neproblem
This namespace contains the NeuroevolutionProblem class.
NEProblem (Problem)
¶
Base class for neuro-evolution problems where the goal is to optimize the parameters of a neural network represented as a PyTorch module.
Any problem inheriting from this class is expected to override the method
_evaluate_network(self, net: torch.nn.Module) -> Union[torch.Tensor, float]
where net is the neural network to be evaluated, and the return value
is a scalar or a vector (for multi-objective cases) expressing the
fitness value(s).
Alternatively, this class can be directly instantiated in the following way:
def f(module: MyTorchModuleClass) -> Union[float, torch.Tensor, tuple]:
# Evaluate the given PyTorch module here
fitness = ...
return fitness
problem = NEProblem(
"min", MyTorchModuleClass, f,
...
)
which specifies that the problem's goal is to minimize the return of the
function f.
For multi-objective cases, the fitness returned by f is expected as a
1-dimensional tensor. For when the problem has additional evaluation data,
a two-element tuple can be returned by f instead, where the first
element is the fitness value(s) and the second element is a 1-dimensional
tensor storing the additional data.
Source code in evotorch/neuroevolution/neproblem.py
class NEProblem(Problem):
"""
Base class for neuro-evolution problems where the goal is to optimize the
parameters of a neural network represented as a PyTorch module.
Any problem inheriting from this class is expected to override the method
`_evaluate_network(self, net: torch.nn.Module) -> Union[torch.Tensor, float]`
where `net` is the neural network to be evaluated, and the return value
is a scalar or a vector (for multi-objective cases) expressing the
fitness value(s).
Alternatively, this class can be directly instantiated in the following
way:
```python
def f(module: MyTorchModuleClass) -> Union[float, torch.Tensor, tuple]:
# Evaluate the given PyTorch module here
fitness = ...
return fitness
problem = NEProblem(
"min", MyTorchModuleClass, f,
...
)
```
which specifies that the problem's goal is to minimize the return of the
function `f`.
For multi-objective cases, the fitness returned by `f` is expected as a
1-dimensional tensor. For when the problem has additional evaluation data,
a two-element tuple can be returned by `f` instead, where the first
element is the fitness value(s) and the second element is a 1-dimensional
tensor storing the additional data.
"""
def __init__(
self,
objective_sense: ObjectiveSense,
network: Union[str, nn.Module, Callable[[], nn.Module]],
network_eval_func: Optional[Callable] = None,
*,
network_args: Optional[dict] = None,
initial_bounds: Optional[BoundsPairLike] = (-0.00001, 0.00001),
eval_dtype: Optional[DType] = None,
eval_data_length: int = 0,
seed: Optional[int] = None,
num_actors: Optional[Union[int, str]] = "num_devices",
actor_config: Optional[dict] = None,
num_gpus_per_actor: Optional[Union[int, float, str]] = None,
num_subbatches: Optional[int] = None,
subbatch_size: Optional[int] = None,
device: Optional[Device] = None,
):
"""
`__init__(...)`: Initialize the NEProblem.
Args:
objective_sense: The objective sense, expected as "min" or "max"
for single-objective cases, or as a sequence of strings
(each string being "min" or "max") for multi-objective cases.
network: A network structure string, or a Callable (which can be
a class inheriting from `torch.nn.Module`, or a function
which returns a `torch.nn.Module` instance), or an instance
of `torch.nn.Module`.
The object provided here determines the structure of the
neural network whose parameters will be evolved.
A network structure string is a string which can be processed
by `evotorch.neuroevolution.net.str_to_net(...)`.
Please see the documentation of the function
`evotorch.neuroevolution.net.str_to_net(...)` to see how such
a neural network structure string looks like.
network_eval_func: Optionally a function (or any Callable object)
which receives a PyTorch module as its argument, and returns
either a fitness, or a two-element tuple containing the fitness
and the additional evaluation data. The fitness can be a scalar
(for single-objective cases) or a 1-dimensional tensor (for
multi-objective cases). The additional evaluation data is
expected as a 1-dimensional tensor.
