目的：熟悉熟悉pytorch

导入数据

!gdown --id '1kLSW_-cW2Huj7bh84YTdimGBOJaODiOS' --output covid.train.csv
!gdown --id '1iiI5qROrAhZn-o4FPqsE97bMzDEFvIdg' --output covid.test.csv

/Users/missbei/miniforge3/envs/NLP_search/lib/python3.8/site-packages/gdown/cli.py:127: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.warnings.warn(
Downloading...
From: https://drive.google.com/uc?id=1kLSW_-cW2Huj7bh84YTdimGBOJaODiOS
To: /Users/missbei/miniforge3/envs/NLP_search/Bilibili/Hung-Yi Lee/covid.train.csv
100%|██████████████████████████████████████| 2.49M/2.49M [00:00<00:00, 38.8MB/s]
/Users/missbei/miniforge3/envs/NLP_search/lib/python3.8/site-packages/gdown/cli.py:127: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.warnings.warn(
Downloading...
From: https://drive.google.com/uc?id=1iiI5qROrAhZn-o4FPqsE97bMzDEFvIdg
To: /Users/missbei/miniforge3/envs/NLP_search/Bilibili/Hung-Yi Lee/covid.test.csv
100%|████████████████████████████████████████| 993k/993k [00:00<00:00, 15.1MB/s]

import Package

import math
import numpy as np
import pandas as pd
import os
import csv

import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader, random_split

Some prepared code, no need to understand

from tqdm import tqdm
def same_seed(seed): '''Fixes random number generator seeds for reproducibility.'''torch.backends.cudnn.deterministic = Truetorch.backends.cudnn.benchmark = Falsenp.random.seed(seed)torch.manual_seed(seed)if torch.cuda.is_available():torch.cuda.manual_seed_all(seed)def train_valid_split(data_set, valid_ratio, seed):'''Split provided training data into training set and validation set'''valid_set_size = int(valid_ratio * len(data_set)) train_set_size = len(data_set) - valid_set_sizetrain_set, valid_set = random_split(data_set, [train_set_size, valid_set_size], generator=torch.Generator().manual_seed(seed))return np.array(train_set), np.array(valid_set)def predict(test_loader, model, device):model.eval() # Set your model to evaluation mode.preds = []for x in tqdm(test_loader):x = x.to(device)                        with torch.no_grad():                   pred = model(x)                     preds.append(pred.detach().cpu())   preds = torch.cat(preds, dim=0).numpy()  return preds

Dataset

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-SlVuXp5c-1661580334880)(attachment:%E6%88%AA%E5%B1%8F2022-08-26%20%E4%B8%8B%E5%8D%886.32.46.png)]

• 第0列是id
• 第1-37列是37个州的one-hot编码
• 38-41是COVID-like illness
• 42-49是Behavior Indicators
• 50-52是Mental Health Indicators
• 53是最后检测结果，阳不阳
• 后续是第2天-第5天的结果，都写成列

class COVID19Dataset(Dataset):'''if no y, the predict'''def __init__(self, x, y = None):if y is None:self.y = yelse:self.y = torch.FloatTensor(y)self.x = torch.FloatTensor(x)def __getitem__(self, index):if self.y is None:return self.x[index]else:return self.x[index], self.y[index]def __len__(self):return len(self.x)

Neural Network Model

class NN_Model(nn.Module):def __init__(self, input_dim):super().__init__()              # 搞定多个继承关系的self.layers = nn.Sequential(nn.Linear(input_dim, 16),nn.ReLU(),nn.Linear(16, 8),nn.ReLU(),nn.Linear(8, 1))def forward(self, x):x = self.layers(x)x = x.squeeze(1)                # (B, 1) ->(B)return x

Featurn Selection

def select_feat(train_data, valid_data, test_data, select_all = True):'''Selects useful features to perform regression'''y_train, y_valid = train_data[:, -1], valid_data[:, -1]raw_x_train, raw_x_valid, raw_x_test = train_data[:, :-1], valid_data[:, :-1], test_dataif select_all:feat_idx = list(range(raw_x_train.shape[1]))# else: #根据传入参数自行筛选return raw_x_train[:, feat_idx], raw_x_valid[:, feat_idx], raw_x_test[:, feat_idx], y_train, y_valid

Configurations

device = 'cuda' if torch.cuda.is_available() else 'cpu'
config = {'seed' : 5201314,'select_all' : True,'valid_ratio' : 0.2,'n_epochs' : 3000,'batch_size': 256,'learning_rate' : 1e-5,'early_stop' : 400, #如果模型连续400个epoch没进步，那就停'save_path' : './models/model.ckpt' # save in hear       问题1:??没有s吗？？
}

Dataloader

# 让种子全部复用
same_seed(config['seed'])# train_data.shape = (2699, 118)     id + 37states + 16feats*5days
# test_data.shape = (1078, 117)      without outcome case
train_data, test_data = pd.read_csv('./covid.train.csv').values, pd.read_csv('./covid.test.csv').values# def train_valid_split(data_set, valid_ratio, seed)
train_data, valid_data = train_valid_split(train_data, config['valid_ratio'], config['seed'])# 打印一下data size
print(f'''train_data size: {train_data.shape}
valid_data size: {valid_data.shape}
test_data size: {test_data.shape}''')# select features    def select_feat(train_data, valid_data, test_data, select_all = True)
x_train, x_valid, x_test, y_train, y_valid = select_feat(train_data, valid_data, test_data, config['select_all'])# 打印选择的featurn数量
print(f'''number of features: {x_train.shape[1]}''')# 制作数据集
train_dataset = COVID19Dataset(x_train, y_train)
valid_dataset = COVID19Dataset(x_valid, y_valid)
test_dataset = COVID19Dataset(x_test)# 使用dataloader
train_loader = DataLoader(train_dataset, batch_size = config['batch_size'], shuffle = True) # 问题2: pin——memory是什么？
valid_loader = DataLoader(valid_dataset, batch_size = config['batch_size'], shuffle = True)
test_loader = DataLoader(test_dataset, batch_size = config['batch_size'], shuffle = True)

train_data size: (2160, 118)
valid_data size: (539, 118)
test_data size: (1078, 117)
number of features: 117

DataLoader??

Training Loop

def trainer(train_loader, valid_loader, model, config, device):# Define your optimization algorithm. # TODO: Please check https://pytorch.org/docs/stable/optim.html to get more available algorithms.# TODO: L2 regularization (optimizer(weight decay...) or implement by your self).criterion = nn.MSELoss(reduction = 'mean') # 损失函数optimizer = torch.optim.SGD(model.parameters(), lr = config['learning_rate'], momentum = 0.9)# 创建存放模型的目录，如果不存在的话if not os.path.isdir('./models'):os.mkdir('./models')n_epochs, best_loss, step, early_stop_count = config['n_epochs'], math.inf, 0, 0for epoch in range(n_epochs):model.train()      #在这里设置成训练模式loss_record = []# tqdm 是用来可视化训练过程的train_pbar = tqdm(train_loader, position = 0, leave = True)      # 问题3: 不会tqdm这个packagefor x, y in train_pbar:optimizer.zero_grad()       # 把gradient设置为0x, y = x.to(device), y.to(device)pred = model(x)loss = criterion(pred, y)loss.backward()           # 计算gradientoptimizer.step()          # 更新参数step += 1loss_record.append(loss.detach().item())# display current epochtrain_pbar.set_description(f'Epoch[{epoch+1}/{n_epochs}]')train_pbar.set_postfix({'loss': loss.detach().item()})mean_train_loss = sum(loss_record) / len(loss_record)# 把模型变成预测模式model.eval()loss_record = []for x, y in valid_loader:x, y = x.to(device), y.to(device)with torch.no_grad():pred = model(x)loss = criterion(pred, y)loss_record.append(loss)mean_valid_loss = sum(loss_record) / len(loss_record)if epoch%5 == 0:print(f'Epoch [{epoch+1}/{n_epochs}]: Train loss: {mean_train_loss:.4f}, Valid loss: {mean_valid_loss:.4f}')if mean_valid_loss < best_loss:best_loss = mean_valid_loss# 存储模型语句torch.save(model.state_dict(), config['save_path'])
#             print('Saving model with loss {:.3f}...'.format(best_loss))early_stop_count = 0else:early_stop_count += 1if early_stop_count >= config['early_stop']:print('\nModel is not improving! Stop training session!')return

Start Training

# loader没有shape，用之前的pandas或tensor的shape
model = NN_Model(input_dim = x_train.shape[1]).to(device)
# def trainer(train_loader, valid_loader, model, config, device):
trainer(train_loader, valid_loader, model, config, device)

