drl_upn_networks.py

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from hermetic_principles import HermeticPrinciples  # Assuming this is the correct import
from ailibrary.some_module import SomeClass  # Example import from ailibrary

class Model:
    def __init__(self, input_dim, output_dim, archetype_name=None, context=None, _api_key=None, _service_url=None):
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.archetype_name = archetype_name
        self.context = context
        self._api_key = _api_key
        self._service_url = _service_url
        self.hermetic_principles = HermeticPrinciples()
        self.some_class_instance = SomeClass()  # Example usage of ailibrary

        # Example neural network model
        self.model = nn.Sequential(
            nn.Linear(input_dim, 128),
            nn.ReLU(),
            nn.Linear(128, output_dim)
        )
        self.criterion = nn.MSELoss()
        self.optimizer = optim.Adam(self.model.parameters(), lr=0.001)

    def train(self, data, targets):
        self.model.train()
        self.optimizer.zero_grad()
        outputs = self.model(data)
        loss = self.criterion(outputs, targets)
        loss.backward()
        self.optimizer.step()
        return loss.item()

    def predict_drl(self, data):
        self.model.eval()
        with torch.no_grad():
            outputs = self.model(data)
        return outputs

# Example usage:
# model = Model(input_dim=10, output_dim=1)
# loss = model.train(data, targets)
# predictions = model.predict_drl(data)