nip.parameters.agents.ImageClassificationAgentParameters

class nip.parameters.agents.ImageClassificationAgentParameters(agent_lr_factor: ~nip.parameters.agents.LrFactors | dict | None = None, body_lr_factor: ~nip.parameters.agents.LrFactors | dict | None = None, update_schedule: ~nip.parameters.update_schedule.AgentUpdateSchedule = ConstantUpdateSchedule(), use_manual_architecture: bool = False, normalize_message_history: bool = False, load_checkpoint_and_parameters: bool = False, checkpoint_entity: str = <factory>, checkpoint_project: str = <factory>, checkpoint_run_id: str | None = None, checkpoint_version: str = 'latest', use_orthogonal_initialisation: bool = True, orthogonal_initialisation_gain: float = 1.0, activation_function: ~typing.Literal['relu', 'tanh', 'sigmoid'] = 'tanh', building_block_type: ~typing.Literal['conv2d', 'residual_basic'] = 'conv2d', num_blocks_per_group: int = 2, kernel_size: int = 3, stride: int = 1, pretrained_embeddings_model: str | None = None, pretrained_embedding_channels: int = 64, d_latent_pixel_selector: int = 16, num_latent_pixel_selector_layers: int = 2, d_decider: int = 16, num_decider_layers: int = 2, include_round_in_decider: bool = True, d_linear_message_selector: int = 16, num_linear_message_selector_layers: int = 2, d_value: int = 16, num_value_layers: int = 2, include_round_in_value: bool = True)

Additional parameters for agents in the image classification experiment.

An image classification network is composed of several groups of building blocks, such as convolutional layers. Each group contains several building blocks.

Parameters:

• activation_function (ActivationType) – The activation function to use.

• building_block_type (ImageBuildingBlockType) – The type of building block to use in the agents’s CNN (e.g. convolutional layer).

• num_blocks_per_group (int) – The number of building blocks in each group in the agents’s CNN.

• kernel_size (int) – The kernel size of the building blocks in the agents’s CNN.

• stride (int) – The stride of the building blocks in the agents’s CNN.

• pretrained_embeddings_model (str or None) – If not None, specifies a pretrained model to load. This is usually either of the form “{hf_user}/{model_name}_{dataset}”, where hf_user is a HuggingFace Hub username, or “{model_name}”, which resolves to “{HF_PRETRAINED_MODELS_USER}/{model_name}_{hyper_params.dataset}”, where HF_PRETRAINED_MODELS_USER is defined in the constants module. The last-layer embeddings will be included in the model architecture.

• pretrained_embedding_channels (int) – The number of channels used to represent the pretrained embeddings. The pretrained embeddings are resized to this number of channels by using a 1x1 convolution.

• d_latent_pixel_selector (int) – The dimension of the hidden layer in the agents’s MLP which selects a latent pixel to send as a message.

• num_latent_pixel_selector_layers (int) – The number of layers in the agents’s latent pixel selector MLP.

• d_decider (int) – The dimension of the hidden layer in the agents’s MLP which decides whether continue exchanging messages or to guess.

• num_decider_layers (int) – The number of layers in the agents’s decider MLP.

• include_round_in_decider (bool) – Whether to include the round number in the agents’s decider MLP.

• d_linear_message_selector (int) – The dimension of the hidden layer in the agents’s MLP which selects a linear message, if we’re using the linear message space.

• num_linear_message_selector_layers (int) – The number of layers in the agents’s linear message selector MLP.

• d_value (int) – The dimension of the hidden layer in the agents’s MLP which estimates the value function.

• num_value_layers (int) – The number of layers in the agents’s value MLP.

• include_round_in_value (bool) – Whether to include the round number in the agents’s value MLP.

Methods Summary

__eq__(other)	Return self==value.
__init__([agent_lr_factor, body_lr_factor, ...])
__post_init__()
__repr__()	Return repr(self).
_get_param_class_from_dict(param_dict)	Try to get the parameter class from a dictionary of serialised parameters.
construct_test_params()	Construct test parameters for the agent.
from_dict(params_dict[, ignore_extra_keys])	Create a parameters object from a dictionary.
get(address)	Get a value from the parameters object using a dot-separated address.
load_from_wandb_config(wandb_config)	Load the parameters from a W&B config dictionary.
to_dict()	Convert the parameters object to a dictionary.