Epoch[1/3000]: 100%|██████████████████| 9/9 [00:00<00:00, 250.23it/s, loss=44.6]Epoch [1/3000]: Train loss: 120.6492, Valid loss: 62.5109Epoch[2/3000]: 100%|████████████████████| 9/9 [00:00<00:00, 341.45it/s, loss=46]
Epoch[3/3000]: 100%|██████████████████| 9/9 [00:00<00:00, 485.88it/s, loss=38.1]
Epoch[4/3000]: 100%|████████████████████| 9/9 [00:00<00:00, 554.18it/s, loss=33]
Epoch[5/3000]: 100%|██████████████████| 9/9 [00:00<00:00, 480.26it/s, loss=33.1]
Epoch[6/3000]: 100%|██████████████████| 9/9 [00:00<00:00, 446.64it/s, loss=27.9]Epoch [6/3000]: Train loss: 35.3303, Valid loss: 38.8439Epoch[7/3000]: 100%|██████████████████| 9/9 [00:00<00:00, 533.33it/s, loss=30.6]
Epoch[8/3000]: 100%|██████████████████| 9/9 [00:00<00:00, 517.69it/s, loss=32.2]
Epoch[9/3000]: 100%|██████████████████| 9/9 [00:00<00:00, 514.35it/s, loss=26.6]
Epoch[10/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 554.39it/s, loss=29.7]
Epoch[11/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 544.46it/s, loss=30.1]Epoch [11/3000]: Train loss: 30.9335, Valid loss: 28.3564Epoch[12/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 534.93it/s, loss=22.5]
Epoch[13/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 521.05it/s, loss=26.4]
Epoch[14/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 570.46it/s, loss=27.1]
Epoch[15/3000]: 100%|███████████████████| 9/9 [00:00<00:00, 557.76it/s, loss=28]
Epoch[16/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 505.56it/s, loss=21.1]Epoch [16/3000]: Train loss: 23.2565, Valid loss: 24.7602Epoch[17/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 531.66it/s, loss=19.7]
Epoch[18/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 524.36it/s, loss=17.1]
Epoch[19/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 622.11it/s, loss=14.3]
Epoch[20/3000]: 100%|███████████████████| 9/9 [00:00<00:00, 580.43it/s, loss=12]
Epoch[21/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 525.15it/s, loss=20.9]Epoch [21/3000]: Train loss: 14.5668, Valid loss: 37.8424Epoch[22/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 105.44it/s, loss=18.7]
Epoch[23/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 598.17it/s, loss=21.5]
Epoch[24/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 517.55it/s, loss=11.8]
Epoch[25/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 523.55it/s, loss=14.4]
Epoch[26/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 581.62it/s, loss=11.7]Epoch [26/3000]: Train loss: 10.2813, Valid loss: 7.9503Epoch[27/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 336.25it/s, loss=12.9]
Epoch[28/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 528.10it/s, loss=26.7]
Epoch[29/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 509.48it/s, loss=31.5]
Epoch[30/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 624.43it/s, loss=54.2]
Epoch[31/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 591.26it/s, loss=45.3]Epoch [31/3000]: Train loss: 35.1419, Valid loss: 30.8228Epoch[32/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 513.39it/s, loss=19.3]
Epoch[33/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 532.32it/s, loss=18.1]
Epoch[34/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 543.74it/s, loss=19.5]
Epoch[35/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 586.43it/s, loss=11.9]
Epoch[36/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 568.03it/s, loss=9.05]Epoch [36/3000]: Train loss: 13.0948, Valid loss: 13.3034Epoch[37/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 500.28it/s, loss=7.06]
Epoch[38/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 528.27it/s, loss=7.84]
Epoch[39/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 540.97it/s, loss=7.28]
Epoch[40/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 606.72it/s, loss=17.6]
Epoch[41/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 607.36it/s, loss=7.56]Epoch [41/3000]: Train loss: 15.5544, Valid loss: 36.4536Epoch[42/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 475.48it/s, loss=27.3]
Epoch[43/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 510.92it/s, loss=15.3]
Epoch[44/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 521.66it/s, loss=8.41]
Epoch[45/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 605.12it/s, loss=7.48]
Epoch[46/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 552.45it/s, loss=8.69]Epoch [46/3000]: Train loss: 9.7680, Valid loss: 11.5441Epoch[47/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 394.27it/s, loss=8.36]
Epoch[48/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 525.98it/s, loss=11.9]
Epoch[49/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 523.81it/s, loss=7.33]
Epoch[50/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 578.78it/s, loss=8.07]
Epoch[51/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 520.15it/s, loss=5.72]Epoch [51/3000]: Train loss: 6.5317, Valid loss: 5.2750Epoch[52/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 555.56it/s, loss=7.72]
Epoch[53/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 522.29it/s, loss=4.57]
Epoch[54/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 482.49it/s, loss=3.73]
Epoch[55/3000]: 100%|██████████████████| 9/9 [00:00<00:00, 568.43it/s, loss=5.9]
Epoch[56/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 514.58it/s, loss=11.8]Epoch [56/3000]: Train loss: 8.1241, Valid loss: 5.8720Epoch[57/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 491.16it/s, loss=4.36]
Epoch[58/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 511.47it/s, loss=4.52]
Epoch[59/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 522.31it/s, loss=8.92]
Epoch[60/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 590.86it/s, loss=10.6]
Epoch[61/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 485.31it/s, loss=9.16]Epoch [61/3000]: Train loss: 9.0994, Valid loss: 7.3874Epoch[62/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 493.52it/s, loss=7.16]
Epoch[63/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 475.24it/s, loss=9.74]
Epoch[64/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 566.42it/s, loss=5.54]
Epoch[65/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 524.84it/s, loss=5.91]
Epoch[66/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 501.70it/s, loss=7.36]Epoch [66/3000]: Train loss: 6.1128, Valid loss: 5.4114Epoch[67/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 501.89it/s, loss=8.69]
Epoch[68/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 494.15it/s, loss=6.44]
Epoch[69/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 552.08it/s, loss=5.46]
Epoch[70/3000]: 100%|██████████████████| 9/9 [00:00<00:00, 331.66it/s, loss=7.9]
Epoch[71/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 565.40it/s, loss=5.18]Epoch [71/3000]: Train loss: 6.4281, Valid loss: 4.6097Epoch[72/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 571.40it/s, loss=6.44]
Epoch[73/3000]: 100%|███████████████████| 9/9 [00:00<00:00, 569.63it/s, loss=10]
Epoch[74/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 634.25it/s, loss=6.29]
Epoch[75/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 578.92it/s, loss=6.46]
Epoch[76/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 575.22it/s, loss=5.16]Epoch [76/3000]: Train loss: 5.7318, Valid loss: 5.1168Epoch[77/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 558.28it/s, loss=4.02]
Epoch[78/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 558.67it/s, loss=5.75]
Epoch[79/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 635.09it/s, loss=6.12]
Epoch[80/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 599.08it/s, loss=6.61]
Epoch[81/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 521.58it/s, loss=5.37]Epoch [81/3000]: Train loss: 5.3825, Valid loss: 5.1466Epoch[82/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 559.97it/s, loss=3.52]
Epoch[83/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 608.11it/s, loss=4.43]
Epoch[84/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 639.70it/s, loss=6.82]
Epoch[85/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 629.72it/s, loss=6.58]
Epoch[86/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 575.89it/s, loss=9.49]Epoch [86/3000]: Train loss: 7.1615, Valid loss: 7.9000Epoch[87/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 564.41it/s, loss=5.49]
Epoch[88/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 558.79it/s, loss=3.65]
Epoch[89/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 577.89it/s, loss=4.14]
Epoch[90/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 652.60it/s, loss=4.35]
Epoch[91/3000]: 100%|██████████████████| 9/9 [00:00<00:00, 603.46it/s, loss=7.1]Epoch [91/3000]: Train loss: 5.8941, Valid loss: 6.2084Epoch[92/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 590.29it/s, loss=6.14]
Epoch[93/3000]: 100%|██████████████████| 9/9 [00:00<00:00, 589.35it/s, loss=5.4]
Epoch[94/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 590.98it/s, loss=4.46]
Epoch[95/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 644.04it/s, loss=5.57]
Epoch[96/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 691.13it/s, loss=3.64]Epoch [96/3000]: Train loss: 5.3036, Valid loss: 6.6357Epoch[97/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 567.65it/s, loss=8.56]
Epoch[98/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 589.28it/s, loss=6.68]
Epoch[99/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 580.15it/s, loss=5.91]
Epoch[100/3000]: 100%|████████████████| 9/9 [00:00<00:00, 584.26it/s, loss=5.26]
Epoch[101/3000]: 100%|████████████████| 9/9 [00:00<00:00, 619.83it/s, loss=5.58]Epoch [101/3000]: Train loss: 5.0137, Valid loss: 6.6421Epoch[102/3000]: 100%|████████████████| 9/9 [00:00<00:00, 583.32it/s, loss=5.95]
Epoch[103/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 572.91it/s, loss=8.5]
Epoch[104/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 596.31it/s, loss=5.6]
Epoch[105/3000]: 100%|████████████████| 9/9 [00:00<00:00, 592.66it/s, loss=4.34]
Epoch[106/3000]: 100%|████████████████| 9/9 [00:00<00:00, 114.23it/s, loss=5.92]Epoch [106/3000]: Train loss: 5.3649, Valid loss: 6.1438Epoch[107/3000]: 100%|████████████████| 9/9 [00:00<00:00, 589.86it/s, loss=6.57]
Epoch[108/3000]: 100%|████████████████| 9/9 [00:00<00:00, 638.35it/s, loss=5.45]
Epoch[109/3000]: 100%|████████████████| 9/9 [00:00<00:00, 654.87it/s, loss=5.07]
Epoch[110/3000]: 100%|████████████████| 9/9 [00:00<00:00, 548.77it/s, loss=4.64]
Epoch[111/3000]: 100%|████████████████| 9/9 [00:00<00:00, 570.70it/s, loss=5.48]Epoch [111/3000]: Train loss: 4.9703, Valid loss: 5.3325Epoch[112/3000]: 100%|████████████████| 9/9 [00:00<00:00, 546.88it/s, loss=5.25]
Epoch[113/3000]: 100%|████████████████| 9/9 [00:00<00:00, 580.29it/s, loss=4.97]
Epoch[114/3000]: 100%|████████████████| 9/9 [00:00<00:00, 638.89it/s, loss=5.27]
Epoch[115/3000]: 100%|████████████████| 9/9 [00:00<00:00, 561.86it/s, loss=5.25]
Epoch[116/3000]: 100%|████████████████| 9/9 [00:00<00:00, 611.25it/s, loss=4.32]Epoch [116/3000]: Train loss: 4.8477, Valid loss: 5.1231Epoch[117/3000]: 100%|████████████████| 9/9 [00:00<00:00, 571.83it/s, loss=5.24]
Epoch[118/3000]: 100%|████████████████| 9/9 [00:00<00:00, 577.41it/s, loss=5.16]
Epoch[119/3000]: 100%|████████████████| 9/9 [00:00<00:00, 635.28it/s, loss=6.45]
Epoch[120/3000]: 100%|████████████████| 9/9 [00:00<00:00, 597.40it/s, loss=3.46]
Epoch[121/3000]: 100%|████████████████| 9/9 [00:00<00:00, 583.35it/s, loss=5.23]Epoch [121/3000]: Train loss: 4.7657, Valid loss: 5.7246Epoch[122/3000]: 100%|████████████████| 9/9 [00:00<00:00, 584.64it/s, loss=4.46]
Epoch[123/3000]: 100%|████████████████| 9/9 [00:00<00:00, 564.09it/s, loss=5.94]
Epoch[124/3000]: 100%|████████████████| 9/9 [00:00<00:00, 604.14it/s, loss=4.37]
Epoch[125/3000]: 100%|████████████████| 9/9 [00:00<00:00, 653.42it/s, loss=4.03]
Epoch[126/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 508.39it/s, loss=4.2]Epoch [126/3000]: Train loss: 4.7372, Valid loss: 5.1906Epoch[127/3000]: 100%|████████████████| 9/9 [00:00<00:00, 558.98it/s, loss=6.33]
Epoch[128/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 510.53it/s, loss=8.5]
Epoch[129/3000]: 100%|████████████████| 9/9 [00:00<00:00, 568.93it/s, loss=12.5]
Epoch[130/3000]: 100%|████████████████| 9/9 [00:00<00:00, 647.90it/s, loss=6.99]
Epoch[131/3000]: 100%|████████████████| 9/9 [00:00<00:00, 559.49it/s, loss=9.89]Epoch [131/3000]: Train loss: 5.8339, Valid loss: 5.4816Epoch[132/3000]: 100%|████████████████| 9/9 [00:00<00:00, 572.34it/s, loss=5.14]
Epoch[133/3000]: 100%|████████████████| 9/9 [00:00<00:00, 595.11it/s, loss=5.67]
Epoch[134/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 606.60it/s, loss=5.5]
Epoch[135/3000]: 100%|████████████████| 9/9 [00:00<00:00, 670.29it/s, loss=4.95]
Epoch[136/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 600.04it/s, loss=4.5]Epoch [136/3000]: Train loss: 4.8287, Valid loss: 5.3120Epoch[137/3000]: 100%|████████████████| 9/9 [00:00<00:00, 577.52it/s, loss=4.38]
Epoch[138/3000]: 100%|████████████████| 9/9 [00:00<00:00, 574.79it/s, loss=5.22]
Epoch[139/3000]: 100%|████████████████| 9/9 [00:00<00:00, 420.46it/s, loss=5.85]
Epoch[140/3000]: 100%|████████████████| 9/9 [00:00<00:00, 651.84it/s, loss=5.34]
Epoch[141/3000]: 100%|████████████████| 9/9 [00:00<00:00, 595.74it/s, loss=4.31]Epoch [141/3000]: Train loss: 4.8080, Valid loss: 4.5696Epoch[142/3000]:   0%|                         | 0/9 [00:00<?, ?it/s, loss=4.18]IOPub message rate exceeded.
The notebook server will temporarily stop sending output
to the client in order to avoid crashing it.
To change this limit, set the config variable
`--NotebookApp.iopub_msg_rate_limit`.Current values:
NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)
NotebookApp.rate_limit_window=3.0 (secs)Epoch[189/3000]: 100%|████████████████| 9/9 [00:00<00:00, 672.20it/s, loss=4.35]
Epoch[190/3000]: 100%|████████████████| 9/9 [00:00<00:00, 652.98it/s, loss=4.53]
Epoch[191/3000]: 100%|████████████████| 9/9 [00:00<00:00, 116.17it/s, loss=4.35]Epoch [191/3000]: Train loss: 4.4727, Valid loss: 3.9192Epoch[192/3000]: 100%|████████████████| 9/9 [00:00<00:00, 597.57it/s, loss=4.91]
Epoch[193/3000]: 100%|████████████████| 9/9 [00:00<00:00, 634.87it/s, loss=4.83]
Epoch[194/3000]: 100%|████████████████| 9/9 [00:00<00:00, 589.70it/s, loss=4.54]
Epoch[195/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 555.49it/s, loss=3.1]