If this argument is left as None, it will be expected that
the method `_evaluate_network(...)` is overriden by the
inheriting class.
network_args: Optionally a dict-like object, storing keyword
arguments to be passed to the network while instantiating it.
initial_bounds: Specifies an interval from which the values of the
initial neural network parameters will be drawn.
eval_dtype: dtype to be used for fitnesses. If not specified, then
`eval_dtype` will be inferred from the dtype of the parameters
of the neural network.
In more details, if the neural network's parameters have a
float dtype, `eval_dtype` will be a compatible float.
Otherwise, it will be "float32".
eval_data_length: Length of the extra evaluation data.
seed: Random number seed. If left as None, this NEProblem instance
will not have its own random generator, and the global random
generator of PyTorch will be used instead.
num_actors: Number of actors to create for parallelized
evaluation of the solutions.
Certain string values are also accepted.
When given as "max" or as "num_cpus", the number of actors
will be equal to the number of all available CPUs in the ray
cluster.
When given as "num_gpus", the number of actors will be
equal to the number of all available GPUs in the ray
cluster, and each actor will be assigned a GPU.
When given as "num_devices", the number of actors will be
equal to the minimum among the number of CPUs and the number
of GPUs available in the cluster (or will be equal to the
number of CPUs if there is no GPU), and each actor will be
assigned a GPU (if available).
If `num_actors` is given as "num_gpus" or "num_devices",
the argument `num_gpus_per_actor` must not be used,
and the `actor_config` dictionary must not contain the
key "num_gpus".
If `num_actors` is given as something other than "num_gpus"
or "num_devices", and if you wish to assign GPUs to each
actor, then please see the argument `num_gpus_per_actor`.
actor_config: A dictionary, representing the keyword arguments
to be passed to the options(...) used when creating the
ray actor objects. To be used for explicitly allocating
resources per each actor.
For example, for declaring that each actor is to use a GPU,
one can pass `actor_config=dict(num_gpus=1)`.
Can also be given as None (which is the default),
if no such options are to be passed.
num_gpus_per_actor: Number of GPUs to be allocated by each
remote actor.
The default behavior is to NOT allocate any GPU at all
(which is the default behavior of the ray library as well).
When given as a number `n`, each actor will be given
`n` GPUs (where `n` can be an integer, or can be a `float`
for fractional allocation).
When given as a string "max", then the available GPUs
across the entire ray cluster (or within the local computer
in the simplest cases) will be equally distributed among
the actors.
When given as a string "all", then each actor will have
access to all the GPUs (this will be achieved by suppressing
the environment variable `CUDA_VISIBLE_DEVICES` for each
actor).
When the problem is not distributed (i.e. when there are
no actors), this argument is expected to be left as None.
num_subbatches: If `num_subbatches` is None (assuming that
`subbatch_size` is also None), then, when evaluating a
population, the population will be split into n pieces, `n`
being the number of actors, and each actor will evaluate
its assigned piece. If `num_subbatches` is an integer `m`,
then the population will be split into `m` pieces,
and actors will continually accept the next unevaluated
piece as they finish their current tasks.
The arguments `num_subbatches` and `subbatch_size` cannot
be given values other than None at the same time.
While using a distributed algorithm, this argument determines
how many sub-batches will be generated, and therefore,
how many gradients will be computed by the remote actors.
subbatch_size: If `subbatch_size` is None (assuming that
`num_subbatches` is also None), then, when evaluating a
population, the population will be split into `n` pieces, `n`
being the number of actors, and each actor will evaluate its
assigned piece. If `subbatch_size` is an integer `m`,
then the population will be split into pieces of size `m`,
and actors will continually accept the next unevaluated
piece as they finish their current tasks.
When there can be significant difference across the solutions
in terms of computational requirements, specifying a
`subbatch_size` can be beneficial, because, while one
actor is busy with a subbatch containing computationally
challenging solutions, other actors can accept more
tasks and save time.
