<jupyter_start><jupyter_text>Deadline до **22.06.2021 23:59**  Обучение TabNet 1. Реализовать архитектуру TabNet (см. папку TabNet в week08_09).2. Обучить на датасете adult, на котором обучали DeepFM.3. Показать графики обучения, точности на выборках.<jupyter_code>import torch
print(torch.__version__)

import warnings
warnings.filterwarnings("ignore")

import torch.optim as optim
import torch.utils.data as data_utils

from torch.utils.tensorboard import SummaryWriter
%load_ext tensorboard
from pytorch_lightning.metrics import Accuracy

from network import TabNet
from data_loader import CustomDataset
from dataset_utils import preprocessing
from train import Tab

import pickle
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
preprocessing()
tab = Tab()
tab.run_train()<jupyter_output>Run train ...
EPOCH 0 STEP 20 : train_loss: 0.706476 train_roc_auc: 0.522350
EPOCH 0 STEP 40 : train_loss: 0.693616 train_roc_auc: 0.516111
EPOCH 0 : valid_roc_auc: 0.507430
EPOCH 1 STEP 60 : train_loss: 0.667704 train_roc_auc: 0.592122
EPOCH 1 STEP 80 : train_loss: 0.678843 train_roc_auc: 0.544139
EPOCH 1 : valid_roc_auc: 0.543362
EPOCH 2 STEP 100 : train_loss: 0.640607 train_roc_auc: 0.620427
EPOCH 2 STEP 120 : train_loss: 0.616681 train_roc_auc: 0.710453
EPOCH 2 : valid_roc_auc: 0.567695
EPOCH 3 STEP 140 : train_loss: 0.612138 train_roc_auc: 0.693242
EPOCH 3 STEP 160 : train_loss: 0.602201 train_roc_auc: 0.703383
EPOCH 3 : valid_roc_auc: 0.598012
EPOCH 4 STEP 180 : train_loss: 0.591517 train_roc_auc: 0.700637
EPOCH 4 STEP 200 : train_loss: 0.578915 train_roc_auc: 0.745691
EPOCH 4 : valid_roc_auc: 0.599383
EPOCH 5 STEP 220 : train_loss: 0.582027 train_roc_auc: 0.711094
EPOCH 5 STEP 240 : train_loss: 0.569309 train_roc_auc: 0.747641
EPOCH 5 : valid_roc_auc: 0.614770
EPOCH 6 STEP 260 :[...]
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