Epoch[196/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 595.41it/s, loss=8.4]Epoch [196/3000]: Train loss: 4.9729, Valid loss: 4.3283Epoch[197/3000]: 100%|████████████████| 9/9 [00:00<00:00, 582.82it/s, loss=4.52]
Epoch[198/3000]: 100%|████████████████| 9/9 [00:00<00:00, 621.34it/s, loss=4.66]
Epoch[199/3000]: 100%|████████████████| 9/9 [00:00<00:00, 646.37it/s, loss=3.67]
Epoch[200/3000]: 100%|████████████████| 9/9 [00:00<00:00, 547.57it/s, loss=3.57]
Epoch[201/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 576.25it/s, loss=3.9]Epoch [201/3000]: Train loss: 4.4208, Valid loss: 4.0958Epoch[202/3000]: 100%|████████████████| 9/9 [00:00<00:00, 554.88it/s, loss=4.68]
Epoch[203/3000]: 100%|████████████████| 9/9 [00:00<00:00, 547.54it/s, loss=6.22]
Epoch[204/3000]: 100%|████████████████| 9/9 [00:00<00:00, 638.16it/s, loss=3.89]
Epoch[205/3000]: 100%|████████████████| 9/9 [00:00<00:00, 592.10it/s, loss=4.02]
Epoch[206/3000]: 100%|████████████████| 9/9 [00:00<00:00, 577.82it/s, loss=4.12]Epoch [206/3000]: Train loss: 3.7307, Valid loss: 3.8586Epoch[207/3000]: 100%|████████████████| 9/9 [00:00<00:00, 608.07it/s, loss=4.89]
Epoch[208/3000]: 100%|████████████████| 9/9 [00:00<00:00, 560.19it/s, loss=4.73]
Epoch[209/3000]: 100%|████████████████| 9/9 [00:00<00:00, 671.15it/s, loss=5.17]
Epoch[210/3000]: 100%|████████████████| 9/9 [00:00<00:00, 698.38it/s, loss=3.82]
Epoch[211/3000]: 100%|████████████████| 9/9 [00:00<00:00, 600.81it/s, loss=5.02]Epoch [211/3000]: Train loss: 4.9311, Valid loss: 4.4578Epoch[212/3000]: 100%|████████████████| 9/9 [00:00<00:00, 557.35it/s, loss=3.39]
Epoch[213/3000]: 100%|████████████████| 9/9 [00:00<00:00, 599.40it/s, loss=4.82]
Epoch[214/3000]: 100%|████████████████| 9/9 [00:00<00:00, 607.91it/s, loss=4.23]
Epoch[215/3000]: 100%|████████████████| 9/9 [00:00<00:00, 662.15it/s, loss=4.06]
Epoch[216/3000]: 100%|████████████████| 9/9 [00:00<00:00, 589.08it/s, loss=5.24]Epoch [216/3000]: Train loss: 4.1603, Valid loss: 3.6981Epoch[217/3000]: 100%|████████████████| 9/9 [00:00<00:00, 587.61it/s, loss=3.21]
Epoch[218/3000]: 100%|███████████████████| 9/9 [00:00<00:00, 586.73it/s, loss=5]
Epoch[219/3000]: 100%|████████████████| 9/9 [00:00<00:00, 634.88it/s, loss=3.73]
Epoch[220/3000]: 100%|████████████████| 9/9 [00:00<00:00, 632.78it/s, loss=5.39]
Epoch[221/3000]: 100%|████████████████| 9/9 [00:00<00:00, 669.65it/s, loss=2.89]Epoch [221/3000]: Train loss: 3.8264, Valid loss: 3.6953Epoch[222/3000]: 100%|████████████████| 9/9 [00:00<00:00, 617.51it/s, loss=3.63]
Epoch[223/3000]: 100%|████████████████| 9/9 [00:00<00:00, 598.01it/s, loss=5.46]
Epoch[224/3000]: 100%|████████████████| 9/9 [00:00<00:00, 595.58it/s, loss=3.06]
Epoch[225/3000]: 100%|████████████████| 9/9 [00:00<00:00, 586.21it/s, loss=3.44]
Epoch[226/3000]: 100%|████████████████| 9/9 [00:00<00:00, 653.88it/s, loss=4.46]Epoch [226/3000]: Train loss: 4.2473, Valid loss: 5.3381Epoch[227/3000]: 100%|████████████████| 9/9 [00:00<00:00, 604.26it/s, loss=4.67]
Epoch[228/3000]: 100%|████████████████| 9/9 [00:00<00:00, 607.61it/s, loss=4.17]
Epoch[229/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 567.21it/s, loss=3.4]
Epoch[230/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 577.66it/s, loss=5.3]
Epoch[231/3000]: 100%|████████████████| 9/9 [00:00<00:00, 605.77it/s, loss=3.68]Epoch [231/3000]: Train loss: 4.4868, Valid loss: 5.0460Epoch[232/3000]: 100%|████████████████| 9/9 [00:00<00:00, 655.29it/s, loss=3.51]
Epoch[233/3000]: 100%|████████████████| 9/9 [00:00<00:00, 594.85it/s, loss=5.81]
Epoch[234/3000]: 100%|████████████████| 9/9 [00:00<00:00, 635.99it/s, loss=3.99]
Epoch[235/3000]: 100%|████████████████| 9/9 [00:00<00:00, 586.51it/s, loss=3.07]
Epoch[236/3000]: 100%|████████████████| 9/9 [00:00<00:00, 610.21it/s, loss=3.83]Epoch [236/3000]: Train loss: 4.1959, Valid loss: 3.8940Epoch[237/3000]: 100%|████████████████| 9/9 [00:00<00:00, 635.92it/s, loss=3.53]
Epoch[238/3000]: 100%|████████████████| 9/9 [00:00<00:00, 609.63it/s, loss=3.22]
Epoch[239/3000]: 100%|████████████████| 9/9 [00:00<00:00, 584.19it/s, loss=4.17]
Epoch[240/3000]: 100%|████████████████| 9/9 [00:00<00:00, 588.16it/s, loss=3.42]
Epoch[241/3000]: 100%|████████████████| 9/9 [00:00<00:00, 607.28it/s, loss=4.53]Epoch [241/3000]: Train loss: 3.8396, Valid loss: 4.1964Epoch[242/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 580.95it/s, loss=4.8]
Epoch[243/3000]: 100%|████████████████| 9/9 [00:00<00:00, 657.55it/s, loss=4.04]
Epoch[244/3000]: 100%|████████████████| 9/9 [00:00<00:00, 603.47it/s, loss=3.65]
Epoch[245/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 618.18it/s, loss=4.1]
Epoch[246/3000]: 100%|████████████████| 9/9 [00:00<00:00, 587.69it/s, loss=3.45]Epoch [246/3000]: Train loss: 3.5246, Valid loss: 3.6940Epoch[247/3000]: 100%|████████████████| 9/9 [00:00<00:00, 602.01it/s, loss=3.12]
Epoch[248/3000]: 100%|████████████████| 9/9 [00:00<00:00, 394.49it/s, loss=6.11]
Epoch[249/3000]: 100%|████████████████| 9/9 [00:00<00:00, 598.76it/s, loss=4.95]
Epoch[250/3000]: 100%|████████████████| 9/9 [00:00<00:00, 590.86it/s, loss=2.76]
Epoch[251/3000]: 100%|████████████████| 9/9 [00:00<00:00, 596.99it/s, loss=2.11]Epoch [251/3000]: Train loss: 3.4091, Valid loss: 3.5728Epoch[252/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 616.09it/s, loss=3.3]
Epoch[253/3000]: 100%|████████████████| 9/9 [00:00<00:00, 652.36it/s, loss=3.44]
Epoch[254/3000]: 100%|████████████████| 9/9 [00:00<00:00, 649.92it/s, loss=3.66]
Epoch[255/3000]: 100%|████████████████| 9/9 [00:00<00:00, 563.80it/s, loss=3.25]
Epoch[256/3000]: 100%|████████████████| 9/9 [00:00<00:00, 569.59it/s, loss=2.87]Epoch [256/3000]: Train loss: 4.1013, Valid loss: 4.8510Epoch[257/3000]: 100%|████████████████| 9/9 [00:00<00:00, 587.70it/s, loss=7.74]
Epoch[258/3000]: 100%|████████████████| 9/9 [00:00<00:00, 578.29it/s, loss=4.94]
Epoch[259/3000]: 100%|████████████████| 9/9 [00:00<00:00, 658.03it/s, loss=2.92]
Epoch[260/3000]: 100%|████████████████| 9/9 [00:00<00:00, 616.14it/s, loss=4.18]
Epoch[261/3000]: 100%|████████████████| 9/9 [00:00<00:00, 593.17it/s, loss=3.64]Epoch [261/3000]: Train loss: 4.7042, Valid loss: 6.1820Epoch[262/3000]: 100%|████████████████| 9/9 [00:00<00:00, 563.34it/s, loss=3.46]
Epoch[263/3000]: 100%|████████████████| 9/9 [00:00<00:00, 589.15it/s, loss=3.93]
Epoch[264/3000]: 100%|████████████████| 9/9 [00:00<00:00, 595.52it/s, loss=5.65]
Epoch[265/3000]: 100%|████████████████| 9/9 [00:00<00:00, 594.41it/s, loss=4.48]
Epoch[266/3000]: 100%|████████████████| 9/9 [00:00<00:00, 594.07it/s, loss=4.43]Epoch [266/3000]: Train loss: 3.4663, Valid loss: 3.2556Epoch[267/3000]: 100%|████████████████| 9/9 [00:00<00:00, 594.88it/s, loss=2.59]
Epoch[268/3000]: 100%|████████████████| 9/9 [00:00<00:00, 683.21it/s, loss=1.86]
Epoch[269/3000]: 100%|████████████████| 9/9 [00:00<00:00, 651.80it/s, loss=3.78]
Epoch[270/3000]: 100%|████████████████| 9/9 [00:00<00:00, 565.01it/s, loss=3.87]
Epoch[271/3000]: 100%|████████████████| 9/9 [00:00<00:00, 604.97it/s, loss=3.54]Epoch [271/3000]: Train loss: 3.3241, Valid loss: 3.2048Epoch[272/3000]: 100%|████████████████| 9/9 [00:00<00:00, 607.98it/s, loss=3.83]
Epoch[273/3000]: 100%|████████████████| 9/9 [00:00<00:00, 651.37it/s, loss=2.75]
Epoch[274/3000]: 100%|████████████████| 9/9 [00:00<00:00, 651.81it/s, loss=3.49]
Epoch[275/3000]: 100%|████████████████| 9/9 [00:00<00:00, 579.19it/s, loss=3.08]
Epoch[276/3000]: 100%|████████████████| 9/9 [00:00<00:00, 677.67it/s, loss=3.34]Epoch [276/3000]: Train loss: 3.2115, Valid loss: 3.7040Epoch[277/3000]: 100%|████████████████| 9/9 [00:00<00:00, 576.53it/s, loss=2.92]
Epoch[278/3000]: 100%|████████████████| 9/9 [00:00<00:00, 600.12it/s, loss=4.01]
Epoch[279/3000]: 100%|████████████████| 9/9 [00:00<00:00, 583.06it/s, loss=4.15]
Epoch[280/3000]: 100%|████████████████| 9/9 [00:00<00:00, 579.56it/s, loss=3.55]
Epoch[281/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 666.08it/s, loss=4.7]Epoch [281/3000]: Train loss: 4.3813, Valid loss: 5.8173Epoch[282/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 631.99it/s, loss=4.7]
Epoch[283/3000]: 100%|████████████████| 9/9 [00:00<00:00, 593.44it/s, loss=2.87]
Epoch[284/3000]: 100%|████████████████| 9/9 [00:00<00:00, 614.87it/s, loss=6.79]
Epoch[285/3000]: 100%|████████████████| 9/9 [00:00<00:00, 565.08it/s, loss=3.93]
Epoch[286/3000]: 100%|████████████████| 9/9 [00:00<00:00, 584.07it/s, loss=2.82]Epoch [286/3000]: Train loss: 3.1314, Valid loss: 2.8088Epoch[287/3000]: 100%|████████████████| 9/9 [00:00<00:00, 648.88it/s, loss=2.95]
Epoch[288/3000]: 100%|████████████████| 9/9 [00:00<00:00, 622.75it/s, loss=3.47]
Epoch[289/3000]: 100%|████████████████| 9/9 [00:00<00:00, 598.01it/s, loss=4.26]
Epoch[290/3000]: 100%|████████████████| 9/9 [00:00<00:00, 625.53it/s, loss=3.21]
Epoch[291/3000]: 100%|████████████████| 9/9 [00:00<00:00, 604.47it/s, loss=2.17]Epoch [291/3000]: Train loss: 3.2334, Valid loss: 2.9025Epoch[292/3000]: 100%|████████████████| 9/9 [00:00<00:00, 637.09it/s, loss=3.61]
Epoch[293/3000]: 100%|████████████████| 9/9 [00:00<00:00, 646.08it/s, loss=3.44]
Epoch[294/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 586.32it/s, loss=3.1]
Epoch[295/3000]: 100%|████████████████| 9/9 [00:00<00:00, 573.31it/s, loss=4.25]
Epoch[296/3000]: 100%|████████████████| 9/9 [00:00<00:00, 587.20it/s, loss=3.19]Epoch [296/3000]: Train loss: 3.5325, Valid loss: 5.3416Epoch[297/3000]: 100%|████████████████| 9/9 [00:00<00:00, 581.05it/s, loss=3.52]
Epoch[298/3000]: 100%|████████████████| 9/9 [00:00<00:00, 657.65it/s, loss=3.34]
Epoch[299/3000]: 100%|████████████████| 9/9 [00:00<00:00, 612.63it/s, loss=5.07]
Epoch[300/3000]: 100%|████████████████| 9/9 [00:00<00:00, 582.08it/s, loss=6.18]
Epoch[301/3000]: 100%|████████████████| 9/9 [00:00<00:00, 608.11it/s, loss=3.35]Epoch [301/3000]: Train loss: 4.0237, Valid loss: 3.0141Epoch[302/3000]: 100%|████████████████| 9/9 [00:00<00:00, 564.34it/s, loss=4.21]
Epoch[303/3000]: 100%|████████████████| 9/9 [00:00<00:00, 610.58it/s, loss=3.91]
Epoch[304/3000]: 100%|████████████████| 9/9 [00:00<00:00, 656.80it/s, loss=3.91]
Epoch[305/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 585.71it/s, loss=4.5]
Epoch[306/3000]: 100%|████████████████| 9/9 [00:00<00:00, 598.87it/s, loss=3.75]Epoch [306/3000]: Train loss: 3.1473, Valid loss: 3.2723Epoch[307/3000]: 100%|████████████████| 9/9 [00:00<00:00, 570.89it/s, loss=2.54]
Epoch[308/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 570.85it/s, loss=2.5]
Epoch[309/3000]: 100%|████████████████| 9/9 [00:00<00:00, 634.34it/s, loss=2.18]
Epoch[310/3000]: 100%|████████████████| 9/9 [00:00<00:00, 585.25it/s, loss=2.96]
Epoch[311/3000]: 100%|████████████████| 9/9 [00:00<00:00, 600.77it/s, loss=3.07]Epoch [311/3000]: Train loss: 3.1179, Valid loss: 6.8008Epoch[312/3000]: 100%|████████████████| 9/9 [00:00<00:00, 600.12it/s, loss=3.08]
Epoch[313/3000]: 100%|████████████████| 9/9 [00:00<00:00, 364.12it/s, loss=4.55]
Epoch[314/3000]: 100%|████████████████| 9/9 [00:00<00:00, 647.99it/s, loss=5.69]
Epoch[315/3000]: 100%|████████████████| 9/9 [00:00<00:00, 616.02it/s, loss=2.61]
Epoch[316/3000]: 100%|████████████████| 9/9 [00:00<00:00, 603.99it/s, loss=2.67]Epoch [316/3000]: Train loss: 3.2263, Valid loss: 3.8949Epoch[317/3000]: 100%|████████████████| 9/9 [00:00<00:00, 583.78it/s, loss=2.39]
Epoch[318/3000]: 100%|████████████████| 9/9 [00:00<00:00, 584.68it/s, loss=3.49]
Epoch[319/3000]: 100%|████████████████| 9/9 [00:00<00:00, 599.53it/s, loss=2.93]
Epoch[320/3000]: 100%|████████████████| 9/9 [00:00<00:00, 689.54it/s, loss=2.94]
Epoch[321/3000]: 100%|████████████████| 9/9 [00:00<00:00, 617.15it/s, loss=5.58]Epoch [321/3000]: Train loss: 3.4596, Valid loss: 2.7697Epoch[322/3000]: 100%|████████████████| 9/9 [00:00<00:00, 594.65it/s, loss=3.08]
Epoch[323/3000]: 100%|████████████████| 9/9 [00:00<00:00, 602.99it/s, loss=5.74]
Epoch[324/3000]: 100%|████████████████| 9/9 [00:00<00:00, 601.40it/s, loss=3.67]
Epoch[325/3000]: 100%|████████████████| 9/9 [00:00<00:00, 646.75it/s, loss=4.57]
Epoch[326/3000]: 100%|████████████████| 9/9 [00:00<00:00, 645.21it/s, loss=3.13]Epoch [326/3000]: Train loss: 4.0077, Valid loss: 6.3895Epoch[327/3000]: 100%|████████████████| 9/9 [00:00<00:00, 551.44it/s, loss=7.44]
Epoch[328/3000]: 100%|████████████████| 9/9 [00:00<00:00, 574.71it/s, loss=3.98]
Epoch[329/3000]: 100%|████████████████| 9/9 [00:00<00:00, 599.87it/s, loss=4.68]
Epoch[330/3000]: 100%|████████████████| 9/9 [00:00<00:00, 593.12it/s, loss=7.25]
Epoch[331/3000]:   0%|                         | 0/9 [00:00<?, ?it/s, loss=3.53]IOPub message rate exceeded.
The notebook server will temporarily stop sending output
to the client in order to avoid crashing it.
To change this limit, set the config variable
`--NotebookApp.iopub_msg_rate_limit`.Current values:
NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)
NotebookApp.rate_limit_window=3.0 (secs)Epoch[384/3000]: 100%|████████████████| 9/9 [00:00<00:00, 731.89it/s, loss=3.67]
Epoch[385/3000]: 100%|████████████████| 9/9 [00:00<00:00, 644.18it/s, loss=6.88]
Epoch[386/3000]: 100%|████████████████| 9/9 [00:00<00:00, 568.83it/s, loss=6.96]Epoch [386/3000]: Train loss: 4.8424, Valid loss: 3.2886Epoch[387/3000]: 100%|████████████████| 9/9 [00:00<00:00, 584.42it/s, loss=2.72]
Epoch[388/3000]: 100%|████████████████| 9/9 [00:00<00:00, 588.43it/s, loss=3.43]
Epoch[389/3000]: 100%|████████████████| 9/9 [00:00<00:00, 587.96it/s, loss=3.59]
Epoch[390/3000]: 100%|████████████████| 9/9 [00:00<00:00, 648.70it/s, loss=2.37]
Epoch[391/3000]: 100%|████████████████| 9/9 [00:00<00:00, 602.69it/s, loss=2.73]Epoch [391/3000]: Train loss: 2.6971, Valid loss: 3.0189Epoch[392/3000]: 100%|████████████████| 9/9 [00:00<00:00, 586.16it/s, loss=2.57]
Epoch[393/3000]: 100%|████████████████| 9/9 [00:00<00:00, 564.61it/s, loss=3.42]
Epoch[394/3000]: 100%|████████████████| 9/9 [00:00<00:00, 599.88it/s, loss=3.34]
Epoch[395/3000]: 100%|████████████████| 9/9 [00:00<00:00, 601.24it/s, loss=2.56]
Epoch[396/3000]: 100%|████████████████| 9/9 [00:00<00:00, 658.27it/s, loss=2.22]Epoch [396/3000]: Train loss: 2.8688, Valid loss: 3.0228Epoch[397/3000]: 100%|████████████████| 9/9 [00:00<00:00, 602.05it/s, loss=2.63]
Epoch[398/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 586.01it/s, loss=3.7]
Epoch[399/3000]: 100%|████████████████| 9/9 [00:00<00:00, 614.29it/s, loss=2.88]
Epoch[400/3000]: 100%|████████████████| 9/9 [00:00<00:00, 596.15it/s, loss=3.96]
Epoch[401/3000]: 100%|████████████████| 9/9 [00:00<00:00, 633.89it/s, loss=3.47]Epoch [401/3000]: Train loss: 3.8574, Valid loss: 3.3016Epoch[402/3000]: 100%|████████████████| 9/9 [00:00<00:00, 631.85it/s, loss=3.24]
Epoch[403/3000]: 100%|████████████████| 9/9 [00:00<00:00, 577.67it/s, loss=3.28]