The arguments `num_subbatches` and `subbatch_size` cannot
be given values other than None at the same time.
While using a distributed algorithm, this argument determines
the size of a sub-batch (or sub-population) sampled by a
remote actor for computing a gradient.
In distributed mode, it is expected that the population size
is divisible by `subbatch_size`.
device: Default device in which a new population will be generated
and the neural networks will operate.
If not specified, "cpu" will be used.
"""
# Set the main device of the problem
# Although the operation of setting the main device is done by the main Problem class,
# here we need this at an earlier stage.
if device is None:
device = "cpu"
self._device = torch.device(device)
# Set the network
self._original_network = network
self._network_args = {} if network_args is None else deepcopy(network_args)
if isinstance(self._original_network, nn.Module):
self._original_network = self._original_network.cpu()
# Store the function that will evaluate the network, if available
self._network_eval_func: Optional[Callable] = network_eval_func
self.instantiated_network: nn.Module = None
# Create temporary network
temp_network = self._instantiate_net(self._original_network, device="cpu")
super().__init__(
objective_sense=objective_sense,
initial_bounds=initial_bounds,
bounds=None, # Neuroevolution is an unbounded problem
solution_length=count_parameters(temp_network), # The solution length is inherited from the network passed
dtype=next(temp_network.parameters()).dtype, # The datatype is inherited from the network passed
eval_dtype=eval_dtype,
device=device,
eval_data_length=eval_data_length,
seed=seed,
num_actors=num_actors,
num_gpus_per_actor=num_gpus_per_actor,
actor_config=actor_config,
num_subbatches=num_subbatches,
subbatch_size=subbatch_size,
store_solution_stats=None,
)
@property
def network_device(self) -> Device:
"""The device on which the problem should place data e.g. the network"""
cpu_device = torch.device("cpu")
if self.is_main:
# This is the case where this is the main process (not a remote actor)
if self.device == cpu_device:
# If the main device of the problem is "cpu", then we assume that the network is going to be on the cpu as well
return cpu_device
else:
# If the main device of the problem is some other device, then it is that device into which the network will be put
return self.device
else:
# If this is a remote actor, then the network will be put into the auxiliary device allocated for that actor
return self.aux_device
@property
def _network_constants(self) -> dict:
"""Named constants which can be passed to the network instantiation e.g. input/output dimension. To be overridden by the user for custom fixed constants for a problem"""
return {}
def network_constants(self) -> dict:
"""Named constants which can be passed to the network instantiation e.g. input/output dimension"""
constants = {}
constants.update(self._network_constants)
constants.update(self._network_args)
return constants
@property
def _nonserialized_attribs(self) -> List[str]:
return ["instantiated_network"]
def _instantiate_net(self, network: Union[str, nn.Module, dict], device: Optional[Device] = None) -> nn.Module:
"""Instantiate the network on the target device, to be overridden by the user for custom behaviour
Returns:
instantiated_network (nn.Module): The network instantiated on the target device
"""
# Branching point determines instantiation of network
if isinstance(network, str):
# Passed argument was a string representation of a torch module
instantiated_network = str_to_net(network, **self.network_constants())
elif isinstance(network, nn.Module):
# Passed argument was directly a torch module
instantiated_network = network
else:
# Passed argument was callable yielding network
instantiated_network = network(**self.network_constants())
# Map to device
device = self.network_device if device is None else device
instantiated_network = instantiated_network.to(device)
return instantiated_network
def _prepare(self) -> None:
"""Instantiate the network on the target device, if not already done"""
self.instantiated_network = self._instantiate_net(self._original_network)
# Clear reference to original network
self._original_network = None
def make_net(self, parameters: Iterable) -> nn.Module:
"""
Make a new network filled with the provided parameters.
Args:
parameters: Parameters to be used as weights within the network.
Can be a Solution, or any 1-dimensional Iterable that can be
converted to a PyTorch tensor.
