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Age Prediction

Solution for submission 175171

A detailed solution for submission 175171 submitted for challenge Age Prediction

Sahcim
In [5]:
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In [6]:
%aicrowd login
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In [7]:
import os
import time
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import argparse
import sys

from torch.utils.data import Dataset
from torch.utils.data import DataLoader

from torchvision import transforms
from PIL import Image

TRAIN_CSV_PATH = ''
TEST_CSV_PATH = ''
IMAGE_PATH = ''
In [8]:
cuda = 1
seed = 5
numworkers = 16
outpath = ''
In [9]:
NUM_WORKERS = numworkers

if cuda >= 0:
    DEVICE = torch.device("cuda:%d" % cuda)
else:
    DEVICE = torch.device("cpu")

if seed == -1:
    RANDOM_SEED = None
else:
    RANDOM_SEED = seed

PATH = outpath
if not os.path.exists(PATH):
    os.mkdir(PATH)
LOGFILE = os.path.join(PATH, 'training.log')
TEST_PREDICTIONS = os.path.join(PATH, 'test_predictions.log')
TEST_ALLPROBAS = os.path.join(PATH, 'test_allprobas.tensor')

# Logging

header = []

header.append('PyTorch Version: %s' % torch.__version__)
header.append('CUDA device available: %s' % torch.cuda.is_available())
header.append('Using CUDA device: %s' % DEVICE)
header.append('Random Seed: %s' % RANDOM_SEED)
header.append('Output Path: %s' % PATH)
header.append('Script: %s' % sys.argv[0])

with open(LOGFILE, 'w') as f:
    for entry in header:
        print(entry)
        f.write('%s\n' % entry)
        f.flush()
PyTorch Version: 1.9.0+cu111
CUDA device available: True
Using CUDA device: cuda:1
Random Seed: 5
Output Path: /home/michal.zobniow/aiblitz/out_finalv2/
Script: /home/michal.zobniow/aura/lib/python3.9/site-packages/ipykernel_launcher.py
In [10]:
class DatasetAge(Dataset):
    """Custom Dataset for loading face images"""

    def __init__(self, csv_path, img_dir, split, transform=None):

        df = pd.read_csv(csv_path)
        self.img_dir = os.path.join(img_dir, split)
        self.image_names = df["ImageID"].values
        self.split = split
        self.csv_path = csv_path
        self.y = [int(int(age.split('-')[0])/10) for age in df['age'].values]
        self.transform = transform

    def __getitem__(self, index):
        img = cv2.imread(os.path.join(self.img_dir,
                                      self.image_names[index])+".jpg")
        if self.transform is not None:
            augmented = self.transform(image=img)
            img = augmented['image']
        if self.split != 'test':
            label = self.y[index]
            levels = [1]*label + [0]*(NUM_CLASSES - 1 - label)
            levels = torch.tensor(levels, dtype=torch.float32)

            return img, label, levels
        else:
            return img, self.image_names[index]

    def __len__(self):
        return len(self.y)
In [11]:
import albumentations
import albumentations as A
import cv2
import numpy as np
import torch
from albumentations.pytorch.transforms import ToTensorV2

train_transforms = A.Compose([
    A.HorizontalFlip(),
    A.Rotate(limit=15, p=0.7, interpolation=cv2.INTER_AREA, border_mode=cv2.BORDER_CONSTANT, value=(0, 0, 0)),
    A.Cutout(8, 138, 138, p=0.7),
    A.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225],
    ),
    ToTensorV2()
])

BATCH_SIZE = 4
NUM_CLASSES =10

val_transforms = A.Compose([
            A.Normalize(
                    mean=[0.485, 0.456, 0.406],
                    std=[0.229, 0.224, 0.225],
                ),
            ToTensorV2()])

train_dataset = DatasetAge(csv_path=TRAIN_CSV_PATH,
                               img_dir=IMAGE_PATH,
                               split="train",
                               transform=train_transforms)

train_loader = DataLoader(dataset=train_dataset,
                          batch_size=BATCH_SIZE,
                          shuffle=True,
                          num_workers=NUM_WORKERS)
In [12]:
from efficientnet_pytorch import EfficientNet

class AgeModel(nn.Module):

    def __init__(self, num_classes):
        super(AgeModel, self).__init__()
        self.num_classes = num_classes
        self.model = EfficientNet.from_pretrained('efficientnet-b6')
        self.adpool = torch.nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Linear(2304, 1, bias=False)
        self.linear_1_bias = nn.Parameter(torch.zeros(self.num_classes-1).float())