Epoch[404/3000]: 100%|████████████████| 9/9 [00:00<00:00, 595.87it/s, loss=2.67]
Epoch[405/3000]: 100%|████████████████| 9/9 [00:00<00:00, 591.64it/s, loss=3.81]
Epoch[406/3000]: 100%|████████████████| 9/9 [00:00<00:00, 583.51it/s, loss=3.23]Epoch [406/3000]: Train loss: 2.9430, Valid loss: 2.7286Epoch[407/3000]: 100%|████████████████| 9/9 [00:00<00:00, 658.42it/s, loss=2.52]
Epoch[408/3000]: 100%|████████████████| 9/9 [00:00<00:00, 629.28it/s, loss=3.45]
Epoch[409/3000]: 100%|████████████████| 9/9 [00:00<00:00, 609.14it/s, loss=2.93]
Epoch[410/3000]: 100%|████████████████| 9/9 [00:00<00:00, 569.54it/s, loss=3.12]
Epoch[411/3000]: 100%|████████████████| 9/9 [00:00<00:00, 596.30it/s, loss=2.67]Epoch [411/3000]: Train loss: 2.9853, Valid loss: 4.9298Epoch[412/3000]: 100%|████████████████| 9/9 [00:00<00:00, 637.09it/s, loss=3.11]
Epoch[413/3000]: 100%|████████████████| 9/9 [00:00<00:00, 655.83it/s, loss=2.29]
Epoch[414/3000]: 100%|████████████████| 9/9 [00:00<00:00, 546.35it/s, loss=1.97]
Epoch[415/3000]: 100%|████████████████| 9/9 [00:00<00:00, 568.65it/s, loss=3.02]
Epoch[416/3000]: 100%|████████████████| 9/9 [00:00<00:00, 565.73it/s, loss=3.02]Epoch [416/3000]: Train loss: 3.2579, Valid loss: 4.9004Epoch[417/3000]: 100%|████████████████| 9/9 [00:00<00:00, 561.69it/s, loss=2.58]
Epoch[418/3000]: 100%|████████████████| 9/9 [00:00<00:00, 649.72it/s, loss=2.66]
Epoch[419/3000]: 100%|████████████████| 9/9 [00:00<00:00, 604.12it/s, loss=1.93]
Epoch[420/3000]: 100%|████████████████| 9/9 [00:00<00:00, 591.43it/s, loss=1.79]
Epoch[421/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 586.43it/s, loss=2.3]Epoch [421/3000]: Train loss: 2.6070, Valid loss: 2.6638Epoch[422/3000]: 100%|████████████████| 9/9 [00:00<00:00, 541.15it/s, loss=3.18]
Epoch[423/3000]: 100%|████████████████| 9/9 [00:00<00:00, 642.77it/s, loss=1.92]
Epoch[424/3000]: 100%|████████████████| 9/9 [00:00<00:00, 622.41it/s, loss=3.04]
Epoch[425/3000]: 100%|████████████████| 9/9 [00:00<00:00, 571.03it/s, loss=8.09]
Epoch[426/3000]: 100%|████████████████| 9/9 [00:00<00:00, 563.57it/s, loss=3.74]Epoch [426/3000]: Train loss: 3.5805, Valid loss: 2.9375Epoch[427/3000]: 100%|████████████████| 9/9 [00:00<00:00, 575.16it/s, loss=3.26]
Epoch[428/3000]: 100%|████████████████| 9/9 [00:00<00:00, 589.62it/s, loss=2.64]
Epoch[429/3000]: 100%|████████████████| 9/9 [00:00<00:00, 417.71it/s, loss=1.87]
Epoch[430/3000]: 100%|████████████████| 9/9 [00:00<00:00, 529.79it/s, loss=2.92]
Epoch[431/3000]: 100%|████████████████| 9/9 [00:00<00:00, 593.08it/s, loss=2.23]Epoch [431/3000]: Train loss: 4.4588, Valid loss: 3.8523Epoch[432/3000]: 100%|███████████████████| 9/9 [00:00<00:00, 559.98it/s, loss=2]
Epoch[433/3000]: 100%|████████████████| 9/9 [00:00<00:00, 605.63it/s, loss=3.18]
Epoch[434/3000]: 100%|████████████████| 9/9 [00:00<00:00, 623.09it/s, loss=3.71]
Epoch[435/3000]: 100%|████████████████| 9/9 [00:00<00:00, 550.18it/s, loss=2.58]
Epoch[436/3000]: 100%|████████████████| 9/9 [00:00<00:00, 558.59it/s, loss=2.75]Epoch [436/3000]: Train loss: 2.7912, Valid loss: 2.1384Epoch[437/3000]: 100%|████████████████| 9/9 [00:00<00:00, 571.72it/s, loss=2.29]
Epoch[438/3000]: 100%|████████████████| 9/9 [00:00<00:00, 579.26it/s, loss=3.15]
Epoch[439/3000]: 100%|████████████████| 9/9 [00:00<00:00, 655.92it/s, loss=2.91]
Epoch[440/3000]: 100%|████████████████| 9/9 [00:00<00:00, 602.81it/s, loss=2.47]
Epoch[441/3000]: 100%|████████████████| 9/9 [00:00<00:00, 594.66it/s, loss=2.29]Epoch [441/3000]: Train loss: 2.6035, Valid loss: 3.4529Epoch[442/3000]: 100%|████████████████| 9/9 [00:00<00:00, 536.54it/s, loss=3.36]
Epoch[443/3000]: 100%|████████████████| 9/9 [00:00<00:00, 571.68it/s, loss=3.14]
Epoch[444/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 635.86it/s, loss=2.5]
Epoch[445/3000]: 100%|████████████████| 9/9 [00:00<00:00, 112.50it/s, loss=2.57]
Epoch[446/3000]: 100%|████████████████| 9/9 [00:00<00:00, 617.06it/s, loss=2.42]Epoch [446/3000]: Train loss: 2.4622, Valid loss: 2.3423Epoch[447/3000]: 100%|████████████████| 9/9 [00:00<00:00, 565.62it/s, loss=1.73]
Epoch[448/3000]: 100%|████████████████| 9/9 [00:00<00:00, 573.58it/s, loss=3.94]
Epoch[449/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 581.02it/s, loss=2.5]
Epoch[450/3000]: 100%|████████████████| 9/9 [00:00<00:00, 581.96it/s, loss=5.96]
Epoch[451/3000]: 100%|████████████████| 9/9 [00:00<00:00, 585.30it/s, loss=8.95]Epoch [451/3000]: Train loss: 7.6025, Valid loss: 5.9773Epoch[452/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 636.85it/s, loss=3.4]
Epoch[453/3000]: 100%|████████████████| 9/9 [00:00<00:00, 611.12it/s, loss=2.94]
Epoch[454/3000]: 100%|████████████████| 9/9 [00:00<00:00, 569.40it/s, loss=3.01]
Epoch[455/3000]: 100%|████████████████| 9/9 [00:00<00:00, 594.03it/s, loss=2.91]
Epoch[456/3000]: 100%|████████████████| 9/9 [00:00<00:00, 552.58it/s, loss=1.98]Epoch [456/3000]: Train loss: 2.5691, Valid loss: 2.5709Epoch[457/3000]: 100%|████████████████| 9/9 [00:00<00:00, 618.68it/s, loss=2.25]
Epoch[458/3000]: 100%|████████████████| 9/9 [00:00<00:00, 593.36it/s, loss=1.98]
Epoch[459/3000]: 100%|████████████████| 9/9 [00:00<00:00, 570.42it/s, loss=2.54]
Epoch[460/3000]: 100%|████████████████| 9/9 [00:00<00:00, 574.57it/s, loss=2.46]
Epoch[461/3000]: 100%|████████████████| 9/9 [00:00<00:00, 587.20it/s, loss=2.38]Epoch [461/3000]: Train loss: 3.3483, Valid loss: 2.9749Epoch[462/3000]: 100%|████████████████| 9/9 [00:00<00:00, 590.17it/s, loss=2.19]
Epoch[463/3000]: 100%|████████████████| 9/9 [00:00<00:00, 634.56it/s, loss=2.99]
Epoch[464/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 525.31it/s, loss=2.8]
Epoch[465/3000]: 100%|████████████████| 9/9 [00:00<00:00, 572.96it/s, loss=3.67]
Epoch[466/3000]: 100%|████████████████| 9/9 [00:00<00:00, 571.28it/s, loss=2.32]Epoch [466/3000]: Train loss: 2.5208, Valid loss: 2.6041Epoch[467/3000]: 100%|████████████████| 9/9 [00:00<00:00, 562.15it/s, loss=2.25]
Epoch[468/3000]: 100%|████████████████| 9/9 [00:00<00:00, 663.33it/s, loss=6.99]
Epoch[469/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 617.24it/s, loss=2.7]
Epoch[470/3000]: 100%|████████████████| 9/9 [00:00<00:00, 568.33it/s, loss=2.73]
Epoch[471/3000]: 100%|████████████████| 9/9 [00:00<00:00, 606.92it/s, loss=4.46]Epoch [471/3000]: Train loss: 3.5814, Valid loss: 2.7042Epoch[472/3000]: 100%|████████████████| 9/9 [00:00<00:00, 535.21it/s, loss=2.03]
Epoch[473/3000]: 100%|████████████████| 9/9 [00:00<00:00, 639.02it/s, loss=2.12]
Epoch[474/3000]: 100%|████████████████| 9/9 [00:00<00:00, 660.12it/s, loss=2.42]
Epoch[475/3000]: 100%|████████████████| 9/9 [00:00<00:00, 563.41it/s, loss=2.59]
Epoch[476/3000]: 100%|████████████████| 9/9 [00:00<00:00, 585.11it/s, loss=3.34]Epoch [476/3000]: Train loss: 2.9076, Valid loss: 2.6528Epoch[477/3000]: 100%|████████████████| 9/9 [00:00<00:00, 567.57it/s, loss=2.93]
Epoch[478/3000]: 100%|████████████████| 9/9 [00:00<00:00, 581.92it/s, loss=2.28]
Epoch[479/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 656.17it/s, loss=3.5]
Epoch[480/3000]: 100%|████████████████| 9/9 [00:00<00:00, 617.93it/s, loss=2.56]
Epoch[481/3000]: 100%|████████████████| 9/9 [00:00<00:00, 575.85it/s, loss=2.51]Epoch [481/3000]: Train loss: 2.9367, Valid loss: 2.9828Epoch[482/3000]: 100%|████████████████| 9/9 [00:00<00:00, 580.56it/s, loss=2.33]
Epoch[483/3000]: 100%|████████████████| 9/9 [00:00<00:00, 607.94it/s, loss=3.18]
Epoch[484/3000]: 100%|████████████████| 9/9 [00:00<00:00, 622.06it/s, loss=5.38]
Epoch[485/3000]: 100%|████████████████| 9/9 [00:00<00:00, 660.12it/s, loss=8.34]
Epoch[486/3000]: 100%|████████████████| 9/9 [00:00<00:00, 585.29it/s, loss=3.75]Epoch [486/3000]: Train loss: 4.1245, Valid loss: 3.9895Epoch[487/3000]: 100%|████████████████| 9/9 [00:00<00:00, 609.95it/s, loss=3.15]
Epoch[488/3000]: 100%|████████████████| 9/9 [00:00<00:00, 581.73it/s, loss=3.46]
Epoch[489/3000]: 100%|████████████████| 9/9 [00:00<00:00, 475.39it/s, loss=4.76]
Epoch[490/3000]: 100%|████████████████| 9/9 [00:00<00:00, 659.72it/s, loss=2.72]
Epoch[491/3000]: 100%|████████████████| 9/9 [00:00<00:00, 596.78it/s, loss=2.57]Epoch [491/3000]: Train loss: 2.5128, Valid loss: 3.7874Epoch[492/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 616.18it/s, loss=2.9]
Epoch[493/3000]: 100%|████████████████| 9/9 [00:00<00:00, 578.62it/s, loss=2.18]
Epoch[494/3000]: 100%|████████████████| 9/9 [00:00<00:00, 576.84it/s, loss=2.51]
Epoch[495/3000]: 100%|████████████████| 9/9 [00:00<00:00, 642.03it/s, loss=1.91]
Epoch[496/3000]: 100%|████████████████| 9/9 [00:00<00:00, 619.42it/s, loss=2.09]Epoch [496/3000]: Train loss: 2.4880, Valid loss: 2.6436Epoch[497/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 559.88it/s, loss=2.7]
Epoch[498/3000]: 100%|████████████████| 9/9 [00:00<00:00, 585.81it/s, loss=1.99]
Epoch[499/3000]: 100%|████████████████| 9/9 [00:00<00:00, 603.54it/s, loss=2.03]
Epoch[500/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 617.91it/s, loss=2.2]
Epoch[501/3000]: 100%|████████████████| 9/9 [00:00<00:00, 642.17it/s, loss=3.11]Epoch [501/3000]: Train loss: 2.6829, Valid loss: 2.4430Epoch[502/3000]: 100%|████████████████| 9/9 [00:00<00:00, 571.02it/s, loss=3.26]
Epoch[503/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 588.96it/s, loss=2.2]
Epoch[504/3000]: 100%|████████████████| 9/9 [00:00<00:00, 576.37it/s, loss=2.61]
Epoch[505/3000]: 100%|████████████████| 9/9 [00:00<00:00, 567.32it/s, loss=2.52]
Epoch[506/3000]: 100%|████████████████| 9/9 [00:00<00:00, 626.78it/s, loss=2.28]Epoch [506/3000]: Train loss: 3.5074, Valid loss: 4.1759Epoch[507/3000]: 100%|████████████████| 9/9 [00:00<00:00, 608.36it/s, loss=2.29]
Epoch[508/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 587.25it/s, loss=2.3]
Epoch[509/3000]: 100%|████████████████| 9/9 [00:00<00:00, 588.51it/s, loss=2.34]
Epoch[510/3000]: 100%|████████████████| 9/9 [00:00<00:00, 590.83it/s, loss=2.18]
Epoch[511/3000]: 100%|████████████████| 9/9 [00:00<00:00, 626.39it/s, loss=3.99]Epoch [511/3000]: Train loss: 3.3137, Valid loss: 2.6259Epoch[512/3000]: 100%|████████████████| 9/9 [00:00<00:00, 638.25it/s, loss=5.13]
Epoch[513/3000]: 100%|████████████████| 9/9 [00:00<00:00, 586.70it/s, loss=2.39]
Epoch[514/3000]: 100%|████████████████| 9/9 [00:00<00:00, 595.00it/s, loss=3.76]
Epoch[515/3000]: 100%|████████████████| 9/9 [00:00<00:00, 575.56it/s, loss=2.89]
Epoch[516/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 588.89it/s, loss=2.3]Epoch [516/3000]: Train loss: 2.8441, Valid loss: 4.7241Epoch[517/3000]: 100%|████████████████| 9/9 [00:00<00:00, 636.49it/s, loss=2.26]
Epoch[518/3000]: 100%|████████████████| 9/9 [00:00<00:00, 640.56it/s, loss=1.83]
Epoch[519/3000]: 100%|████████████████| 9/9 [00:00<00:00, 582.49it/s, loss=2.13]
Epoch[520/3000]: 100%|████████████████| 9/9 [00:00<00:00, 590.55it/s, loss=2.62]
Epoch[521/3000]: 100%|████████████████| 9/9 [00:00<00:00, 585.98it/s, loss=2.32]Epoch [521/3000]: Train loss: 3.1263, Valid loss: 2.7250Epoch[522/3000]: 100%|████████████████| 9/9 [00:00<00:00, 596.58it/s, loss=2.86]
Epoch[523/3000]: 100%|████████████████| 9/9 [00:00<00:00, 653.87it/s, loss=2.82]
Epoch[524/3000]: 100%|████████████████| 9/9 [00:00<00:00, 561.14it/s, loss=2.12]
Epoch[525/3000]:   0%|                          | 0/9 [00:00<?, ?it/s, loss=3.9]IOPub message rate exceeded.
The notebook server will temporarily stop sending output
to the client in order to avoid crashing it.
To change this limit, set the config variable
`--NotebookApp.iopub_msg_rate_limit`.Current values:
NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)
NotebookApp.rate_limit_window=3.0 (secs)Epoch[574/3000]: 100%|████████████████| 9/9 [00:00<00:00, 664.06it/s, loss=1.58]
Epoch[575/3000]: 100%|████████████████| 9/9 [00:00<00:00, 658.77it/s, loss=3.21]
Epoch[576/3000]: 100%|████████████████| 9/9 [00:00<00:00, 502.26it/s, loss=7.64]Epoch [576/3000]: Train loss: 4.3182, Valid loss: 4.5022Epoch[577/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 523.66it/s, loss=3.6]
Epoch[578/3000]: 100%|████████████████| 9/9 [00:00<00:00, 552.27it/s, loss=1.73]
Epoch[579/3000]: 100%|████████████████| 9/9 [00:00<00:00, 562.68it/s, loss=4.39]
Epoch[580/3000]: 100%|████████████████| 9/9 [00:00<00:00, 637.66it/s, loss=2.79]
Epoch[581/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 555.90it/s, loss=2.1]Epoch [581/3000]: Train loss: 2.8006, Valid loss: 4.3405Epoch[582/3000]: 100%|████████████████| 9/9 [00:00<00:00, 526.56it/s, loss=2.38]
Epoch[583/3000]: 100%|████████████████| 9/9 [00:00<00:00, 556.80it/s, loss=2.14]
Epoch[584/3000]: 100%|████████████████| 9/9 [00:00<00:00, 538.90it/s, loss=3.92]
Epoch[585/3000]: 100%|████████████████| 9/9 [00:00<00:00, 606.26it/s, loss=3.57]
Epoch[586/3000]: 100%|████████████████| 9/9 [00:00<00:00, 578.29it/s, loss=2.29]Epoch [586/3000]: Train loss: 2.2924, Valid loss: 2.6480Epoch[587/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 549.49it/s, loss=2.9]
Epoch[588/3000]: 100%|████████████████| 9/9 [00:00<00:00, 591.48it/s, loss=2.23]
Epoch[589/3000]: 100%|████████████████| 9/9 [00:00<00:00, 567.51it/s, loss=2.08]
Epoch[590/3000]: 100%|████████████████| 9/9 [00:00<00:00, 600.85it/s, loss=2.05]
Epoch[591/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 613.33it/s, loss=2.5]Epoch [591/3000]: Train loss: 2.2474, Valid loss: 2.4743Epoch[592/3000]: 100%|████████████████| 9/9 [00:00<00:00, 528.60it/s, loss=2.16]
Epoch[593/3000]: 100%|████████████████| 9/9 [00:00<00:00, 564.97it/s, loss=2.05]
Epoch[594/3000]: 100%|████████████████| 9/9 [00:00<00:00, 529.73it/s, loss=1.78]
Epoch[595/3000]: 100%|████████████████| 9/9 [00:00<00:00, 578.19it/s, loss=2.31]
Epoch[596/3000]: 100%|████████████████| 9/9 [00:00<00:00, 388.94it/s, loss=3.07]Epoch [596/3000]: Train loss: 2.4106, Valid loss: 2.5501Epoch[597/3000]: 100%|████████████████| 9/9 [00:00<00:00, 539.70it/s, loss=2.71]
Epoch[598/3000]: 100%|████████████████| 9/9 [00:00<00:00, 537.78it/s, loss=1.78]
Epoch[599/3000]: 100%|████████████████| 9/9 [00:00<00:00, 548.55it/s, loss=1.91]
Epoch[600/3000]: 100%|████████████████| 9/9 [00:00<00:00, 598.09it/s, loss=2.27]
Epoch[601/3000]: 100%|████████████████| 9/9 [00:00<00:00, 554.39it/s, loss=2.22]Epoch [601/3000]: Train loss: 2.5903, Valid loss: 2.0986Epoch[602/3000]: 100%|████████████████| 9/9 [00:00<00:00, 521.82it/s, loss=3.26]
Epoch[603/3000]: 100%|████████████████| 9/9 [00:00<00:00, 571.84it/s, loss=2.91]
Epoch[604/3000]: 100%|████████████████| 9/9 [00:00<00:00, 541.13it/s, loss=2.72]
Epoch[605/3000]: 100%|████████████████| 9/9 [00:00<00:00, 608.98it/s, loss=2.09]