Returns:
A new network, as a `torch.Module` instance.
"""
if isinstance(parameters, Solution):
parameters = parameters.access_values(keep_evals=True)
else:
parameters = self.as_tensor(parameters)
with torch.no_grad():
net = deepcopy(self.parameterize_net(parameters))
return net
def parameterize_net(self, parameters: torch.Tensor) -> nn.Module:
"""Parameterize the network with a given set of parameters.
Args:
parameters (torch.Tensor): The parameters with which to instantiate the network
Returns:
instantiated_network (nn.Module): The network instantiated with the parameters
"""
# Check if network exists
if self.instantiated_network is None:
self.instantiated_network = self._instantiate_net(self._original_network)
network = self.instantiated_network
# Move the parameters if needed
if parameters.device != self.network_device:
parameters = parameters.to(self.network_device)
# Fill the network with the parameters
fill_parameters(network, parameters)
# Return the network
return network
@property
def _grad_device(self) -> Device:
"""
Get the device in which new solutions will be made in distributed mode.
In more details, in distributed mode, each actor creates its own
sub-populations, evaluates them, and computes its own gradient
(all such actor gradients eventually being collected by the
distribution-based search algorithm in the main process).
For some problem types, it can make sense for the remote actors to
create their temporary sub-populations on another device
(e.g. on the GPU that is allocated specifically for them).
For such situations, one is encouraged to override this property
and make it return whatever device is to be used.
In the case of NEProblem, this property returns whatever device
is specified by the property `network_device`.
"""
return self.network_device
def _evaluate_network(self, network: nn.Module) -> Union[float, torch.Tensor, tuple]:
"""
Evaluate a network and return the evaluation result(s).
In the case where the `__init__` of `NEProblem` was not given
a network evaluator function (via the argument `network_eval_func`),
it will be expected that the inheriting class overrides this
method and defines how a network should be evaluated.
Args:
network (nn.Module): The network to evaluate
Returns:
fitness: The networks' fitness value(s), as a scalar for
single-objective cases, or as a 1-dimensional tensor
for multi-objective cases. The returned value can also
be a two-element tuple where the first element is the
fitness (as a scalar or as a vector) and the second
element is a 1-dimensional vector storing the extra
evaluation data.
"""
raise NotImplementedError
def _evaluate(self, solution: Solution):
"""
Evaluate a single solution.
This is achieved by parameterising the problem's attribute
named `instantiated_network`, and then evaluating the network
with the method `_evaluate_network(...)`.
Args:
solution (Solution): The solution to evaluate.
"""
parameters = solution.values
if self._network_eval_func is None:
evaluator = self._evaluate_network
else:
evaluator = self._network_eval_func
fitnesses = evaluator(self.parameterize_net(parameters))
if isinstance(fitnesses, tuple):
solution.set_evals(*fitnesses)
else:
solution.set_evals(fitnesses)
network_device: Union[str, torch.device]
property
readonly
¶
The device on which the problem should place data e.g. the network
__init__(self, objective_sense, network, network_eval_func=None, *, network_args=None, initial_bounds=(-1e-05, 1e-05), eval_dtype=None, eval_data_length=0, seed=None, num_actors='num_devices', actor_config=None, num_gpus_per_actor=None, num_subbatches=None, subbatch_size=None, device=None)
special
¶
__init__(...): Initialize the NEProblem.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
objective_sense |
Union[str, Iterable[str]] |
The objective sense, expected as "min" or "max" for single-objective cases, or as a sequence of strings (each string being "min" or "max") for multi-objective cases. |
required |
network |
Union[str, torch.nn.modules.module.Module, Callable[[], torch.nn.modules.module.Module]] |
A network structure string, or a Callable (which can be
a class inheriting from |
required |
network_eval_func |
Optional[Callable] |
Optionally a function (or any Callable object)
which receives a PyTorch module as its argument, and returns
either a fitness, or a two-element tuple containing the fitness
and the additional evaluation data. The fitness can be a scalar
(for single-objective cases) or a 1-dimensional tensor (for
multi-objective cases). The additional evaluation data is
expected as a 1-dimensional tensor.