Attributes

LOAD_PRESERVED_PARAMETERS
activation_function
agent_lr_factor
body_lr_factor
building_block_type
checkpoint_run_id
checkpoint_version
d_decider
d_latent_pixel_selector
d_linear_message_selector
d_value
include_round_in_decider
include_round_in_value
is_random
kernel_size
load_checkpoint_and_parameters
normalize_message_history
num_blocks_per_group
num_decider_layers
num_latent_pixel_selector_layers
num_linear_message_selector_layers
num_value_layers
orthogonal_initialisation_gain
pretrained_embedding_channels
pretrained_embeddings_model
stride
update_schedule
use_manual_architecture
use_orthogonal_initialisation
checkpoint_entity
checkpoint_project

Methods

__eq__(other)

Return self==value.

__init__(agent_lr_factor: ~nip.parameters.agents.LrFactors | dict | None = None, body_lr_factor: ~nip.parameters.agents.LrFactors | dict | None = None, update_schedule: ~nip.parameters.update_schedule.AgentUpdateSchedule = ConstantUpdateSchedule(), use_manual_architecture: bool = False, normalize_message_history: bool = False, load_checkpoint_and_parameters: bool = False, checkpoint_entity: str = <factory>, checkpoint_project: str = <factory>, checkpoint_run_id: str | None = None, checkpoint_version: str = 'latest', use_orthogonal_initialisation: bool = True, orthogonal_initialisation_gain: float = 1.0, activation_function: ~typing.Literal['relu', 'tanh', 'sigmoid'] = 'tanh', building_block_type: ~typing.Literal['conv2d', 'residual_basic'] = 'conv2d', num_blocks_per_group: int = 2, kernel_size: int = 3, stride: int = 1, pretrained_embeddings_model: str | None = None, pretrained_embedding_channels: int = 64, d_latent_pixel_selector: int = 16, num_latent_pixel_selector_layers: int = 2, d_decider: int = 16, num_decider_layers: int = 2, include_round_in_decider: bool = True, d_linear_message_selector: int = 16, num_linear_message_selector_layers: int = 2, d_value: int = 16, num_value_layers: int = 2, include_round_in_value: bool = True) → None

__post_init__()

__repr__()

Return repr(self).

classmethod _get_param_class_from_dict(param_dict: dict) → type[ParameterValue] | None

Try to get the parameter class from a dictionary of serialised parameters.

Parameters:

param_dict (dict) – A dictionary of parameters, which may have come from a to_dict method. This dictionary may contain a _type key, which is used to determine the class of the parameter.

Returns:

param_class (type[ParameterValue] | None) – The class of the parameter, if it can be determined.

Raises:

ValueError – If the class specified in the dictionary is not a valid parameter class.

classmethod construct_test_params() → ImageClassificationAgentParameters

Construct test parameters for the agent.

We use a simple architecture with one convolutional layer.

Returns:

test_params (ImageClassificationAgentParameters) – The test parameters.

classmethod from_dict(params_dict: dict, ignore_extra_keys: bool = False) → AgentsParameters

Create a parameters object from a dictionary.

Parameters:

• params_dict (dict) – A dictionary of the parameters.

• ignore_extra_keys (bool, default=False) – If True, ignore keys in the dictionary that do not correspond to fields in the parameters object.

Returns:

hyper_params (AgentsParameters) – The parameters object.

get(address: str) → Any

Get a value from the parameters object using a dot-separated address.

Parameters:

address (str) – The path to the value in the parameters object, separated by dots.

Returns:

value (Any) – The value at the address.

Raises:

KeyError – If the address does not exist.

load_from_wandb_config(wandb_config: dict)

Load the parameters from a W&B config dictionary.

Parameters:

wandb_config (dict) – The W&B config dictionary for this agent (e.g. wandb_run.config["agents"][agent_name]).

to_dict() → dict

Convert the parameters object to a dictionary.

Adds the is_random parameter to the dictionary. This is not a field of the parameters object, but we want to include it in the dictionary for logging.

Returns:

params_dict (dict) – A dictionary of the parameters.