Epoch[606/3000]: 100%|████████████████| 9/9 [00:00<00:00, 626.92it/s, loss=2.42]Epoch [606/3000]: Train loss: 4.1149, Valid loss: 8.1701Epoch[607/3000]: 100%|████████████████| 9/9 [00:00<00:00, 538.73it/s, loss=4.99]
Epoch[608/3000]: 100%|████████████████| 9/9 [00:00<00:00, 559.35it/s, loss=3.04]
Epoch[609/3000]: 100%|████████████████| 9/9 [00:00<00:00, 572.63it/s, loss=2.25]
Epoch[610/3000]: 100%|████████████████| 9/9 [00:00<00:00, 561.80it/s, loss=4.08]
Epoch[611/3000]: 100%|████████████████| 9/9 [00:00<00:00, 624.05it/s, loss=7.32]Epoch [611/3000]: Train loss: 5.6649, Valid loss: 4.6380Epoch[612/3000]: 100%|████████████████| 9/9 [00:00<00:00, 514.67it/s, loss=2.23]
Epoch[613/3000]: 100%|████████████████| 9/9 [00:00<00:00, 558.18it/s, loss=3.18]
Epoch[614/3000]: 100%|████████████████| 9/9 [00:00<00:00, 114.84it/s, loss=2.58]
Epoch[615/3000]: 100%|████████████████| 9/9 [00:00<00:00, 548.64it/s, loss=2.53]
Epoch[616/3000]: 100%|████████████████| 9/9 [00:00<00:00, 544.90it/s, loss=2.52]Epoch [616/3000]: Train loss: 2.3287, Valid loss: 2.9083Epoch[617/3000]: 100%|████████████████| 9/9 [00:00<00:00, 560.05it/s, loss=3.65]
Epoch[618/3000]: 100%|████████████████| 9/9 [00:00<00:00, 641.25it/s, loss=2.46]
Epoch[619/3000]: 100%|████████████████| 9/9 [00:00<00:00, 609.07it/s, loss=2.82]
Epoch[620/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 540.54it/s, loss=2.3]
Epoch[621/3000]: 100%|████████████████| 9/9 [00:00<00:00, 558.24it/s, loss=4.14]Epoch [621/3000]: Train loss: 2.4373, Valid loss: 2.5114Epoch[622/3000]: 100%|████████████████| 9/9 [00:00<00:00, 536.10it/s, loss=2.96]
Epoch[623/3000]: 100%|████████████████| 9/9 [00:00<00:00, 620.44it/s, loss=3.13]
Epoch[624/3000]: 100%|████████████████| 9/9 [00:00<00:00, 592.78it/s, loss=3.45]
Epoch[625/3000]: 100%|████████████████| 9/9 [00:00<00:00, 571.25it/s, loss=2.96]
Epoch[626/3000]: 100%|████████████████| 9/9 [00:00<00:00, 593.42it/s, loss=2.18]Epoch [626/3000]: Train loss: 2.3340, Valid loss: 2.6088Epoch[627/3000]: 100%|████████████████| 9/9 [00:00<00:00, 574.46it/s, loss=2.34]
Epoch[628/3000]: 100%|████████████████| 9/9 [00:00<00:00, 557.44it/s, loss=1.94]
Epoch[629/3000]: 100%|████████████████| 9/9 [00:00<00:00, 636.22it/s, loss=2.53]
Epoch[630/3000]: 100%|████████████████| 9/9 [00:00<00:00, 551.81it/s, loss=2.67]
Epoch[631/3000]: 100%|████████████████| 9/9 [00:00<00:00, 556.93it/s, loss=2.23]Epoch [631/3000]: Train loss: 2.2390, Valid loss: 2.3074Epoch[632/3000]: 100%|████████████████| 9/9 [00:00<00:00, 536.86it/s, loss=2.56]
Epoch[633/3000]: 100%|████████████████| 9/9 [00:00<00:00, 573.21it/s, loss=2.32]
Epoch[634/3000]: 100%|████████████████| 9/9 [00:00<00:00, 655.96it/s, loss=1.87]
Epoch[635/3000]: 100%|████████████████| 9/9 [00:00<00:00, 586.63it/s, loss=1.92]
Epoch[636/3000]: 100%|████████████████| 9/9 [00:00<00:00, 519.87it/s, loss=2.75]Epoch [636/3000]: Train loss: 2.4584, Valid loss: 3.0224Epoch[637/3000]: 100%|████████████████| 9/9 [00:00<00:00, 534.34it/s, loss=2.74]
Epoch[638/3000]: 100%|████████████████| 9/9 [00:00<00:00, 564.51it/s, loss=2.98]
Epoch[639/3000]: 100%|████████████████| 9/9 [00:00<00:00, 622.62it/s, loss=2.61]
Epoch[640/3000]: 100%|████████████████| 9/9 [00:00<00:00, 584.94it/s, loss=2.11]
Epoch[641/3000]: 100%|████████████████| 9/9 [00:00<00:00, 544.69it/s, loss=2.86]Epoch [641/3000]: Train loss: 2.3943, Valid loss: 2.2415Epoch[642/3000]: 100%|████████████████| 9/9 [00:00<00:00, 555.66it/s, loss=2.35]
Epoch[643/3000]: 100%|████████████████| 9/9 [00:00<00:00, 566.53it/s, loss=2.73]
Epoch[644/3000]: 100%|████████████████| 9/9 [00:00<00:00, 592.96it/s, loss=2.05]
Epoch[645/3000]: 100%|████████████████| 9/9 [00:00<00:00, 619.02it/s, loss=2.24]
Epoch[646/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 548.67it/s, loss=2.3]Epoch [646/3000]: Train loss: 4.0327, Valid loss: 7.2953Epoch[647/3000]: 100%|████████████████| 9/9 [00:00<00:00, 565.46it/s, loss=2.08]
Epoch[648/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 577.14it/s, loss=3.2]
Epoch[649/3000]: 100%|████████████████| 9/9 [00:00<00:00, 579.63it/s, loss=2.12]
Epoch[650/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 644.51it/s, loss=2.4]
Epoch[651/3000]: 100%|████████████████| 9/9 [00:00<00:00, 576.18it/s, loss=4.39]Epoch [651/3000]: Train loss: 3.8813, Valid loss: 4.1052Epoch[652/3000]: 100%|███████████████████| 9/9 [00:00<00:00, 405.67it/s, loss=6]
Epoch[653/3000]: 100%|████████████████| 9/9 [00:00<00:00, 531.85it/s, loss=2.02]
Epoch[654/3000]: 100%|████████████████| 9/9 [00:00<00:00, 617.96it/s, loss=4.84]
Epoch[655/3000]: 100%|████████████████| 9/9 [00:00<00:00, 634.66it/s, loss=2.45]
Epoch[656/3000]: 100%|████████████████| 9/9 [00:00<00:00, 543.80it/s, loss=2.54]Epoch [656/3000]: Train loss: 2.6390, Valid loss: 3.0343Epoch[657/3000]: 100%|████████████████| 9/9 [00:00<00:00, 549.02it/s, loss=1.51]
Epoch[658/3000]: 100%|████████████████| 9/9 [00:00<00:00, 572.05it/s, loss=2.39]
Epoch[659/3000]: 100%|████████████████| 9/9 [00:00<00:00, 565.79it/s, loss=4.61]
Epoch[660/3000]: 100%|████████████████| 9/9 [00:00<00:00, 635.14it/s, loss=2.39]
Epoch[661/3000]: 100%|████████████████| 9/9 [00:00<00:00, 599.53it/s, loss=2.56]Epoch [661/3000]: Train loss: 2.4647, Valid loss: 3.9992Epoch[662/3000]: 100%|████████████████| 9/9 [00:00<00:00, 535.52it/s, loss=2.26]
Epoch[663/3000]: 100%|████████████████| 9/9 [00:00<00:00, 544.40it/s, loss=2.44]
Epoch[664/3000]: 100%|████████████████| 9/9 [00:00<00:00, 570.08it/s, loss=2.55]
Epoch[665/3000]: 100%|████████████████| 9/9 [00:00<00:00, 607.22it/s, loss=4.07]
Epoch[666/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 595.32it/s, loss=2.6]Epoch [666/3000]: Train loss: 2.5610, Valid loss: 2.5195Epoch[667/3000]: 100%|████████████████| 9/9 [00:00<00:00, 550.32it/s, loss=1.58]
Epoch[668/3000]: 100%|████████████████| 9/9 [00:00<00:00, 574.09it/s, loss=2.31]
Epoch[669/3000]: 100%|████████████████| 9/9 [00:00<00:00, 546.81it/s, loss=1.48]
Epoch[670/3000]: 100%|████████████████| 9/9 [00:00<00:00, 621.47it/s, loss=1.88]
Epoch[671/3000]: 100%|████████████████| 9/9 [00:00<00:00, 632.82it/s, loss=2.16]Epoch [671/3000]: Train loss: 2.1134, Valid loss: 3.4154Epoch[672/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 550.49it/s, loss=2.1]
Epoch[673/3000]: 100%|████████████████| 9/9 [00:00<00:00, 562.64it/s, loss=2.74]
Epoch[674/3000]: 100%|████████████████| 9/9 [00:00<00:00, 580.46it/s, loss=2.52]
Epoch[675/3000]: 100%|████████████████| 9/9 [00:00<00:00, 570.64it/s, loss=2.07]
Epoch[676/3000]: 100%|████████████████| 9/9 [00:00<00:00, 629.23it/s, loss=2.97]Epoch [676/3000]: Train loss: 2.6306, Valid loss: 2.3509Epoch[677/3000]: 100%|████████████████| 9/9 [00:00<00:00, 561.12it/s, loss=1.91]
Epoch[678/3000]: 100%|████████████████| 9/9 [00:00<00:00, 539.18it/s, loss=2.64]
Epoch[679/3000]: 100%|████████████████| 9/9 [00:00<00:00, 551.63it/s, loss=1.84]
Epoch[680/3000]: 100%|████████████████| 9/9 [00:00<00:00, 554.63it/s, loss=1.88]
Epoch[681/3000]: 100%|████████████████| 9/9 [00:00<00:00, 627.18it/s, loss=2.81]Epoch [681/3000]: Train loss: 3.9626, Valid loss: 3.2350Epoch[682/3000]: 100%|████████████████| 9/9 [00:00<00:00, 584.41it/s, loss=3.63]
Epoch[683/3000]: 100%|████████████████| 9/9 [00:00<00:00, 566.15it/s, loss=2.64]
Epoch[684/3000]: 100%|████████████████| 9/9 [00:00<00:00, 585.86it/s, loss=2.31]
Epoch[685/3000]: 100%|████████████████| 9/9 [00:00<00:00, 553.47it/s, loss=2.21]
Epoch[686/3000]: 100%|████████████████| 9/9 [00:00<00:00, 609.79it/s, loss=2.31]Epoch [686/3000]: Train loss: 2.1325, Valid loss: 1.9751Epoch[687/3000]: 100%|████████████████| 9/9 [00:00<00:00, 591.98it/s, loss=2.76]
Epoch[688/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 550.69it/s, loss=2.7]
Epoch[689/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 565.44it/s, loss=2.9]
Epoch[690/3000]: 100%|████████████████| 9/9 [00:00<00:00, 587.04it/s, loss=3.79]
Epoch[691/3000]: 100%|████████████████| 9/9 [00:00<00:00, 576.67it/s, loss=2.65]Epoch [691/3000]: Train loss: 2.8756, Valid loss: 2.3805Epoch[692/3000]: 100%|████████████████| 9/9 [00:00<00:00, 617.24it/s, loss=3.71]
Epoch[693/3000]: 100%|████████████████| 9/9 [00:00<00:00, 543.21it/s, loss=3.55]
Epoch[694/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 547.31it/s, loss=2.6]
Epoch[695/3000]: 100%|████████████████| 9/9 [00:00<00:00, 553.78it/s, loss=2.09]
Epoch[696/3000]: 100%|████████████████| 9/9 [00:00<00:00, 574.86it/s, loss=2.55]Epoch [696/3000]: Train loss: 3.3709, Valid loss: 4.8467Epoch[697/3000]: 100%|████████████████| 9/9 [00:00<00:00, 614.71it/s, loss=6.68]
Epoch[698/3000]: 100%|████████████████| 9/9 [00:00<00:00, 598.45it/s, loss=1.88]
Epoch[699/3000]: 100%|████████████████| 9/9 [00:00<00:00, 574.79it/s, loss=2.01]
Epoch[700/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 602.01it/s, loss=2.2]
Epoch[701/3000]: 100%|████████████████| 9/9 [00:00<00:00, 601.40it/s, loss=3.59]Epoch [701/3000]: Train loss: 2.3842, Valid loss: 2.4825Epoch[702/3000]: 100%|████████████████| 9/9 [00:00<00:00, 548.08it/s, loss=2.12]
Epoch[703/3000]: 100%|████████████████| 9/9 [00:00<00:00, 529.60it/s, loss=2.49]
Epoch[704/3000]: 100%|████████████████| 9/9 [00:00<00:00, 621.64it/s, loss=2.07]
Epoch[705/3000]: 100%|████████████████| 9/9 [00:00<00:00, 560.96it/s, loss=2.24]
Epoch[706/3000]: 100%|████████████████| 9/9 [00:00<00:00, 541.32it/s, loss=1.76]Epoch [706/3000]: Train loss: 2.3140, Valid loss: 2.8863Epoch[707/3000]: 100%|████████████████| 9/9 [00:00<00:00, 575.71it/s, loss=2.54]
Epoch[708/3000]: 100%|████████████████| 9/9 [00:00<00:00, 466.15it/s, loss=2.74]
Epoch[709/3000]: 100%|████████████████| 9/9 [00:00<00:00, 307.89it/s, loss=2.17]
Epoch[710/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 496.15it/s, loss=2.1]
Epoch[711/3000]: 100%|████████████████| 9/9 [00:00<00:00, 359.97it/s, loss=1.64]Epoch [711/3000]: Train loss: 2.0468, Valid loss: 3.8356Epoch[712/3000]: 100%|████████████████| 9/9 [00:00<00:00, 465.26it/s, loss=3.16]
Epoch[713/3000]: 100%|████████████████| 9/9 [00:00<00:00, 665.59it/s, loss=3.62]
Epoch[714/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 519.33it/s, loss=2.9]
Epoch[715/3000]: 100%|████████████████| 9/9 [00:00<00:00, 412.72it/s, loss=3.66]
Epoch[716/3000]:   0%|                         | 0/9 [00:00<?, ?it/s, loss=2.24]IOPub message rate exceeded.
The notebook server will temporarily stop sending output
to the client in order to avoid crashing it.
To change this limit, set the config variable
`--NotebookApp.iopub_msg_rate_limit`.Current values:
NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)
NotebookApp.rate_limit_window=3.0 (secs)Epoch[759/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 702.35it/s, loss=1.5]
Epoch[760/3000]: 100%|████████████████| 9/9 [00:00<00:00, 547.14it/s, loss=2.38]
Epoch[761/3000]: 100%|████████████████| 9/9 [00:00<00:00, 581.43it/s, loss=3.39]Epoch [761/3000]: Train loss: 2.9445, Valid loss: 2.2484Epoch[762/3000]: 100%|████████████████| 9/9 [00:00<00:00, 570.02it/s, loss=1.96]
Epoch[763/3000]: 100%|████████████████| 9/9 [00:00<00:00, 556.79it/s, loss=3.02]
Epoch[764/3000]: 100%|████████████████| 9/9 [00:00<00:00, 566.86it/s, loss=2.75]
Epoch[765/3000]: 100%|████████████████| 9/9 [00:00<00:00, 579.08it/s, loss=1.65]
Epoch[766/3000]: 100%|████████████████| 9/9 [00:00<00:00, 603.61it/s, loss=1.76]Epoch [766/3000]: Train loss: 2.0495, Valid loss: 2.4243Epoch[767/3000]: 100%|████████████████| 9/9 [00:00<00:00, 559.59it/s, loss=1.68]
Epoch[768/3000]: 100%|████████████████| 9/9 [00:00<00:00, 556.96it/s, loss=2.15]
Epoch[769/3000]: 100%|████████████████| 9/9 [00:00<00:00, 565.15it/s, loss=2.36]
Epoch[770/3000]: 100%|████████████████| 9/9 [00:00<00:00, 572.44it/s, loss=3.69]
Epoch[771/3000]: 100%|████████████████| 9/9 [00:00<00:00, 556.66it/s, loss=3.23]Epoch [771/3000]: Train loss: 2.9689, Valid loss: 2.3331Epoch[772/3000]: 100%|████████████████| 9/9 [00:00<00:00, 642.77it/s, loss=2.81]
Epoch[773/3000]: 100%|████████████████| 9/9 [00:00<00:00, 549.82it/s, loss=3.93]
Epoch[774/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 575.11it/s, loss=3.5]
Epoch[775/3000]: 100%|████████████████| 9/9 [00:00<00:00, 558.62it/s, loss=2.26]
Epoch[776/3000]: 100%|████████████████| 9/9 [00:00<00:00, 563.99it/s, loss=3.32]Epoch [776/3000]: Train loss: 2.0926, Valid loss: 4.1980Epoch[777/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 622.79it/s, loss=1.5]
Epoch[778/3000]: 100%|████████████████| 9/9 [00:00<00:00, 572.81it/s, loss=2.46]
Epoch[779/3000]: 100%|████████████████| 9/9 [00:00<00:00, 578.36it/s, loss=4.75]
Epoch[780/3000]: 100%|████████████████| 9/9 [00:00<00:00, 600.40it/s, loss=2.33]
Epoch[781/3000]: 100%|████████████████| 9/9 [00:00<00:00, 529.81it/s, loss=2.81]Epoch [781/3000]: Train loss: 2.8147, Valid loss: 2.1015Epoch[782/3000]: 100%|████████████████| 9/9 [00:00<00:00, 601.55it/s, loss=2.12]
Epoch[783/3000]: 100%|████████████████| 9/9 [00:00<00:00, 606.72it/s, loss=2.51]
Epoch[784/3000]: 100%|████████████████| 9/9 [00:00<00:00, 110.13it/s, loss=2.43]
Epoch[785/3000]: 100%|████████████████| 9/9 [00:00<00:00, 582.16it/s, loss=2.69]
Epoch[786/3000]: 100%|████████████████| 9/9 [00:00<00:00, 560.19it/s, loss=2.26]Epoch [786/3000]: Train loss: 2.3868, Valid loss: 2.1281Epoch[787/3000]: 100%|████████████████| 9/9 [00:00<00:00, 514.67it/s, loss=2.74]
Epoch[788/3000]: 100%|████████████████| 9/9 [00:00<00:00, 358.91it/s, loss=2.48]
Epoch[789/3000]: 100%|████████████████| 9/9 [00:00<00:00, 632.20it/s, loss=2.04]
Epoch[790/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 581.43it/s, loss=2.2]
Epoch[791/3000]: 100%|████████████████| 9/9 [00:00<00:00, 576.63it/s, loss=1.78]Epoch [791/3000]: Train loss: 2.1769, Valid loss: 2.2084Epoch[792/3000]: 100%|████████████████| 9/9 [00:00<00:00, 576.56it/s, loss=2.54]
Epoch[793/3000]: 100%|████████████████| 9/9 [00:00<00:00, 568.15it/s, loss=1.84]
Epoch[794/3000]: 100%|████████████████| 9/9 [00:00<00:00, 607.08it/s, loss=2.51]
Epoch[795/3000]: 100%|████████████████| 9/9 [00:00<00:00, 621.63it/s, loss=2.68]
Epoch[796/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 545.53it/s, loss=2.2]Epoch [796/3000]: Train loss: 2.1000, Valid loss: 2.2019Epoch[797/3000]: 100%|████████████████| 9/9 [00:00<00:00, 559.26it/s, loss=2.11]
Epoch[798/3000]: 100%|████████████████| 9/9 [00:00<00:00, 591.76it/s, loss=1.62]
Epoch[799/3000]: 100%|████████████████| 9/9 [00:00<00:00, 637.27it/s, loss=1.45]
Epoch[800/3000]: 100%|████████████████| 9/9 [00:00<00:00, 586.51it/s, loss=1.58]
Epoch[801/3000]: 100%|████████████████| 9/9 [00:00<00:00, 579.96it/s, loss=1.97]Epoch [801/3000]: Train loss: 2.1124, Valid loss: 2.0986Epoch[802/3000]: 100%|████████████████| 9/9 [00:00<00:00, 611.79it/s, loss=2.21]