If this argument is left as None, it will be expected that
the method |
None |
network_args |
Optional[dict] |
Optionally a dict-like object, storing keyword arguments to be passed to the network while instantiating it. |
None |
initial_bounds |
Union[Iterable[Union[float, Iterable[float], torch.Tensor]], evotorch.core.BoundsPair] |
Specifies an interval from which the values of the initial neural network parameters will be drawn. |
(-1e-05, 1e-05) |
eval_dtype |
Union[str, torch.dtype, numpy.dtype, Type] |
dtype to be used for fitnesses. If not specified, then
|
None |
eval_data_length |
int |
Length of the extra evaluation data. |
0 |
seed |
Optional[int] |
Random number seed. If left as None, this NEProblem instance will not have its own random generator, and the global random generator of PyTorch will be used instead. |
None |
num_actors |
Union[int, str] |
Number of actors to create for parallelized
evaluation of the solutions.
Certain string values are also accepted.
When given as "max" or as "num_cpus", the number of actors
will be equal to the number of all available CPUs in the ray
cluster.
When given as "num_gpus", the number of actors will be
equal to the number of all available GPUs in the ray
cluster, and each actor will be assigned a GPU.
When given as "num_devices", the number of actors will be
equal to the minimum among the number of CPUs and the number
of GPUs available in the cluster (or will be equal to the
number of CPUs if there is no GPU), and each actor will be
assigned a GPU (if available).
If |
'num_devices' |
actor_config |
Optional[dict] |
A dictionary, representing the keyword arguments
to be passed to the options(...) used when creating the
ray actor objects. To be used for explicitly allocating
resources per each actor.
For example, for declaring that each actor is to use a GPU,
one can pass |
None |
num_gpus_per_actor |
Union[int, float, str] |
Number of GPUs to be allocated by each
remote actor.
The default behavior is to NOT allocate any GPU at all
(which is the default behavior of the ray library as well).
When given as a number |
None |
num_subbatches |
Optional[int] |
If |
None |
subbatch_size |
Optional[int] |
If |
None |
device |
Union[str, torch.device] |
Default device in which a new population will be generated and the neural networks will operate. If not specified, "cpu" will be used. |
None |
Source code in evotorch/neuroevolution/neproblem.py
def __init__(
self,
objective_sense: ObjectiveSense,
network: Union[str, nn.Module, Callable[[], nn.Module]],
network_eval_func: Optional[Callable] = None,
*,
network_args: Optional[dict] = None,
initial_bounds: Optional[BoundsPairLike] = (-0.00001, 0.00001),
eval_dtype: Optional[DType] = None,
eval_data_length: int = 0,
seed: Optional[int] = None,
num_actors: Optional[Union[int, str]] = "num_devices",
actor_config: Optional[dict] = None,
num_gpus_per_actor: Optional[Union[int, float, str]] = None,
num_subbatches: Optional[int] = None,
subbatch_size: Optional[int] = None,
device: Optional[Device] = None,
):
"""
`__init__(...)`: Initialize the NEProblem.
Args:
objective_sense: The objective sense, expected as "min" or "max"
for single-objective cases, or as a sequence of strings
(each string being "min" or "max") for multi-objective cases.
network: A network structure string, or a Callable (which can be
a class inheriting from `torch.nn.Module`, or a function
which returns a `torch.nn.Module` instance), or an instance
of `torch.nn.Module`.
The object provided here determines the structure of the
neural network whose parameters will be evolved.
A network structure string is a string which can be processed
by `evotorch.neuroevolution.net.str_to_net(...)`.