    def forward(self, x):
        x = self.model.extract_features(x)
        x = self.adpool(x)
        x = x.view(x.size(0), -1)
        logits = self.fc(x)
        logits = logits + self.linear_1_bias
        probas = torch.sigmoid(logits)
        return logits, probas
In [13]:
from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts
from torch.optim import SGD
In [14]:
lr = 0.001
momentum=0.9
weight_decay=0.0001
num_epochs=200
In [ ]:
def cost_fn(logits, levels):
    val = (-torch.sum((F.logsigmoid(logits)*levels
                      + (F.logsigmoid(logits) - logits)*(1-levels)),
           dim=1))
    return torch.mean(val)

torch.manual_seed(RANDOM_SEED)
torch.cuda.manual_seed(RANDOM_SEED)
model = AgeModel(NUM_CLASSES)

model.to(DEVICE)

optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=momentum, weight_decay=weight_decay)
scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=50, T_mult=1, eta_min=0.00001, last_epoch=-1)

def compute_mae_and_mse_and_accuracy(model, data_loader, device):
    mae, mse, accuracy, num_examples = 0, 0, 0, 0
    for i, (features, targets, levels) in enumerate(data_loader):

        features = features.to(device)
        targets = targets.to(device)

        logits, probas = model(features)
        predict_levels = probas > 0.5
        predicted_labels = torch.sum(predict_levels, dim=1)
        num_examples += targets.size(0)
        mae += torch.sum(torch.abs(predicted_labels - targets))
        mse += torch.sum((predicted_labels - targets)**2)
        accuracy += torch.sum(predicted_labels == targets)
    mae = mae.float() / num_examples
    mse = mse.float() / num_examples
    accuracy = accuracy.float() / num_examples
    return mae, mse, accuracy


start_time = time.time()

best_mae, best_rmse, best_epoch = 999, 999, -1
for epoch in range(num_epochs):

    model.train()
    for batch_idx, (features, targets, levels) in enumerate(train_loader):

        features = features.to(DEVICE)
        targets = targets
        targets = targets.to(DEVICE)
        levels = levels.to(DEVICE)

        # FORWARD AND BACK PROP
        logits, probas = model(features)
        cost = cost_fn(logits, levels)
        optimizer.zero_grad()

        cost.backward()

        # UPDATE MODEL PARAMETERS
        optimizer.step()

        # LOGGING
        if not batch_idx % 50:
            s = ('Epoch: %03d/%03d | Batch %04d/%04d | Cost: %.4f'
                 % (epoch+1, num_epochs, batch_idx,
                     len(train_dataset)//BATCH_SIZE, cost))
            print(s)
            with open(LOGFILE, 'a') as f:
                f.write('%s\n' % s)
    scheduler.step()
    model.eval()
    with torch.set_grad_enabled(False):
        valid_mae, valid_mse, valid_accuracy = compute_mae_and_mse_and_accuracy(model, train_loader,
                                                   device=DEVICE)

    if valid_mae < best_mae:
        best_mae, best_rmse, best_epoch, best_accuracy = valid_mae, torch.sqrt(valid_mse), epoch, valid_accuracy
        ########## SAVE MODEL #############
        torch.save(model.state_dict(), os.path.join(PATH, 'best_model.pt'))


    s = 'MAE/RMSE/ACCURACY: | Current Valid: %.2f/%.2f/%.2f Ep. %d | Best Valid : %.2f/%.2f/%.2f Ep. %d' % (
        valid_mae, torch.sqrt(valid_mse),valid_accuracy, epoch, best_mae, best_rmse, best_accuracy, best_epoch)
    print(s)
    with open(LOGFILE, 'a') as f:
        f.write('%s\n' % s)