Epoch[803/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 596.82it/s, loss=2.7]
Epoch[804/3000]: 100%|████████████████| 9/9 [00:00<00:00, 567.36it/s, loss=1.78]
Epoch[805/3000]: 100%|████████████████| 9/9 [00:00<00:00, 591.65it/s, loss=2.16]
Epoch[806/3000]: 100%|████████████████| 9/9 [00:00<00:00, 589.46it/s, loss=2.55]Epoch [806/3000]: Train loss: 2.3287, Valid loss: 2.2653Epoch[807/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 641.80it/s, loss=1.9]
Epoch[808/3000]: 100%|████████████████| 9/9 [00:00<00:00, 662.31it/s, loss=3.49]
Epoch[809/3000]: 100%|████████████████| 9/9 [00:00<00:00, 537.85it/s, loss=1.99]
Epoch[810/3000]: 100%|████████████████| 9/9 [00:00<00:00, 577.48it/s, loss=3.58]
Epoch[811/3000]: 100%|████████████████| 9/9 [00:00<00:00, 574.98it/s, loss=3.52]Epoch [811/3000]: Train loss: 2.2483, Valid loss: 2.0888Epoch[812/3000]: 100%|████████████████| 9/9 [00:00<00:00, 555.86it/s, loss=2.05]
Epoch[813/3000]: 100%|████████████████| 9/9 [00:00<00:00, 585.59it/s, loss=2.46]
Epoch[814/3000]: 100%|████████████████| 9/9 [00:00<00:00, 552.69it/s, loss=1.78]
Epoch[815/3000]: 100%|████████████████| 9/9 [00:00<00:00, 574.89it/s, loss=1.79]
Epoch[816/3000]: 100%|████████████████| 9/9 [00:00<00:00, 560.26it/s, loss=1.95]Epoch [816/3000]: Train loss: 2.0252, Valid loss: 2.1108Epoch[817/3000]: 100%|████████████████| 9/9 [00:00<00:00, 578.44it/s, loss=1.65]
Epoch[818/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 608.60it/s, loss=2.2]
Epoch[819/3000]: 100%|████████████████| 9/9 [00:00<00:00, 589.79it/s, loss=2.69]
Epoch[820/3000]: 100%|████████████████| 9/9 [00:00<00:00, 556.76it/s, loss=3.81]
Epoch[821/3000]: 100%|████████████████| 9/9 [00:00<00:00, 534.58it/s, loss=2.41]Epoch [821/3000]: Train loss: 2.5700, Valid loss: 2.2080Epoch[822/3000]: 100%|████████████████| 9/9 [00:00<00:00, 536.48it/s, loss=2.74]
Epoch[823/3000]: 100%|████████████████| 9/9 [00:00<00:00, 636.44it/s, loss=2.15]
Epoch[824/3000]: 100%|████████████████| 9/9 [00:00<00:00, 639.60it/s, loss=2.07]
Epoch[825/3000]: 100%|████████████████| 9/9 [00:00<00:00, 572.92it/s, loss=2.58]
Epoch[826/3000]: 100%|████████████████| 9/9 [00:00<00:00, 536.26it/s, loss=1.42]Epoch [826/3000]: Train loss: 2.3873, Valid loss: 2.3527Epoch[827/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 559.32it/s, loss=2.2]
Epoch[828/3000]: 100%|████████████████| 9/9 [00:00<00:00, 563.83it/s, loss=1.97]
Epoch[829/3000]: 100%|████████████████| 9/9 [00:00<00:00, 594.68it/s, loss=2.08]
Epoch[830/3000]: 100%|████████████████| 9/9 [00:00<00:00, 555.32it/s, loss=1.75]
Epoch[831/3000]: 100%|████████████████| 9/9 [00:00<00:00, 603.05it/s, loss=2.34]Epoch [831/3000]: Train loss: 2.2160, Valid loss: 2.0451Epoch[832/3000]: 100%|████████████████| 9/9 [00:00<00:00, 548.41it/s, loss=1.58]
Epoch[833/3000]: 100%|████████████████| 9/9 [00:00<00:00, 567.00it/s, loss=1.63]
Epoch[834/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 621.19it/s, loss=2.7]
Epoch[835/3000]: 100%|████████████████| 9/9 [00:00<00:00, 558.06it/s, loss=2.49]
Epoch[836/3000]: 100%|████████████████| 9/9 [00:00<00:00, 554.29it/s, loss=1.79]Epoch [836/3000]: Train loss: 2.2321, Valid loss: 2.2503Epoch[837/3000]: 100%|████████████████| 9/9 [00:00<00:00, 546.48it/s, loss=1.97]
Epoch[838/3000]: 100%|████████████████| 9/9 [00:00<00:00, 540.02it/s, loss=1.46]
Epoch[839/3000]: 100%|████████████████| 9/9 [00:00<00:00, 643.87it/s, loss=3.67]
Epoch[840/3000]: 100%|████████████████| 9/9 [00:00<00:00, 587.19it/s, loss=1.55]
Epoch[841/3000]: 100%|███████████████████| 9/9 [00:00<00:00, 560.44it/s, loss=4]Epoch [841/3000]: Train loss: 2.8251, Valid loss: 2.8979Epoch[842/3000]: 100%|████████████████| 9/9 [00:00<00:00, 541.58it/s, loss=3.07]
Epoch[843/3000]: 100%|████████████████| 9/9 [00:00<00:00, 346.83it/s, loss=3.69]
Epoch[844/3000]: 100%|████████████████| 9/9 [00:00<00:00, 636.63it/s, loss=2.61]
Epoch[845/3000]: 100%|████████████████| 9/9 [00:00<00:00, 559.11it/s, loss=2.12]
Epoch[846/3000]: 100%|████████████████| 9/9 [00:00<00:00, 548.55it/s, loss=2.06]Epoch [846/3000]: Train loss: 3.2342, Valid loss: 4.6133Epoch[847/3000]: 100%|████████████████| 9/9 [00:00<00:00, 540.96it/s, loss=2.68]
Epoch[848/3000]: 100%|████████████████| 9/9 [00:00<00:00, 555.42it/s, loss=2.46]
Epoch[849/3000]: 100%|████████████████| 9/9 [00:00<00:00, 637.57it/s, loss=2.43]
Epoch[850/3000]: 100%|████████████████| 9/9 [00:00<00:00, 588.51it/s, loss=2.31]
Epoch[851/3000]: 100%|████████████████| 9/9 [00:00<00:00, 553.06it/s, loss=2.25]Epoch [851/3000]: Train loss: 2.1678, Valid loss: 2.2319Epoch[852/3000]: 100%|████████████████| 9/9 [00:00<00:00, 542.20it/s, loss=2.21]
Epoch[853/3000]: 100%|████████████████| 9/9 [00:00<00:00, 523.31it/s, loss=2.68]
Epoch[854/3000]: 100%|████████████████| 9/9 [00:00<00:00, 590.43it/s, loss=3.71]
Epoch[855/3000]: 100%|████████████████| 9/9 [00:00<00:00, 631.98it/s, loss=3.75]
Epoch[856/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 557.72it/s, loss=3.7]Epoch [856/3000]: Train loss: 3.8727, Valid loss: 5.0476Epoch[857/3000]: 100%|████████████████| 9/9 [00:00<00:00, 546.97it/s, loss=4.96]
Epoch[858/3000]: 100%|████████████████| 9/9 [00:00<00:00, 549.38it/s, loss=1.72]
Epoch[859/3000]: 100%|████████████████| 9/9 [00:00<00:00, 547.34it/s, loss=2.38]
Epoch[860/3000]: 100%|████████████████| 9/9 [00:00<00:00, 631.75it/s, loss=2.19]
Epoch[861/3000]: 100%|████████████████| 9/9 [00:00<00:00, 552.24it/s, loss=4.83]Epoch [861/3000]: Train loss: 3.2318, Valid loss: 5.5522Epoch[862/3000]: 100%|████████████████| 9/9 [00:00<00:00, 574.82it/s, loss=5.04]
Epoch[863/3000]: 100%|████████████████| 9/9 [00:00<00:00, 573.61it/s, loss=4.09]
Epoch[864/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 555.94it/s, loss=4.6]
Epoch[865/3000]: 100%|████████████████| 9/9 [00:00<00:00, 609.10it/s, loss=3.41]
Epoch[866/3000]: 100%|████████████████| 9/9 [00:00<00:00, 579.07it/s, loss=1.92]Epoch [866/3000]: Train loss: 2.8998, Valid loss: 4.5630Epoch[867/3000]: 100%|████████████████| 9/9 [00:00<00:00, 538.57it/s, loss=2.07]
Epoch[868/3000]: 100%|████████████████| 9/9 [00:00<00:00, 559.36it/s, loss=2.32]
Epoch[869/3000]: 100%|████████████████| 9/9 [00:00<00:00, 113.21it/s, loss=1.91]
Epoch[870/3000]: 100%|████████████████| 9/9 [00:00<00:00, 532.26it/s, loss=2.39]
Epoch[871/3000]: 100%|████████████████| 9/9 [00:00<00:00, 570.99it/s, loss=2.19]Epoch [871/3000]: Train loss: 2.5965, Valid loss: 2.5087Epoch[872/3000]: 100%|████████████████| 9/9 [00:00<00:00, 612.59it/s, loss=2.09]
Epoch[873/3000]: 100%|████████████████| 9/9 [00:00<00:00, 564.48it/s, loss=3.95]
Epoch[874/3000]: 100%|████████████████| 9/9 [00:00<00:00, 548.71it/s, loss=1.77]
Epoch[875/3000]: 100%|████████████████| 9/9 [00:00<00:00, 559.70it/s, loss=2.74]
Epoch[876/3000]: 100%|████████████████| 9/9 [00:00<00:00, 585.41it/s, loss=1.75]Epoch [876/3000]: Train loss: 2.1449, Valid loss: 2.5297Epoch[877/3000]: 100%|████████████████| 9/9 [00:00<00:00, 599.03it/s, loss=2.34]
Epoch[878/3000]: 100%|████████████████| 9/9 [00:00<00:00, 653.65it/s, loss=2.11]
Epoch[879/3000]: 100%|████████████████| 9/9 [00:00<00:00, 550.66it/s, loss=2.96]
Epoch[880/3000]: 100%|████████████████| 9/9 [00:00<00:00, 547.34it/s, loss=1.77]
Epoch[881/3000]: 100%|████████████████| 9/9 [00:00<00:00, 568.15it/s, loss=2.35]Epoch [881/3000]: Train loss: 2.0954, Valid loss: 2.9795Epoch[882/3000]: 100%|████████████████| 9/9 [00:00<00:00, 538.61it/s, loss=2.68]
Epoch[883/3000]: 100%|████████████████| 9/9 [00:00<00:00, 615.30it/s, loss=2.22]
Epoch[884/3000]: 100%|████████████████| 9/9 [00:00<00:00, 562.18it/s, loss=5.88]
Epoch[885/3000]: 100%|████████████████| 9/9 [00:00<00:00, 574.68it/s, loss=3.25]
Epoch[886/3000]: 100%|████████████████| 9/9 [00:00<00:00, 565.93it/s, loss=5.59]Epoch [886/3000]: Train loss: 3.6591, Valid loss: 5.9619Epoch[887/3000]: 100%|████████████████| 9/9 [00:00<00:00, 523.29it/s, loss=6.93]
Epoch[888/3000]: 100%|████████████████| 9/9 [00:00<00:00, 596.70it/s, loss=3.38]
Epoch[889/3000]: 100%|████████████████| 9/9 [00:00<00:00, 573.03it/s, loss=2.05]
Epoch[890/3000]: 100%|████████████████| 9/9 [00:00<00:00, 542.16it/s, loss=2.94]
Epoch[891/3000]: 100%|████████████████| 9/9 [00:00<00:00, 572.74it/s, loss=1.54]Epoch [891/3000]: Train loss: 2.8107, Valid loss: 3.9627Epoch[892/3000]: 100%|████████████████| 9/9 [00:00<00:00, 567.00it/s, loss=1.71]
Epoch[893/3000]: 100%|████████████████| 9/9 [00:00<00:00, 616.74it/s, loss=2.33]
Epoch[894/3000]: 100%|████████████████| 9/9 [00:00<00:00, 631.23it/s, loss=1.69]
Epoch[895/3000]: 100%|████████████████| 9/9 [00:00<00:00, 552.02it/s, loss=1.87]
Epoch[896/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 556.82it/s, loss=1.7]Epoch [896/3000]: Train loss: 2.1222, Valid loss: 2.1637Epoch[897/3000]: 100%|████████████████| 9/9 [00:00<00:00, 543.67it/s, loss=1.88]
Epoch[898/3000]: 100%|████████████████| 9/9 [00:00<00:00, 339.30it/s, loss=1.89]
Epoch[899/3000]: 100%|████████████████| 9/9 [00:00<00:00, 580.27it/s, loss=2.62]
Epoch[900/3000]:   0%|                         | 0/9 [00:00<?, ?it/s, loss=2.11]IOPub message rate exceeded.
The notebook server will temporarily stop sending output
to the client in order to avoid crashing it.
To change this limit, set the config variable
`--NotebookApp.iopub_msg_rate_limit`.Current values:
NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)
NotebookApp.rate_limit_window=3.0 (secs)Epoch[949/3000]: 100%|████████████████| 9/9 [00:00<00:00, 745.40it/s, loss=1.49]
Epoch[950/3000]: 100%|████████████████| 9/9 [00:00<00:00, 571.65it/s, loss=1.78]
Epoch[951/3000]: 100%|████████████████| 9/9 [00:00<00:00, 521.95it/s, loss=1.83]Epoch [951/3000]: Train loss: 2.0092, Valid loss: 2.6209Epoch[952/3000]: 100%|████████████████| 9/9 [00:00<00:00, 539.86it/s, loss=2.42]
Epoch[953/3000]: 100%|████████████████| 9/9 [00:00<00:00, 111.96it/s, loss=2.13]
Epoch[954/3000]: 100%|█████████████████| 9/9 [00:00<00:00, 538.98it/s, loss=1.9]
Epoch[955/3000]: 100%|████████████████| 9/9 [00:00<00:00, 571.69it/s, loss=2.05]
Epoch[956/3000]: 100%|████████████████| 9/9 [00:00<00:00, 618.18it/s, loss=2.42]Epoch [956/3000]: Train loss: 2.2175, Valid loss: 3.2116Epoch[957/3000]: 100%|████████████████| 9/9 [00:00<00:00, 537.41it/s, loss=3.24]
Epoch[958/3000]: 100%|████████████████| 9/9 [00:00<00:00, 547.65it/s, loss=2.59]
Epoch[959/3000]: 100%|████████████████| 9/9 [00:00<00:00, 549.89it/s, loss=1.59]
Epoch[960/3000]: 100%|████████████████| 9/9 [00:00<00:00, 563.02it/s, loss=2.26]
Epoch[961/3000]: 100%|████████████████| 9/9 [00:00<00:00, 625.35it/s, loss=1.62]Epoch [961/3000]: Train loss: 2.1834, Valid loss: 2.1123Epoch[962/3000]: 100%|████████████████| 9/9 [00:00<00:00, 562.00it/s, loss=1.74]
Epoch[963/3000]: 100%|████████████████| 9/9 [00:00<00:00, 564.94it/s, loss=2.34]
Epoch[964/3000]: 100%|████████████████| 9/9 [00:00<00:00, 578.36it/s, loss=2.42]
Epoch[965/3000]: 100%|████████████████| 9/9 [00:00<00:00, 537.69it/s, loss=1.98]
Epoch[966/3000]: 100%|████████████████| 9/9 [00:00<00:00, 655.55it/s, loss=3.15]Epoch [966/3000]: Train loss: 2.0302, Valid loss: 2.0463Epoch[967/3000]: 100%|████████████████| 9/9 [00:00<00:00, 619.19it/s, loss=2.44]
Epoch[968/3000]: 100%|████████████████| 9/9 [00:00<00:00, 574.71it/s, loss=1.76]
Epoch[969/3000]: 100%|████████████████| 9/9 [00:00<00:00, 581.74it/s, loss=1.73]
Epoch[970/3000]: 100%|████████████████| 9/9 [00:00<00:00, 565.04it/s, loss=1.55]
Epoch[971/3000]: 100%|████████████████| 9/9 [00:00<00:00, 556.90it/s, loss=2.15]Epoch [971/3000]: Train loss: 1.9082, Valid loss: 2.0408Epoch[972/3000]: 100%|████████████████| 9/9 [00:00<00:00, 631.41it/s, loss=2.64]
Epoch[973/3000]: 100%|████████████████| 9/9 [00:00<00:00, 556.59it/s, loss=2.21]
Epoch[974/3000]: 100%|████████████████| 9/9 [00:00<00:00, 534.06it/s, loss=4.51]
Epoch[975/3000]: 100%|████████████████| 9/9 [00:00<00:00, 562.78it/s, loss=2.24]
Epoch[976/3000]: 100%|████████████████| 9/9 [00:00<00:00, 580.27it/s, loss=2.27]Epoch [976/3000]: Train loss: 2.4002, Valid loss: 2.4491Epoch[977/3000]: 100%|████████████████| 9/9 [00:00<00:00, 638.39it/s, loss=2.45]
Epoch[978/3000]: 100%|████████████████| 9/9 [00:00<00:00, 590.78it/s, loss=1.75]
Epoch[979/3000]: 100%|████████████████| 9/9 [00:00<00:00, 545.82it/s, loss=1.83]
Epoch[980/3000]: 100%|████████████████| 9/9 [00:00<00:00, 606.87it/s, loss=1.89]
Epoch[981/3000]: 100%|████████████████| 9/9 [00:00<00:00, 596.50it/s, loss=1.99]Epoch [981/3000]: Train loss: 2.1948, Valid loss: 3.2242Epoch[982/3000]: 100%|████████████████| 9/9 [00:00<00:00, 603.83it/s, loss=2.35]
Epoch[983/3000]: 100%|████████████████| 9/9 [00:00<00:00, 660.45it/s, loss=2.21]
Epoch[984/3000]: 100%|████████████████| 9/9 [00:00<00:00, 543.58it/s, loss=2.22]
Epoch[985/3000]: 100%|████████████████| 9/9 [00:00<00:00, 557.97it/s, loss=2.37]
Epoch[986/3000]: 100%|████████████████| 9/9 [00:00<00:00, 578.89it/s, loss=4.07]Epoch [986/3000]: Train loss: 3.2702, Valid loss: 2.1775Epoch[987/3000]: 100%|████████████████| 9/9 [00:00<00:00, 555.42it/s, loss=2.33]
Epoch[988/3000]: 100%|████████████████| 9/9 [00:00<00:00, 642.72it/s, loss=1.46]
Epoch[989/3000]: 100%|████████████████| 9/9 [00:00<00:00, 587.28it/s, loss=1.65]
Epoch[990/3000]: 100%|████████████████| 9/9 [00:00<00:00, 582.02it/s, loss=2.46]
Epoch[991/3000]: 100%|████████████████| 9/9 [00:00<00:00, 601.12it/s, loss=1.76]Epoch [991/3000]: Train loss: 2.1049, Valid loss: 2.5184Epoch[992/3000]: 100%|████████████████| 9/9 [00:00<00:00, 568.75it/s, loss=2.12]
Epoch[993/3000]: 100%|████████████████| 9/9 [00:00<00:00, 651.04it/s, loss=2.78]
Epoch[994/3000]: 100%|████████████████| 9/9 [00:00<00:00, 592.84it/s, loss=2.68]
Epoch[995/3000]: 100%|████████████████| 9/9 [00:00<00:00, 563.62it/s, loss=2.82]
Epoch[996/3000]: 100%|████████████████| 9/9 [00:00<00:00, 553.95it/s, loss=1.89]Epoch [996/3000]: Train loss: 1.8945, Valid loss: 3.4714Epoch[997/3000]: 100%|████████████████| 9/9 [00:00<00:00, 556.90it/s, loss=2.74]
Epoch[998/3000]: 100%|████████████████| 9/9 [00:00<00:00, 585.02it/s, loss=1.98]
Epoch[999/3000]: 100%|████████████████| 9/9 [00:00<00:00, 646.26it/s, loss=2.05]
Epoch[1000/3000]: 100%|███████████████| 9/9 [00:00<00:00, 582.11it/s, loss=1.77]
Epoch[1001/3000]: 100%|███████████████| 9/9 [00:00<00:00, 577.52it/s, loss=1.74]Epoch [1001/3000]: Train loss: 1.9686, Valid loss: 2.2102Epoch[1002/3000]: 100%|███████████████| 9/9 [00:00<00:00, 372.22it/s, loss=2.97]
Epoch[1003/3000]: 100%|███████████████| 9/9 [00:00<00:00, 592.84it/s, loss=3.07]
Epoch[1004/3000]: 100%|███████████████| 9/9 [00:00<00:00, 651.37it/s, loss=2.27]