Please see the documentation of the function
`evotorch.neuroevolution.net.str_to_net(...)` to see how such
a neural network structure string looks like.
network_eval_func: Optionally a function (or any Callable object)
which receives a PyTorch module as its argument, and returns
either a fitness, or a two-element tuple containing the fitness
and the additional evaluation data. The fitness can be a scalar
(for single-objective cases) or a 1-dimensional tensor (for
multi-objective cases). The additional evaluation data is
expected as a 1-dimensional tensor.
If this argument is left as None, it will be expected that
the method `_evaluate_network(...)` is overriden by the
inheriting class.
network_args: Optionally a dict-like object, storing keyword
arguments to be passed to the network while instantiating it.
initial_bounds: Specifies an interval from which the values of the
initial neural network parameters will be drawn.
eval_dtype: dtype to be used for fitnesses. If not specified, then
`eval_dtype` will be inferred from the dtype of the parameters
of the neural network.
In more details, if the neural network's parameters have a
float dtype, `eval_dtype` will be a compatible float.
Otherwise, it will be "float32".
eval_data_length: Length of the extra evaluation data.
seed: Random number seed. If left as None, this NEProblem instance
will not have its own random generator, and the global random
generator of PyTorch will be used instead.
num_actors: Number of actors to create for parallelized
evaluation of the solutions.
Certain string values are also accepted.
When given as "max" or as "num_cpus", the number of actors
will be equal to the number of all available CPUs in the ray
cluster.
When given as "num_gpus", the number of actors will be
equal to the number of all available GPUs in the ray
cluster, and each actor will be assigned a GPU.
When given as "num_devices", the number of actors will be
equal to the minimum among the number of CPUs and the number
of GPUs available in the cluster (or will be equal to the
number of CPUs if there is no GPU), and each actor will be
assigned a GPU (if available).
If `num_actors` is given as "num_gpus" or "num_devices",
the argument `num_gpus_per_actor` must not be used,
and the `actor_config` dictionary must not contain the
key "num_gpus".
If `num_actors` is given as something other than "num_gpus"
or "num_devices", and if you wish to assign GPUs to each
actor, then please see the argument `num_gpus_per_actor`.
actor_config: A dictionary, representing the keyword arguments
to be passed to the options(...) used when creating the
ray actor objects. To be used for explicitly allocating
resources per each actor.
For example, for declaring that each actor is to use a GPU,
one can pass `actor_config=dict(num_gpus=1)`.
Can also be given as None (which is the default),
if no such options are to be passed.
num_gpus_per_actor: Number of GPUs to be allocated by each
remote actor.
The default behavior is to NOT allocate any GPU at all
(which is the default behavior of the ray library as well).
When given as a number `n`, each actor will be given
`n` GPUs (where `n` can be an integer, or can be a `float`
for fractional allocation).
When given as a string "max", then the available GPUs
across the entire ray cluster (or within the local computer
in the simplest cases) will be equally distributed among
the actors.
When given as a string "all", then each actor will have
access to all the GPUs (this will be achieved by suppressing
the environment variable `CUDA_VISIBLE_DEVICES` for each
actor).
When the problem is not distributed (i.e. when there are
no actors), this argument is expected to be left as None.
num_subbatches: If `num_subbatches` is None (assuming that
`subbatch_size` is also None), then, when evaluating a
population, the population will be split into n pieces, `n`
being the number of actors, and each actor will evaluate
its assigned piece. If `num_subbatches` is an integer `m`,
then the population will be split into `m` pieces,
and actors will continually accept the next unevaluated
piece as they finish their current tasks.
The arguments `num_subbatches` and `subbatch_size` cannot
be given values other than None at the same time.
While using a distributed algorithm, this argument determines
how many sub-batches will be generated, and therefore,
how many gradients will be computed by the remote actors.
subbatch_size: If `subbatch_size` is None (assuming that
`num_subbatches` is also None), then, when evaluating a
population, the population will be split into `n` pieces, `n`
being the number of actors, and each actor will evaluate its
assigned piece. If `subbatch_size` is an integer `m`,
then the population will be split into pieces of size `m`,
and actors will continually accept the next unevaluated
piece as they finish their current tasks.
