variable], compile: bool = True
) -> Callable[[np.ndarray], Tuple[np.ndarray, np.ndarray]]:
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 compile:
_tf_eval = tf.function(_tf_eval)
def _eval(x: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
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: Sequence[tf.Variable], step_callback: StepCallback
) -> Callable[[np.ndarray], None]:
step = 0 # type: int
def _callback(x: np.ndarray) -> None:
nonlocal step
values = cls.unpack_tensors(variables, x)
step_callback(step, variables, values)
step += 1
return _callback
@staticmethod
def pack_tensors(tensors: Sequence[Union[tf.Tensor, tf.Variable]]) -> 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: Sequence[Union[tf.Tensor, tf.Variable]], from_vector: tf.Tensor
) -> List[tf.Tensor]:
s = 0
values = []
for target_tensor in to_tensors:
shape = tf.shape(target_tensor)
dtype = target_tensor.dtype
tensor_size = tf.reduce_prod(shape)
tensor_vector = from_vector[s : s + tensor_size]
tensor = tf.reshape(tf.cast(tensor_vector, dtype), shape)
values.append(tensor)
s += tensor_size
return values
@staticmethod
def assign_tensors(to_tensors: Sequence[tf.Variable], values: Sequence[tf.Tensor]) -> None:
if len(to_tensors) != len(values):
raise ValueError("to_tensors and values should have same length")
for target, value in zip(to_tensors, values):
target.assign(value)
def _compute_loss_and_gradients(
loss_closure: LossClosure, variables: Sequence[tf.Variable]
) -> Tuple[tf.Tensor, Sequence[tf.Tensor]]:
with tf.GradientTape(watch_accessed_variables=False) as tape:
tape.watch(variables)
loss = loss_closure()
grads = tape.gradient(loss, variables)
return loss, grads