Epoch[1005/3000]: 100%|███████████████| 9/9 [00:00<00:00, 549.46it/s, loss=2.64]
Epoch[1006/3000]: 100%|███████████████| 9/9 [00:00<00:00, 589.59it/s, loss=1.47]Epoch [1006/3000]: Train loss: 3.1894, Valid loss: 6.6508Epoch[1007/3000]: 100%|███████████████| 9/9 [00:00<00:00, 570.70it/s, loss=2.23]
Epoch[1008/3000]: 100%|███████████████| 9/9 [00:00<00:00, 552.80it/s, loss=1.91]
Epoch[1009/3000]: 100%|███████████████| 9/9 [00:00<00:00, 642.12it/s, loss=1.95]
Epoch[1010/3000]: 100%|███████████████| 9/9 [00:00<00:00, 594.68it/s, loss=2.58]
Epoch[1011/3000]: 100%|███████████████| 9/9 [00:00<00:00, 561.27it/s, loss=1.71]Epoch [1011/3000]: Train loss: 2.4340, Valid loss: 2.3478Epoch[1012/3000]: 100%|███████████████| 9/9 [00:00<00:00, 557.08it/s, loss=2.63]
Epoch[1013/3000]: 100%|███████████████| 9/9 [00:00<00:00, 591.76it/s, loss=2.35]
Epoch[1014/3000]: 100%|████████████████| 9/9 [00:00<00:00, 643.78it/s, loss=2.5]
Epoch[1015/3000]: 100%|███████████████| 9/9 [00:00<00:00, 669.49it/s, loss=1.67]
Epoch[1016/3000]: 100%|███████████████| 9/9 [00:00<00:00, 565.29it/s, loss=2.28]Epoch [1016/3000]: Train loss: 1.8940, Valid loss: 2.0376Epoch[1017/3000]: 100%|████████████████| 9/9 [00:00<00:00, 555.55it/s, loss=2.7]
Epoch[1018/3000]: 100%|███████████████| 9/9 [00:00<00:00, 592.10it/s, loss=2.16]
Epoch[1019/3000]: 100%|███████████████| 9/9 [00:00<00:00, 588.97it/s, loss=4.49]
Epoch[1020/3000]: 100%|███████████████| 9/9 [00:00<00:00, 631.02it/s, loss=2.43]
Epoch[1021/3000]: 100%|███████████████| 9/9 [00:00<00:00, 588.66it/s, loss=1.75]Epoch [1021/3000]: Train loss: 2.0224, Valid loss: 2.2147Epoch[1022/3000]: 100%|███████████████| 9/9 [00:00<00:00, 576.74it/s, loss=1.67]
Epoch[1023/3000]: 100%|███████████████| 9/9 [00:00<00:00, 612.91it/s, loss=2.17]
Epoch[1024/3000]: 100%|███████████████| 9/9 [00:00<00:00, 584.07it/s, loss=2.39]
Epoch[1025/3000]: 100%|███████████████| 9/9 [00:00<00:00, 670.64it/s, loss=2.22]
Epoch[1026/3000]: 100%|███████████████| 9/9 [00:00<00:00, 662.97it/s, loss=1.71]Epoch [1026/3000]: Train loss: 2.3764, Valid loss: 2.3493Epoch[1027/3000]: 100%|███████████████| 9/9 [00:00<00:00, 572.01it/s, loss=2.79]
Epoch[1028/3000]: 100%|███████████████| 9/9 [00:00<00:00, 573.29it/s, loss=3.04]
Epoch[1029/3000]: 100%|███████████████| 9/9 [00:00<00:00, 546.15it/s, loss=2.79]
Epoch[1030/3000]: 100%|███████████████| 9/9 [00:00<00:00, 571.65it/s, loss=1.57]
Epoch[1031/3000]: 100%|███████████████| 9/9 [00:00<00:00, 641.34it/s, loss=1.86]Epoch [1031/3000]: Train loss: 2.0662, Valid loss: 2.0160Epoch[1032/3000]: 100%|███████████████| 9/9 [00:00<00:00, 570.12it/s, loss=1.94]
Epoch[1033/3000]: 100%|███████████████| 9/9 [00:00<00:00, 603.90it/s, loss=1.85]
Epoch[1034/3000]: 100%|███████████████| 9/9 [00:00<00:00, 618.90it/s, loss=2.33]
Epoch[1035/3000]: 100%|███████████████| 9/9 [00:00<00:00, 596.57it/s, loss=3.15]
Epoch[1036/3000]: 100%|███████████████| 9/9 [00:00<00:00, 662.10it/s, loss=1.77]Epoch [1036/3000]: Train loss: 2.1482, Valid loss: 2.3399Epoch[1037/3000]: 100%|██████████████████| 9/9 [00:00<00:00, 593.81it/s, loss=2]
Epoch[1038/3000]: 100%|███████████████| 9/9 [00:00<00:00, 116.48it/s, loss=1.49]
Epoch[1039/3000]: 100%|████████████████| 9/9 [00:00<00:00, 579.15it/s, loss=2.5]
Epoch[1040/3000]: 100%|███████████████| 9/9 [00:00<00:00, 561.28it/s, loss=2.95]
Epoch[1041/3000]: 100%|███████████████| 9/9 [00:00<00:00, 572.44it/s, loss=3.13]Epoch [1041/3000]: Train loss: 2.9021, Valid loss: 2.4611Epoch[1042/3000]: 100%|███████████████| 9/9 [00:00<00:00, 538.74it/s, loss=2.33]
Epoch[1043/3000]: 100%|███████████████| 9/9 [00:00<00:00, 593.94it/s, loss=2.73]
Epoch[1044/3000]: 100%|███████████████| 9/9 [00:00<00:00, 655.17it/s, loss=2.84]
Epoch[1045/3000]: 100%|███████████████| 9/9 [00:00<00:00, 554.43it/s, loss=3.25]
Epoch[1046/3000]: 100%|███████████████| 9/9 [00:00<00:00, 585.14it/s, loss=3.18]Epoch [1046/3000]: Train loss: 2.5910, Valid loss: 4.8387Epoch[1047/3000]: 100%|███████████████| 9/9 [00:00<00:00, 525.76it/s, loss=2.38]
Epoch[1048/3000]: 100%|███████████████| 9/9 [00:00<00:00, 555.67it/s, loss=3.26]
Epoch[1049/3000]: 100%|███████████████| 9/9 [00:00<00:00, 639.57it/s, loss=2.54]
Epoch[1050/3000]: 100%|███████████████| 9/9 [00:00<00:00, 593.51it/s, loss=4.52]
Epoch[1051/3000]: 100%|████████████████| 9/9 [00:00<00:00, 544.04it/s, loss=2.4]Epoch [1051/3000]: Train loss: 2.7422, Valid loss: 3.1845Epoch[1052/3000]: 100%|███████████████| 9/9 [00:00<00:00, 564.22it/s, loss=2.03]
Epoch[1053/3000]: 100%|████████████████| 9/9 [00:00<00:00, 582.25it/s, loss=2.7]
Epoch[1054/3000]: 100%|████████████████| 9/9 [00:00<00:00, 648.56it/s, loss=1.2]
Epoch[1055/3000]: 100%|███████████████| 9/9 [00:00<00:00, 661.18it/s, loss=3.39]
Epoch[1056/3000]: 100%|███████████████| 9/9 [00:00<00:00, 380.48it/s, loss=2.46]Epoch [1056/3000]: Train loss: 2.5639, Valid loss: 1.9056Epoch[1057/3000]: 100%|██████████████████| 9/9 [00:00<00:00, 563.66it/s, loss=2]
Epoch[1058/3000]: 100%|███████████████| 9/9 [00:00<00:00, 595.62it/s, loss=1.86]
Epoch[1059/3000]: 100%|███████████████| 9/9 [00:00<00:00, 646.33it/s, loss=2.38]
Epoch[1060/3000]: 100%|███████████████| 9/9 [00:00<00:00, 655.17it/s, loss=1.82]
Epoch[1061/3000]: 100%|███████████████| 9/9 [00:00<00:00, 594.22it/s, loss=1.58]Epoch [1061/3000]: Train loss: 1.8466, Valid loss: 2.0081Epoch[1062/3000]: 100%|███████████████| 9/9 [00:00<00:00, 582.98it/s, loss=1.71]
Epoch[1063/3000]: 100%|███████████████| 9/9 [00:00<00:00, 569.43it/s, loss=1.38]
Epoch[1064/3000]: 100%|███████████████| 9/9 [00:00<00:00, 561.65it/s, loss=2.25]
Epoch[1065/3000]: 100%|███████████████| 9/9 [00:00<00:00, 645.94it/s, loss=1.84]
Epoch[1066/3000]: 100%|███████████████| 9/9 [00:00<00:00, 593.63it/s, loss=2.03]Epoch [1066/3000]: Train loss: 2.0165, Valid loss: 2.4945Epoch[1067/3000]: 100%|███████████████| 9/9 [00:00<00:00, 550.14it/s, loss=2.83]
Epoch[1068/3000]: 100%|███████████████| 9/9 [00:00<00:00, 609.11it/s, loss=1.51]
Epoch[1069/3000]: 100%|████████████████| 9/9 [00:00<00:00, 569.99it/s, loss=1.8]
Epoch[1070/3000]: 100%|███████████████| 9/9 [00:00<00:00, 662.61it/s, loss=2.83]
Epoch[1071/3000]: 100%|███████████████| 9/9 [00:00<00:00, 629.54it/s, loss=2.04]Epoch [1071/3000]: Train loss: 1.9308, Valid loss: 2.4547Epoch[1072/3000]: 100%|███████████████| 9/9 [00:00<00:00, 568.86it/s, loss=2.54]
Epoch[1073/3000]: 100%|███████████████| 9/9 [00:00<00:00, 575.67it/s, loss=1.53]
Epoch[1074/3000]: 100%|███████████████| 9/9 [00:00<00:00, 572.05it/s, loss=2.63]
Epoch[1075/3000]: 100%|████████████████| 9/9 [00:00<00:00, 568.33it/s, loss=2.5]
Epoch[1076/3000]: 100%|███████████████| 9/9 [00:00<00:00, 639.29it/s, loss=2.61]Epoch [1076/3000]: Train loss: 2.0557, Valid loss: 2.1908Epoch[1077/3000]: 100%|███████████████| 9/9 [00:00<00:00, 576.15it/s, loss=2.22]
Epoch[1078/3000]: 100%|███████████████| 9/9 [00:00<00:00, 564.33it/s, loss=2.05]
Epoch[1079/3000]: 100%|███████████████| 9/9 [00:00<00:00, 554.66it/s, loss=2.02]
Epoch[1080/3000]: 100%|███████████████| 9/9 [00:00<00:00, 554.97it/s, loss=2.63]
Epoch[1081/3000]: 100%|███████████████| 9/9 [00:00<00:00, 621.81it/s, loss=2.41]Epoch [1081/3000]: Train loss: 3.0756, Valid loss: 4.6525Epoch[1082/3000]: 100%|███████████████| 9/9 [00:00<00:00, 555.77it/s, loss=1.82]
Epoch[1083/3000]: 100%|███████████████| 9/9 [00:00<00:00, 552.49it/s, loss=1.63]
Epoch[1084/3000]: 100%|███████████████| 9/9 [00:00<00:00, 548.21it/s, loss=2.89]
Epoch[1085/3000]: 100%|███████████████| 9/9 [00:00<00:00, 575.71it/s, loss=2.15]
Epoch[1086/3000]: 100%|███████████████| 9/9 [00:00<00:00, 627.23it/s, loss=2.54]Epoch [1086/3000]: Train loss: 2.3169, Valid loss: 2.2510Epoch[1087/3000]: 100%|███████████████| 9/9 [00:00<00:00, 561.17it/s, loss=1.77]
Epoch[1088/3000]: 100%|███████████████| 9/9 [00:00<00:00, 554.32it/s, loss=2.91]
Epoch[1089/3000]: 100%|███████████████| 9/9 [00:00<00:00, 551.65it/s, loss=1.78]
Epoch[1090/3000]: 100%|████████████████| 9/9 [00:00<00:00, 567.79it/s, loss=2.1]
Epoch[1091/3000]:   0%|                        | 0/9 [00:00<?, ?it/s, loss=2.09]IOPub message rate exceeded.
The notebook server will temporarily stop sending output
to the client in order to avoid crashing it.
To change this limit, set the config variable
`--NotebookApp.iopub_msg_rate_limit`.Current values:
NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)
NotebookApp.rate_limit_window=3.0 (secs)Epoch[1139/3000]: 100%|███████████████| 9/9 [00:00<00:00, 732.64it/s, loss=2.01]
Epoch[1140/3000]: 100%|███████████████| 9/9 [00:00<00:00, 558.83it/s, loss=2.35]
Epoch[1141/3000]: 100%|███████████████| 9/9 [00:00<00:00, 547.98it/s, loss=1.61]Epoch [1141/3000]: Train loss: 1.7869, Valid loss: 2.1842Epoch[1142/3000]: 100%|███████████████| 9/9 [00:00<00:00, 568.40it/s, loss=1.92]
Epoch[1143/3000]: 100%|███████████████| 9/9 [00:00<00:00, 569.92it/s, loss=2.38]
Epoch[1144/3000]: 100%|███████████████| 9/9 [00:00<00:00, 628.32it/s, loss=2.23]
Epoch[1145/3000]: 100%|███████████████| 9/9 [00:00<00:00, 604.03it/s, loss=2.16]
Epoch[1146/3000]: 100%|███████████████| 9/9 [00:00<00:00, 582.79it/s, loss=2.46]Epoch [1146/3000]: Train loss: 2.5158, Valid loss: 2.3283Epoch[1147/3000]: 100%|████████████████| 9/9 [00:00<00:00, 563.88it/s, loss=2.8]
Epoch[1148/3000]: 100%|███████████████| 9/9 [00:00<00:00, 566.81it/s, loss=3.93]
Epoch[1149/3000]: 100%|███████████████| 9/9 [00:00<00:00, 631.05it/s, loss=1.73]
Epoch[1150/3000]: 100%|███████████████| 9/9 [00:00<00:00, 659.10it/s, loss=3.97]
Epoch[1151/3000]: 100%|███████████████| 9/9 [00:00<00:00, 551.97it/s, loss=2.35]Epoch [1151/3000]: Train loss: 2.7878, Valid loss: 3.2121Epoch[1152/3000]: 100%|████████████████| 9/9 [00:00<00:00, 560.05it/s, loss=5.9]
Epoch[1153/3000]: 100%|███████████████| 9/9 [00:00<00:00, 584.23it/s, loss=4.31]
Epoch[1154/3000]: 100%|███████████████| 9/9 [00:00<00:00, 577.63it/s, loss=5.07]
Epoch[1155/3000]: 100%|███████████████| 9/9 [00:00<00:00, 429.60it/s, loss=3.61]
Epoch[1156/3000]: 100%|████████████████| 9/9 [00:00<00:00, 494.23it/s, loss=4.5]Epoch [1156/3000]: Train loss: 3.6346, Valid loss: 4.2013Epoch[1157/3000]: 100%|███████████████| 9/9 [00:00<00:00, 555.35it/s, loss=2.66]
Epoch[1158/3000]: 100%|███████████████| 9/9 [00:00<00:00, 576.11it/s, loss=2.15]
Epoch[1159/3000]: 100%|███████████████| 9/9 [00:00<00:00, 556.38it/s, loss=2.23]
Epoch[1160/3000]: 100%|███████████████| 9/9 [00:00<00:00, 621.72it/s, loss=1.82]
Epoch[1161/3000]: 100%|███████████████| 9/9 [00:00<00:00, 569.23it/s, loss=1.31]Epoch [1161/3000]: Train loss: 2.6093, Valid loss: 2.8377Epoch[1162/3000]: 100%|███████████████| 9/9 [00:00<00:00, 555.14it/s, loss=1.84]
Epoch[1163/3000]: 100%|███████████████| 9/9 [00:00<00:00, 560.15it/s, loss=1.79]
Epoch[1164/3000]: 100%|███████████████| 9/9 [00:00<00:00, 533.55it/s, loss=1.67]
Epoch[1165/3000]: 100%|███████████████| 9/9 [00:00<00:00, 640.58it/s, loss=1.54]
Epoch[1166/3000]: 100%|███████████████| 9/9 [00:00<00:00, 585.67it/s, loss=2.85]Epoch [1166/3000]: Train loss: 2.0465, Valid loss: 2.3628Epoch[1167/3000]: 100%|███████████████| 9/9 [00:00<00:00, 557.86it/s, loss=2.15]
Epoch[1168/3000]: 100%|███████████████| 9/9 [00:00<00:00, 585.97it/s, loss=3.05]
Epoch[1169/3000]: 100%|███████████████| 9/9 [00:00<00:00, 601.77it/s, loss=1.82]
Epoch[1170/3000]: 100%|███████████████| 9/9 [00:00<00:00, 623.88it/s, loss=2.08]
Epoch[1171/3000]: 100%|███████████████| 9/9 [00:00<00:00, 654.64it/s, loss=4.63]Epoch [1171/3000]: Train loss: 2.3638, Valid loss: 5.8901Epoch[1172/3000]: 100%|███████████████| 9/9 [00:00<00:00, 523.34it/s, loss=2.69]
Epoch[1173/3000]: 100%|███████████████| 9/9 [00:00<00:00, 572.48it/s, loss=1.75]
Epoch[1174/3000]: 100%|███████████████| 9/9 [00:00<00:00, 576.92it/s, loss=2.23]
Epoch[1175/3000]: 100%|████████████████| 9/9 [00:00<00:00, 581.31it/s, loss=3.9]
Epoch[1176/3000]: 100%|███████████████| 9/9 [00:00<00:00, 642.46it/s, loss=1.82]Epoch [1176/3000]: Train loss: 2.6319, Valid loss: 3.2360Epoch[1177/3000]: 100%|███████████████| 9/9 [00:00<00:00, 592.15it/s, loss=4.78]
Epoch[1178/3000]: 100%|███████████████| 9/9 [00:00<00:00, 584.83it/s, loss=3.14]
Epoch[1179/3000]: 100%|████████████████| 9/9 [00:00<00:00, 610.05it/s, loss=2.6]
Epoch[1180/3000]: 100%|███████████████| 9/9 [00:00<00:00, 608.61it/s, loss=2.06]
Epoch[1181/3000]: 100%|███████████████| 9/9 [00:00<00:00, 625.74it/s, loss=2.98]Epoch [1181/3000]: Train loss: 2.3710, Valid loss: 2.3263Epoch[1182/3000]: 100%|███████████████| 9/9 [00:00<00:00, 643.82it/s, loss=1.87]
Epoch[1183/3000]: 100%|███████████████| 9/9 [00:00<00:00, 566.15it/s, loss=3.03]
Epoch[1184/3000]: 100%|███████████████| 9/9 [00:00<00:00, 548.40it/s, loss=2.93]
Epoch[1185/3000]: 100%|███████████████| 9/9 [00:00<00:00, 565.85it/s, loss=3.03]
Epoch[1186/3000]: 100%|███████████████| 9/9 [00:00<00:00, 547.08it/s, loss=1.75]Epoch [1186/3000]: Train loss: 2.1877, Valid loss: 3.3927Epoch[1187/3000]: 100%|███████████████| 9/9 [00:00<00:00, 594.09it/s, loss=2.13]
Epoch[1188/3000]: 100%|███████████████| 9/9 [00:00<00:00, 543.70it/s, loss=2.01]
Epoch[1189/3000]: 100%|███████████████| 9/9 [00:00<00:00, 489.18it/s, loss=2.29]
Epoch[1190/3000]: 100%|████████████████| 9/9 [00:00<00:00, 531.79it/s, loss=1.7]
Epoch[1191/3000]: 100%|███████████████| 9/9 [00:00<00:00, 549.78it/s, loss=2.31]Epoch [1191/3000]: Train loss: 1.9398, Valid loss: 1.8707Epoch[1192/3000]: 100%|███████████████| 9/9 [00:00<00:00, 579.19it/s, loss=1.65]
Epoch[1193/3000]: 100%|███████████████| 9/9 [00:00<00:00, 534.89it/s, loss=1.99]
Epoch[1194/3000]: 100%|███████████████| 9/9 [00:00<00:00, 559.28it/s, loss=2.54]
Epoch[1195/3000]: 100%|███████████████| 9/9 [00:00<00:00, 538.92it/s, loss=2.02]
Epoch[1196/3000]: 100%|███████████████| 9/9 [00:00<00:00, 548.28it/s, loss=1.76]Epoch [1196/3000]: Train loss: 2.1550, Valid loss: 3.1150Epoch[1197/3000]: 100%|████████████████| 9/9 [00:00<00:00, 576.18it/s, loss=2.6]
Epoch[1198/3000]: 100%|████████████████| 9/9 [00:00<00:00, 523.10it/s, loss=2.9]
Epoch[1199/3000]: 100%|███████████████| 9/9 [00:00<00:00, 563.02it/s, loss=2.14]
Epoch[1200/3000]: 100%|███████████████| 9/9 [00:00<00:00, 557.86it/s, loss=1.42]
Epoch[1201/3000]: 100%|███████████████| 9/9 [00:00<00:00, 541.02it/s, loss=2.32]Epoch [1201/3000]: Train loss: 2.3896, Valid loss: 4.2695Epoch[1202/3000]: 100%|███████████████| 9/9 [00:00<00:00, 589.55it/s, loss=5.17]
Epoch[1203/3000]: 100%|███████████████| 9/9 [00:00<00:00, 557.59it/s, loss=3.53]
Epoch[1204/3000]: 100%|███████████████| 9/9 [00:00<00:00, 553.37it/s, loss=3.36]
Epoch[1205/3000]: 100%|████████████████| 9/9 [00:00<00:00, 559.38it/s, loss=2.2]
Epoch[1206/3000]: 100%|███████████████| 9/9 [00:00<00:00, 569.41it/s, loss=1.71]Epoch [1206/3000]: Train loss: 2.0516, Valid loss: 2.2728Epoch[1207/3000]: 100%|████████████████| 9/9 [00:00<00:00, 385.20it/s, loss=1.9]