When there can be significant difference across the solutions
in terms of computational requirements, specifying a
`subbatch_size` can be beneficial, because, while one
actor is busy with a subbatch containing computationally
challenging solutions, other actors can accept more
tasks and save time.
The arguments `num_subbatches` and `subbatch_size` cannot
be given values other than None at the same time.
While using a distributed algorithm, this argument determines
the size of a sub-batch (or sub-population) sampled by a
remote actor for computing a gradient.
In distributed mode, it is expected that the population size
is divisible by `subbatch_size`.
device: Default device in which a new population will be generated
and the neural networks will operate.
If not specified, "cpu" will be used.
"""
# Set the main device of the problem
# Although the operation of setting the main device is done by the main Problem class,
# here we need this at an earlier stage.
if device is None:
device = "cpu"
self._device = torch.device(device)
# Set the network
self._original_network = network
self._network_args = {} if network_args is None else deepcopy(network_args)
if isinstance(self._original_network, nn.Module):
self._original_network = self._original_network.cpu()
# Store the function that will evaluate the network, if available
self._network_eval_func: Optional[Callable] = network_eval_func
self.instantiated_network: nn.Module = None
# Create temporary network
temp_network = self._instantiate_net(self._original_network, device="cpu")
super().__init__(
objective_sense=objective_sense,
initial_bounds=initial_bounds,
bounds=None, # Neuroevolution is an unbounded problem
solution_length=count_parameters(temp_network), # The solution length is inherited from the network passed
dtype=next(temp_network.parameters()).dtype, # The datatype is inherited from the network passed
eval_dtype=eval_dtype,
device=device,
eval_data_length=eval_data_length,
seed=seed,
num_actors=num_actors,
num_gpus_per_actor=num_gpus_per_actor,
actor_config=actor_config,
num_subbatches=num_subbatches,
subbatch_size=subbatch_size,
store_solution_stats=None,
)
make_net(self, parameters)
¶
Make a new network filled with the provided parameters.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
parameters |
Iterable |
Parameters to be used as weights within the network. Can be a Solution, or any 1-dimensional Iterable that can be converted to a PyTorch tensor. |
required |
Returns:
| Type | Description |
|---|---|
Module |
A new network, as a |
Source code in evotorch/neuroevolution/neproblem.py
def make_net(self, parameters: Iterable) -> nn.Module:
"""
Make a new network filled with the provided parameters.
Args:
parameters: Parameters to be used as weights within the network.
Can be a Solution, or any 1-dimensional Iterable that can be
converted to a PyTorch tensor.
Returns:
A new network, as a `torch.Module` instance.
"""
if isinstance(parameters, Solution):
parameters = parameters.access_values(keep_evals=True)
else:
parameters = self.as_tensor(parameters)
with torch.no_grad():
net = deepcopy(self.parameterize_net(parameters))
return net
network_constants(self)
¶
Named constants which can be passed to the network instantiation e.g. input/output dimension
parameterize_net(self, parameters)
¶
Parameterize the network with a given set of parameters.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
parameters |
torch.Tensor |
The parameters with which to instantiate the network |
required |
Returns:
| Type | Description |
|---|---|
instantiated_network (nn.Module) |
The network instantiated with the parameters |
Source code in evotorch/neuroevolution/neproblem.py
def parameterize_net(self, parameters: torch.Tensor) -> nn.Module:
"""Parameterize the network with a given set of parameters.
Args:
parameters (torch.Tensor): The parameters with which to instantiate the network
Returns:
instantiated_network (nn.Module): The network instantiated with the parameters
"""
# Check if network exists
if self.instantiated_network is None:
self.instantiated_network = self._instantiate_net(self._original_network)
network = self.instantiated_network
# Move the parameters if needed
if parameters.device != self.network_device:
parameters = parameters.to(self.network_device)
# Fill the network with the parameters
fill_parameters(network, parameters)
# Return the network
return network