stroblmeandClaude Opus 5
60d7ec81c0
A wheel whose top-level module is `app` collides with anything else in a user's venv, so the package that is about to be published takes the name it is published under. Only the Python package moves; the repo, the Docker WORKDIR and the compose project keep theirs. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
101 lines
3.3 KiB
Python
101 lines
3.3 KiB
Python
"""A small perceptron node, kept as a worked example of numeric logic."""
|
|
|
|
from __future__ import annotations
|
|
|
|
import logging
|
|
import threading
|
|
from collections.abc import Iterable
|
|
from typing import Any
|
|
|
|
import numpy as np
|
|
from pydantic import BaseModel, ConfigDict
|
|
|
|
from fluksio.flow.messages import MessageSpec
|
|
from fluksio.flow.nodes.base import Node
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class MLPNode(Node):
|
|
"""
|
|
Multi-Layer Perceptron node for neural network processing in pipelines.
|
|
|
|
This node implements a simple single-layer neural network that applies
|
|
weights and biases to input values. Weights and biases are randomly
|
|
initialized using the provided random number generator.
|
|
|
|
The computation follows the standard neural network formula:
|
|
output = weights @ inputs + biases
|
|
|
|
:param requires: Input messages consumed by this node.
|
|
:type requires: MessageSpec | list[MessageSpec]
|
|
:param provides: Output messages produced by this node.
|
|
:type provides: MessageSpec | list[MessageSpec]
|
|
:param params: Parameters dict containing:
|
|
- ``rng`` (numpy.random.Generator): Random number generator for weight initialization
|
|
- Additional node parameters
|
|
:type params: dict
|
|
:param name: Name for this node.
|
|
:type name: str
|
|
|
|
:example:
|
|
>>> import numpy as np
|
|
>>> rng = np.random.default_rng(seed=42)
|
|
>>> mlp = MLPNode(
|
|
... requires=[MessageSpec(name="input1", dtype=DType.FLOAT), MessageSpec(name="input2", dtype=DType.FLOAT)],
|
|
... provides=[MessageSpec(name="output", dtype=DType.FLOAT)],
|
|
... params={"rng": rng},
|
|
... name="mlp_layer1",
|
|
... )
|
|
"""
|
|
|
|
class Params(BaseModel):
|
|
"""Weights are drawn from ``seed``, so a node reloads identically."""
|
|
|
|
model_config = ConfigDict(extra="allow")
|
|
|
|
seed: int = 0
|
|
|
|
def __init__(
|
|
self,
|
|
requires: MessageSpec | Iterable[MessageSpec] = (),
|
|
provides: MessageSpec | Iterable[MessageSpec] = (),
|
|
params: dict[str, Any] | None = None,
|
|
name: str | None = None,
|
|
):
|
|
super().__init__(
|
|
self._forward,
|
|
requires=requires,
|
|
provides=provides,
|
|
params=params,
|
|
name=name or "mlp",
|
|
)
|
|
cfg = self.Params.model_validate(self.params)
|
|
|
|
rng = np.random.default_rng(seed=cfg.seed)
|
|
num_inputs = max(1, len(self.input_ports))
|
|
num_outputs = max(1, len(self.output_ports))
|
|
self.weights = rng.normal(loc=1, size=(num_outputs, num_inputs))
|
|
self.biases = rng.normal(loc=0, size=(num_outputs,))
|
|
|
|
def _forward(
|
|
self, params: dict[str, Any], **kwargs: float
|
|
) -> dict[str, Any] | None:
|
|
"""Apply ``weights @ inputs + biases`` to the incoming values."""
|
|
if not self.output_ports:
|
|
return None
|
|
|
|
logger.info(
|
|
"Executing MLP node in thread %s: %s",
|
|
threading.current_thread().name,
|
|
self.id,
|
|
)
|
|
|
|
if kwargs:
|
|
input_array = np.array([float(v) for v in kwargs.values()])
|
|
else:
|
|
input_array = np.array([1.0]) # Bias only, for source nodes.
|
|
|
|
outputs = np.dot(self.weights, input_array) + self.biases
|
|
return {p.port: float(outputs[i]) for i, p in enumerate(self.output_ports)}
|