Update AI model

AutoencoderCheb.py

@@ -1,52 +1,9 @@
 import torch
 import torch.nn as nn
-# import torch.nn.functional as F
-# import torch.optim as optim
 import torch
-# import torchvision.transforms as transforms
-# import numpy as np
-# import os
-# from os import listdir
-from os.path import join
-# import pandas as pd
-# import cv2 as cv
-# from torch.utils.data import DataLoader, Dataset, random_split
-# from PIL import Image, ImageOps
-#from torchmetrics import Accuracy
 from pytorch_lightning import LightningModule
-# from pytorch_lightning.callbacks.early_stopping import EarlyStopping
-# from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
-#from pytorch_lightning.callbacks.progress import TQDMProgressBar
-# from pytorch_lightning.loggers import TensorBoardLogger
-# from torchvision import models
-# import general_functions
-# from data_loader import DatasetCostum
-# import time
-# import optuna
-# from pytorch_lightning.callbacks.progress import TQDMProgressBar
-# from pytorch_lightning.callbacks.early_stopping import EarlyStopping
-# import statistics
-# import panorama.datasets
-# from optuna.integration import PyTorchLightningPruningCallback
-# from graph_attention_layer_or import GATLayerImp3
-
-# from pytorch_forecasting import TimeSeriesDataSet
-# from pytorch_forecasting.data.encoders import TorchNormalizer
-# from torch_geometric import EdgeIndex
-# from graph.astgcn import ASTGCN
-# from graph.gconv_gru import GConvGRU
-#import ray
-#from ray import tune
-#from ray.tune.tuner import Tuner, TuneConfig
-#from ray.tune.search.optuna import OptunaSearch
-# import pickle
-# import sqlite3
-# import json
-# from torch.utils.tensorboard import SummaryWriter
-# from tgcn import TGCN2
 from torch_geometric.nn import ChebConv, Sequential
-# from torch.optim.lr_scheduler import ReduceLROnPlateau
-# from GAT_layer import GATLayer, GraphAttentionLayer
+
 
 
 class AutoEncoderModel(LightningModule):
@@ -63,31 +20,16 @@ class AutoEncoderModel(LightningModule):
         else:
             self.dev = "cpu"
         self.num_nodes = 15
-        # self.optimizer_name = hyper_params["optimizer_name"]
-      
-
-        # self.tb_writer = summary_writer
 
         self.edge_index_att = None
         
         self.batch_size = batch_size  
         self.criterion = nn.MSELoss(reduction='mean')
-
-        self.steps = 0
-        self.recon_loss_train_step = 0
-        self.recon_loss_train_step_list = list()
-        self.recon_loss_tain_epoch_list = list()
-        self.recon_loss_val_step = 0
-        self.recon_loss_val_step_list = list()
-        self.recon_loss_val_epoch_list = list()
-        self.recon_loss_test_step_list = list()
-        self.epoch = 0
         
         self.window = 64
         self.automatic_optimization = True
         self.test_target_data, self.test_predict_data = list(), list()
-        # self.output_decoder = torch.rand(self.batch_size, self.num_nodes, self.window) # check size
-        #  self.output_decoder.requires_grad_()
+
         self.output_first_layer_decoder = torch.rand(self.batch_size, self.num_nodes, self.window*4) # check size
         self.output_first_layer_decoder.requires_grad_()
         self.output_first_layer_decoder.to(self.dev)
@@ -95,9 +37,6 @@ class AutoEncoderModel(LightningModule):
         self.node_num_featues = 5
         self.total_feat = self.node_num_featues * self.window
         
-        
-        
-        ### Original Code
         self.k = 4
         latent_dim = 104
         self.beta = 0.009256865323169841

eval.py

@@ -1,7 +1,7 @@
 import torch
-from HuggingFaceProjects.FalseDetector.AutoencoderCheb import AutoEncoderModel
+from AutoencoderCheb import AutoEncoderModel
 model = AutoEncoderModel(cuda_true=True, batch_size=32)
 
-model.load_state_dict(torch.load("/home/roth/git_projects/HuggingFaceProjects/FalseDetector/gae_25_08_2025.pt"))
+model.load_state_dict(torch.load("ChebAutoencoder/gae_25_08_2025.pt"))
 print("model", model)
 model.eval()