e3nn_mlx.models.gate_points_2102.Network

class e3nn_mlx.models.gate_points_2102.Network(irreps_in, irreps_hidden, irreps_out, irreps_node_attr, irreps_edge_attr, layers, max_radius, number_of_basis, radial_layers, radial_neurons, num_neighbors, num_nodes, reduce_output=True)

Gated E(3)-equivariant network operating on one or more point graphs.

Parameters:

• layers (int)

• max_radius (float)

• number_of_basis (int)

• radial_layers (int)

• radial_neurons (int)

• num_neighbors (float)

• num_nodes (float)

• reduce_output (bool)

__init__(irreps_in, irreps_hidden, irreps_out, irreps_node_attr, irreps_edge_attr, layers, max_radius, number_of_basis, radial_layers, radial_neurons, num_neighbors, num_nodes, reduce_output=True)

Should be called by the subclasses of Module.

Parameters:

• layers (int)

• max_radius (float)

• number_of_basis (int)

• radial_layers (int)

• radial_neurons (int)

• num_neighbors (float)

• num_nodes (float)

• reduce_output (bool)

Return type:

None

Methods

__init__(irreps_in, irreps_hidden, ...[, ...])	Should be called by the subclasses of Module.
apply(map_fn[, filter_fn])	Map all the parameters using the provided map_fn and immediately update the module with the mapped parameters.
apply_to_modules(apply_fn)	Apply a function to all the modules in this instance (including this instance).
children()	Return the direct descendants of this Module instance.
clear()
copy()
eval()	Set the model to evaluation mode.
filter_and_map(filter_fn[, map_fn, is_leaf_fn])	Recursively filter the contents of the module using filter_fn, namely only select keys and values where filter_fn returns true.
forward_with_edges(positions, node_input, ...)
freeze(*[, recurse, keys, strict])	Freeze the Module's parameters or some of them.
fromkeys(iterable[, value])	Create a new dictionary with keys from iterable and values set to value.
get(key[, default])	Return the value for key if key is in the dictionary, else default.
is_module(value)
items()
keys()
leaf_modules()	Return the submodules that do not contain other modules.
load_weights(file_or_weights[, strict])	Update the model's weights from a .npz, a .safetensors file, or a list.
modules()	Return a list with all the modules in this instance.
named_modules()	Return a list with all the modules in this instance and their name with dot notation.
parameters()	Recursively return all the mlx.core.array members of this Module as a dict of dicts and lists.
pop(k[,d])	If the key is not found, return the default if given; otherwise, raise a KeyError.
popitem()	Remove and return a (key, value) pair as a 2-tuple.
save_weights(file)	Save the model's weights to a file.
set_dtype(dtype[, predicate])	Set the dtype of the module's parameters.
setdefault(key[, default])	Insert key with a value of default if key is not in the dictionary.
train([mode])	Set the model in or out of training mode.
trainable_parameter_filter(module, key, value)
trainable_parameters()	Recursively return all the non frozen mlx.core.array members of this Module as a dict of dicts and lists.
unfreeze(*[, recurse, keys, strict])	Unfreeze the Module's parameters or some of them.
update(parameters[, strict])	Replace the parameters of this Module with the provided ones in the dict of dicts and lists.
update_modules(modules[, strict])	Replace the child modules of this Module instance with the provided ones in the dict of dicts and lists.
valid_child_filter(module, key, value)
valid_parameter_filter(module, key, value)
values()

Attributes

state	The module's state dictionary
training	Boolean indicating if the model is in training mode.