Epoch[1208/3000]: 100%|███████████████| 9/9 [00:00<00:00, 111.46it/s, loss=2.24]
Epoch[1209/3000]: 100%|███████████████| 9/9 [00:00<00:00, 615.51it/s, loss=1.32]
Epoch[1210/3000]: 100%|████████████████| 9/9 [00:00<00:00, 561.70it/s, loss=1.9]
Epoch[1211/3000]: 100%|███████████████| 9/9 [00:00<00:00, 563.73it/s, loss=1.82]Epoch [1211/3000]: Train loss: 1.9920, Valid loss: 2.1961Epoch[1212/3000]: 100%|███████████████| 9/9 [00:00<00:00, 538.90it/s, loss=2.13]
Epoch[1213/3000]: 100%|███████████████| 9/9 [00:00<00:00, 543.48it/s, loss=1.37]
Epoch[1214/3000]: 100%|███████████████| 9/9 [00:00<00:00, 619.62it/s, loss=1.89]
Epoch[1215/3000]: 100%|███████████████| 9/9 [00:00<00:00, 575.05it/s, loss=1.85]
Epoch[1216/3000]: 100%|████████████████| 9/9 [00:00<00:00, 567.43it/s, loss=2.3]Epoch [1216/3000]: Train loss: 2.3782, Valid loss: 2.5100Epoch[1217/3000]: 100%|███████████████| 9/9 [00:00<00:00, 531.07it/s, loss=3.03]
Epoch[1218/3000]: 100%|███████████████| 9/9 [00:00<00:00, 558.17it/s, loss=1.99]
Epoch[1219/3000]: 100%|███████████████| 9/9 [00:00<00:00, 606.95it/s, loss=2.14]
Epoch[1220/3000]: 100%|███████████████| 9/9 [00:00<00:00, 559.46it/s, loss=3.41]
Epoch[1221/3000]: 100%|███████████████| 9/9 [00:00<00:00, 560.44it/s, loss=1.77]Epoch [1221/3000]: Train loss: 2.7198, Valid loss: 3.6252Epoch[1222/3000]: 100%|███████████████| 9/9 [00:00<00:00, 541.09it/s, loss=3.38]
Epoch[1223/3000]: 100%|███████████████| 9/9 [00:00<00:00, 585.48it/s, loss=3.18]
Epoch[1224/3000]: 100%|███████████████| 9/9 [00:00<00:00, 619.80it/s, loss=2.22]
Epoch[1225/3000]: 100%|███████████████| 9/9 [00:00<00:00, 584.26it/s, loss=2.42]
Epoch[1226/3000]: 100%|███████████████| 9/9 [00:00<00:00, 564.11it/s, loss=2.11]Epoch [1226/3000]: Train loss: 2.2463, Valid loss: 2.5812Epoch[1227/3000]: 100%|███████████████| 9/9 [00:00<00:00, 568.00it/s, loss=2.75]
Epoch[1228/3000]: 100%|███████████████| 9/9 [00:00<00:00, 545.56it/s, loss=3.19]
Epoch[1229/3000]: 100%|███████████████| 9/9 [00:00<00:00, 548.05it/s, loss=2.27]
Epoch[1230/3000]: 100%|███████████████| 9/9 [00:00<00:00, 621.11it/s, loss=1.68]
Epoch[1231/3000]: 100%|███████████████| 9/9 [00:00<00:00, 504.09it/s, loss=1.52]Epoch [1231/3000]: Train loss: 2.0155, Valid loss: 2.0492Epoch[1232/3000]: 100%|███████████████| 9/9 [00:00<00:00, 542.23it/s, loss=2.12]
Epoch[1233/3000]: 100%|███████████████| 9/9 [00:00<00:00, 521.43it/s, loss=2.19]
Epoch[1234/3000]: 100%|███████████████| 9/9 [00:00<00:00, 538.31it/s, loss=2.14]
Epoch[1235/3000]: 100%|███████████████| 9/9 [00:00<00:00, 622.07it/s, loss=1.47]
Epoch[1236/3000]: 100%|███████████████| 9/9 [00:00<00:00, 538.24it/s, loss=1.72]Epoch [1236/3000]: Train loss: 1.8716, Valid loss: 2.1906Epoch[1237/3000]: 100%|███████████████| 9/9 [00:00<00:00, 546.71it/s, loss=1.23]
Epoch[1238/3000]: 100%|███████████████| 9/9 [00:00<00:00, 551.57it/s, loss=1.74]
Epoch[1239/3000]: 100%|███████████████| 9/9 [00:00<00:00, 551.91it/s, loss=3.81]
Epoch[1240/3000]: 100%|████████████████| 9/9 [00:00<00:00, 610.96it/s, loss=2.1]
Epoch[1241/3000]: 100%|███████████████| 9/9 [00:00<00:00, 553.86it/s, loss=2.69]Epoch [1241/3000]: Train loss: 2.0241, Valid loss: 2.5140Epoch[1242/3000]: 100%|███████████████| 9/9 [00:00<00:00, 540.57it/s, loss=1.71]
Epoch[1243/3000]: 100%|███████████████| 9/9 [00:00<00:00, 546.48it/s, loss=1.96]
Epoch[1244/3000]: 100%|███████████████| 9/9 [00:00<00:00, 551.26it/s, loss=2.09]
Epoch[1245/3000]: 100%|████████████████| 9/9 [00:00<00:00, 596.78it/s, loss=2.2]
Epoch[1246/3000]: 100%|███████████████| 9/9 [00:00<00:00, 555.86it/s, loss=1.66]Epoch [1246/3000]: Train loss: 1.9275, Valid loss: 2.4996Epoch[1247/3000]: 100%|███████████████| 9/9 [00:00<00:00, 544.53it/s, loss=2.09]
Epoch[1248/3000]: 100%|███████████████| 9/9 [00:00<00:00, 559.47it/s, loss=2.71]
Epoch[1249/3000]: 100%|███████████████| 9/9 [00:00<00:00, 528.16it/s, loss=2.45]
Epoch[1250/3000]: 100%|███████████████| 9/9 [00:00<00:00, 598.72it/s, loss=2.69]
Epoch[1251/3000]: 100%|███████████████| 9/9 [00:00<00:00, 549.41it/s, loss=1.36]Epoch [1251/3000]: Train loss: 2.0453, Valid loss: 2.0993Epoch[1252/3000]: 100%|███████████████| 9/9 [00:00<00:00, 533.23it/s, loss=2.07]
Epoch[1253/3000]: 100%|███████████████| 9/9 [00:00<00:00, 591.72it/s, loss=2.85]
Epoch[1254/3000]: 100%|███████████████| 9/9 [00:00<00:00, 552.58it/s, loss=1.95]
Epoch[1255/3000]: 100%|███████████████| 9/9 [00:00<00:00, 617.80it/s, loss=3.59]
Epoch[1256/3000]: 100%|███████████████| 9/9 [00:00<00:00, 462.87it/s, loss=1.94]Epoch [1256/3000]: Train loss: 2.3875, Valid loss: 2.4607Epoch[1257/3000]: 100%|████████████████| 9/9 [00:00<00:00, 505.02it/s, loss=1.8]
Epoch[1258/3000]: 100%|███████████████| 9/9 [00:00<00:00, 559.15it/s, loss=1.56]
Epoch[1259/3000]: 100%|███████████████| 9/9 [00:00<00:00, 563.10it/s, loss=1.69]
Epoch[1260/3000]: 100%|███████████████| 9/9 [00:00<00:00, 627.45it/s, loss=1.65]
Epoch[1261/3000]: 100%|███████████████| 9/9 [00:00<00:00, 565.62it/s, loss=1.32]Epoch [1261/3000]: Train loss: 1.8494, Valid loss: 2.1600Epoch[1262/3000]: 100%|███████████████| 9/9 [00:00<00:00, 525.60it/s, loss=2.76]
Epoch[1263/3000]: 100%|███████████████| 9/9 [00:00<00:00, 588.22it/s, loss=3.14]
Epoch[1264/3000]: 100%|███████████████| 9/9 [00:00<00:00, 578.29it/s, loss=2.12]
Epoch[1265/3000]: 100%|███████████████| 9/9 [00:00<00:00, 626.87it/s, loss=1.55]
Epoch[1266/3000]: 100%|███████████████| 9/9 [00:00<00:00, 566.46it/s, loss=1.48]Epoch [1266/3000]: Train loss: 1.9145, Valid loss: 2.1119Epoch[1267/3000]: 100%|███████████████| 9/9 [00:00<00:00, 540.70it/s, loss=3.06]
Epoch[1268/3000]: 100%|███████████████| 9/9 [00:00<00:00, 551.47it/s, loss=1.35]
Epoch[1269/3000]: 100%|███████████████| 9/9 [00:00<00:00, 537.63it/s, loss=2.08]
Epoch[1270/3000]: 100%|███████████████| 9/9 [00:00<00:00, 597.78it/s, loss=2.85]
Epoch[1271/3000]: 100%|███████████████| 9/9 [00:00<00:00, 629.36it/s, loss=2.18]Epoch [1271/3000]: Train loss: 2.6121, Valid loss: 2.3414Epoch[1272/3000]: 100%|███████████████| 9/9 [00:00<00:00, 513.13it/s, loss=2.26]
Epoch[1273/3000]: 100%|██████████████████| 9/9 [00:00<00:00, 494.51it/s, loss=4]
Epoch[1274/3000]: 100%|███████████████| 9/9 [00:00<00:00, 562.67it/s, loss=1.44]
Epoch[1275/3000]: 100%|███████████████| 9/9 [00:00<00:00, 562.72it/s, loss=2.15]
Epoch[1276/3000]: 100%|████████████████| 9/9 [00:00<00:00, 615.21it/s, loss=1.7]Epoch [1276/3000]: Train loss: 1.9510, Valid loss: 2.3598Epoch[1277/3000]: 100%|███████████████| 9/9 [00:00<00:00, 485.80it/s, loss=1.68]
Epoch[1278/3000]: 100%|███████████████| 9/9 [00:00<00:00, 540.58it/s, loss=2.13]
Epoch[1279/3000]: 100%|███████████████| 9/9 [00:00<00:00, 532.29it/s, loss=1.64]
Epoch[1280/3000]: 100%|███████████████| 9/9 [00:00<00:00, 569.61it/s, loss=1.48]
IOPub message rate exceeded.
The notebook server will temporarily stop sending output
to the client in order to avoid crashing it.
To change this limit, set the config variable
`--NotebookApp.iopub_msg_rate_limit`.Current values:
NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)
NotebookApp.rate_limit_window=3.0 (secs)Epoch[1324/3000]: 100%|███████████████| 9/9 [00:00<00:00, 670.53it/s, loss=2.25]
Epoch[1325/3000]: 100%|███████████████| 9/9 [00:00<00:00, 527.31it/s, loss=1.98]
Epoch[1326/3000]: 100%|███████████████| 9/9 [00:00<00:00, 547.32it/s, loss=1.39]Epoch [1326/3000]: Train loss: 2.0875, Valid loss: 1.8407Epoch[1327/3000]: 100%|███████████████| 9/9 [00:00<00:00, 604.31it/s, loss=1.71]
Epoch[1328/3000]: 100%|███████████████| 9/9 [00:00<00:00, 583.51it/s, loss=2.31]
Epoch[1329/3000]: 100%|███████████████| 9/9 [00:00<00:00, 584.00it/s, loss=1.66]
Epoch[1330/3000]: 100%|███████████████| 9/9 [00:00<00:00, 568.75it/s, loss=1.55]
Epoch[1331/3000]: 100%|███████████████| 9/9 [00:00<00:00, 633.75it/s, loss=2.61]Epoch [1331/3000]: Train loss: 2.0345, Valid loss: 3.9449Epoch[1332/3000]: 100%|███████████████| 9/9 [00:00<00:00, 581.92it/s, loss=1.92]
Epoch[1333/3000]: 100%|███████████████| 9/9 [00:00<00:00, 535.84it/s, loss=3.69]
Epoch[1334/3000]: 100%|███████████████| 9/9 [00:00<00:00, 576.25it/s, loss=2.04]
Epoch[1335/3000]: 100%|███████████████| 9/9 [00:00<00:00, 620.99it/s, loss=1.91]
Epoch[1336/3000]: 100%|███████████████| 9/9 [00:00<00:00, 581.88it/s, loss=1.78]Epoch [1336/3000]: Train loss: 2.1853, Valid loss: 2.2201Epoch[1337/3000]: 100%|███████████████| 9/9 [00:00<00:00, 543.74it/s, loss=1.96]
Epoch[1338/3000]: 100%|███████████████| 9/9 [00:00<00:00, 575.63it/s, loss=2.28]
Epoch[1339/3000]: 100%|███████████████| 9/9 [00:00<00:00, 556.36it/s, loss=1.92]
Epoch[1340/3000]: 100%|███████████████| 9/9 [00:00<00:00, 576.67it/s, loss=1.55]
Epoch[1341/3000]: 100%|███████████████| 9/9 [00:00<00:00, 602.58it/s, loss=2.03]Epoch [1341/3000]: Train loss: 2.0533, Valid loss: 2.1898Epoch[1342/3000]: 100%|███████████████| 9/9 [00:00<00:00, 521.69it/s, loss=2.55]
Epoch[1343/3000]: 100%|███████████████| 9/9 [00:00<00:00, 563.59it/s, loss=2.15]
Epoch[1344/3000]: 100%|███████████████| 9/9 [00:00<00:00, 553.57it/s, loss=3.42]
Epoch[1345/3000]: 100%|████████████████| 9/9 [00:00<00:00, 553.86it/s, loss=3.8]
Epoch[1346/3000]: 100%|███████████████| 9/9 [00:00<00:00, 636.10it/s, loss=3.14]Epoch [1346/3000]: Train loss: 2.5571, Valid loss: 1.9337Epoch[1347/3000]: 100%|███████████████| 9/9 [00:00<00:00, 343.35it/s, loss=1.95]
Epoch[1348/3000]: 100%|███████████████| 9/9 [00:00<00:00, 524.60it/s, loss=2.69]
Epoch[1349/3000]: 100%|███████████████| 9/9 [00:00<00:00, 577.62it/s, loss=2.58]
Epoch[1350/3000]: 100%|███████████████| 9/9 [00:00<00:00, 574.32it/s, loss=2.24]
Epoch[1351/3000]: 100%|███████████████| 9/9 [00:00<00:00, 637.40it/s, loss=2.39]Epoch [1351/3000]: Train loss: 2.2778, Valid loss: 2.5843Epoch[1352/3000]: 100%|████████████████| 9/9 [00:00<00:00, 526.34it/s, loss=2.5]
Epoch[1353/3000]: 100%|████████████████| 9/9 [00:00<00:00, 575.70it/s, loss=2.4]
Epoch[1354/3000]: 100%|███████████████| 9/9 [00:00<00:00, 564.82it/s, loss=2.07]
Epoch[1355/3000]: 100%|███████████████| 9/9 [00:00<00:00, 577.75it/s, loss=1.87]
Epoch[1356/3000]: 100%|███████████████| 9/9 [00:00<00:00, 637.00it/s, loss=2.03]Epoch [1356/3000]: Train loss: 2.0856, Valid loss: 2.5915Epoch[1357/3000]: 100%|███████████████| 9/9 [00:00<00:00, 569.73it/s, loss=2.92]
Epoch[1358/3000]: 100%|███████████████| 9/9 [00:00<00:00, 574.13it/s, loss=2.31]
Epoch[1359/3000]: 100%|███████████████| 9/9 [00:00<00:00, 589.97it/s, loss=2.17]
Epoch[1360/3000]: 100%|███████████████| 9/9 [00:00<00:00, 580.87it/s, loss=2.27]
Epoch[1361/3000]: 100%|███████████████| 9/9 [00:00<00:00, 619.96it/s, loss=2.46]Epoch [1361/3000]: Train loss: 2.1678, Valid loss: 2.2656Epoch[1362/3000]: 100%|███████████████| 9/9 [00:00<00:00, 610.49it/s, loss=1.75]
Epoch[1363/3000]: 100%|███████████████| 9/9 [00:00<00:00, 543.90it/s, loss=1.97]
Epoch[1364/3000]: 100%|███████████████| 9/9 [00:00<00:00, 558.93it/s, loss=2.09]
Epoch[1365/3000]: 100%|███████████████| 9/9 [00:00<00:00, 554.88it/s, loss=2.09]
Epoch[1366/3000]: 100%|███████████████| 9/9 [00:00<00:00, 553.64it/s, loss=1.87]Epoch [1366/3000]: Train loss: 2.1771, Valid loss: 3.0970Epoch[1367/3000]: 100%|████████████████| 9/9 [00:00<00:00, 605.58it/s, loss=2.2]
Epoch[1368/3000]: 100%|███████████████| 9/9 [00:00<00:00, 530.00it/s, loss=2.02]
Epoch[1369/3000]: 100%|███████████████| 9/9 [00:00<00:00, 552.97it/s, loss=1.74]
Epoch[1370/3000]: 100%|███████████████| 9/9 [00:00<00:00, 561.44it/s, loss=1.67]
Epoch[1371/3000]: 100%|███████████████| 9/9 [00:00<00:00, 555.73it/s, loss=3.08]Epoch [1371/3000]: Train loss: 2.4718, Valid loss: 3.3138Epoch[1372/3000]: 100%|███████████████| 9/9 [00:00<00:00, 617.16it/s, loss=1.79]
Epoch[1373/3000]: 100%|███████████████| 9/9 [00:00<00:00, 589.35it/s, loss=2.33]
Epoch[1374/3000]: 100%|████████████████| 9/9 [00:00<00:00, 541.65it/s, loss=2.2]
Epoch[1375/3000]: 100%|███████████████| 9/9 [00:00<00:00, 551.03it/s, loss=2.42]
Epoch[1376/3000]: 100%|███████████████| 9/9 [00:00<00:00, 546.54it/s, loss=1.92]Epoch [1376/3000]: Train loss: 2.5514, Valid loss: 2.1887Epoch[1377/3000]: 100%|███████████████| 9/9 [00:00<00:00, 112.19it/s, loss=1.88]
Epoch[1378/3000]: 100%|███████████████| 9/9 [00:00<00:00, 536.64it/s, loss=2.51]
Epoch[1379/3000]: 100%|███████████████| 9/9 [00:00<00:00, 641.84it/s, loss=2.27]
Epoch[1380/3000]: 100%|███████████████| 9/9 [00:00<00:00, 589.67it/s, loss=1.78]
Epoch[1381/3000]: 100%|███████████████| 9/9 [00:00<00:00, 578.15it/s, loss=1.97]Epoch [1381/3000]: Train loss: 1.8528, Valid loss: 2.0413Epoch[1382/3000]: 100%|███████████████| 9/9 [00:00<00:00, 573.69it/s, loss=1.51]
Epoch[1383/3000]: 100%|███████████████| 9/9 [00:00<00:00, 598.88it/s, loss=1.47]
Epoch[1384/3000]: 100%|███████████████| 9/9 [00:00<00:00, 633.54it/s, loss=2.49]
Epoch[1385/3000]: 100%|███████████████| 9/9 [00:00<00:00, 648.09it/s, loss=1.59]
Epoch[1386/3000]: 100%|████████████████| 9/9 [00:00<00:00, 536.62it/s, loss=1.7]Epoch [1386/3000]: Train loss: 1.8121, Valid loss: 2.3791Epoch[1387/3000]: 100%|███████████████| 9/9 [00:00<00:00, 504.97it/s, loss=1.24]
Epoch[1388/3000]: 100%|███████████████| 9/9 [00:00<00:00, 531.98it/s, loss=1.97]
Epoch[1389/3000]: 100%|███████████████| 9/9 [00:00<00:00, 573.92it/s, loss=1.84]
Epoch[1390/3000]: 100%|███████████████| 9/9 [00:00<00:00, 642.44it/s, loss=1.44]
Epoch[1391/3000]: 100%|████████████████| 9/9 [00:00<00:00, 508.77it/s, loss=2.2]Epoch [1391/3000]: Train loss: 2.4561, Valid loss: 2.1803Epoch[1392/3000]: 100%|███████████████| 9/9 [00:00<00:00, 526.07it/s, loss=2.15]
Epoch[1393/3000]: 100%|████████████████| 9/9 [00:00<00:00, 550.27it/s, loss=2.5]
Epoch[1394/3000]: 100%|███████████████| 9/9 [00:00<00:00, 535.78it/s, loss=2.66]
Epoch[1395/3000]: 100%|███████████████| 9/9 [00:00<00:00, 414.96it/s, loss=1.37]
Epoch[1396/3000]: 100%|███████████████| 9/9 [00:00<00:00, 506.59it/s, loss=1.69]Epoch [1396/3000]: Train loss: 1.7613, Valid loss: 1.8655Epoch[1397/3000]: 100%|████████████████| 9/9 [00:00<00:00, 560.71it/s, loss=2.7]
Epoch[1398/3000]: 100%|███████████████| 9/9 [00:00<00:00, 512.67it/s, loss=1.97]
Epoch[1399/3000]: 100%|███████████████| 9/9 [00:00<00:00, 604.32it/s, loss=1.74]Model is not improving! Stop training session!

Testing

def save_pred(preds, file):with open(file, 'w') as fp:writer = csv.writer(fp)writer.writerow(['id', 'tested_positive'])for i, p in enumerate(preds):writer.writerow([i, p])

model = NN_Model(input_dim = x_train.shape[1]).to(device)
model.load_state_dict(torch.load(config['save_path']))
preds = predict(test_loader, model, device)
save_pred(preds, 'pred.csv')

100%|████████████████████████████████████████████| 5/5 [00:00<00:00, 895.61it/s]

预测结果截图