    s = 'Time elapsed: %.2f min' % ((time.time() - start_time)/60)
    print(s)
    with open(LOGFILE, 'a') as f:
        f.write('%s\n' % s)
Loaded pretrained weights for efficientnet-b6
Epoch: 001/200 | Batch 0000/1500 | Cost: 6.2667
Epoch: 001/200 | Batch 0050/1500 | Cost: 5.3987
Epoch: 001/200 | Batch 0100/1500 | Cost: 4.1580
Epoch: 001/200 | Batch 0150/1500 | Cost: 4.3337
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Epoch: 001/200 | Batch 0250/1500 | Cost: 2.9786
Epoch: 001/200 | Batch 0300/1500 | Cost: 1.3488
Epoch: 001/200 | Batch 0350/1500 | Cost: 3.7009
Epoch: 001/200 | Batch 0400/1500 | Cost: 2.8217
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Epoch: 001/200 | Batch 1050/1500 | Cost: 2.8674
Epoch: 001/200 | Batch 1100/1500 | Cost: 1.4793
Epoch: 001/200 | Batch 1150/1500 | Cost: 1.5293
Epoch: 001/200 | Batch 1200/1500 | Cost: 0.9813
Epoch: 001/200 | Batch 1250/1500 | Cost: 2.4903
Epoch: 001/200 | Batch 1300/1500 | Cost: 2.3255
Epoch: 001/200 | Batch 1350/1500 | Cost: 3.3853
Epoch: 001/200 | Batch 1400/1500 | Cost: 2.6060
Epoch: 001/200 | Batch 1450/1500 | Cost: 2.2771
MAE/RMSE/ACCURACY: | Current Valid: 0.64/0.97/0.49 Ep. 0 | Best Valid : 0.64/0.97/0.49 Ep. 0
Time elapsed: 11.34 min
Epoch: 002/200 | Batch 0000/1500 | Cost: 2.3940
Epoch: 002/200 | Batch 0050/1500 | Cost: 1.6543
Epoch: 002/200 | Batch 0100/1500 | Cost: 1.6536
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Epoch: 002/200 | Batch 1100/1500 | Cost: 2.4132
Epoch: 002/200 | Batch 1150/1500 | Cost: 1.4428
Epoch: 002/200 | Batch 1200/1500 | Cost: 1.2692
Epoch: 002/200 | Batch 1250/1500 | Cost: 1.8306
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Epoch: 002/200 | Batch 1350/1500 | Cost: 3.3025
Epoch: 002/200 | Batch 1400/1500 | Cost: 1.8129
Epoch: 002/200 | Batch 1450/1500 | Cost: 2.0207
MAE/RMSE/ACCURACY: | Current Valid: 0.43/0.71/0.61 Ep. 1 | Best Valid : 0.43/0.71/0.61 Ep. 1
Time elapsed: 22.72 min
Epoch: 003/200 | Batch 0000/1500 | Cost: 1.6410
Epoch: 003/200 | Batch 0050/1500 | Cost: 1.3592
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Epoch: 003/200 | Batch 1000/1500 | Cost: 1.8815
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Epoch: 003/200 | Batch 1200/1500 | Cost: 1.0041
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Epoch: 003/200 | Batch 1300/1500 | Cost: 1.2875
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Epoch: 003/200 | Batch 1400/1500 | Cost: 1.5500
Epoch: 003/200 | Batch 1450/1500 | Cost: 1.7723
MAE/RMSE/ACCURACY: | Current Valid: 0.58/0.85/0.48 Ep. 2 | Best Valid : 0.43/0.71/0.61 Ep. 1
Time elapsed: 34.11 min
Epoch: 004/200 | Batch 0000/1500 | Cost: 1.3117
Epoch: 004/200 | Batch 0050/1500 | Cost: 1.8815
Epoch: 004/200 | Batch 0100/1500 | Cost: 2.0193
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Epoch: 004/200 | Batch 1300/1500 | Cost: 0.6213
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Epoch: 004/200 | Batch 1400/1500 | Cost: 1.6074
Epoch: 004/200 | Batch 1450/1500 | Cost: 1.1651
MAE/RMSE/ACCURACY: | Current Valid: 0.35/0.62/0.67 Ep. 3 | Best Valid : 0.35/0.62/0.67 Ep. 3
Time elapsed: 45.50 min
Epoch: 005/200 | Batch 0000/1500 | Cost: 1.4198
Epoch: 005/200 | Batch 0050/1500 | Cost: 1.0570
Epoch: 005/200 | Batch 0100/1500 | Cost: 1.1474
Epoch: 005/200 | Batch 0150/1500 | Cost: 1.2011
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Epoch: 005/200 | Batch 0500/1500 | Cost: 1.0065
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Epoch: 005/200 | Batch 1000/1500 | Cost: 1.6520
Epoch: 005/200 | Batch 1050/1500 | Cost: 1.1224
Epoch: 005/200 | Batch 1100/1500 | Cost: 1.4284
Epoch: 005/200 | Batch 1150/1500 | Cost: 1.2013
Epoch: 005/200 | Batch 1200/1500 | Cost: 1.0444
Epoch: 005/200 | Batch 1250/1500 | Cost: 0.8012
Epoch: 005/200 | Batch 1300/1500 | Cost: 1.5566
Epoch: 005/200 | Batch 1350/1500 | Cost: 0.9188
Epoch: 005/200 | Batch 1400/1500 | Cost: 1.4623
Epoch: 005/200 | Batch 1450/1500 | Cost: 0.9740
MAE/RMSE/ACCURACY: | Current Valid: 0.34/0.61/0.67 Ep. 4 | Best Valid : 0.34/0.61/0.67 Ep. 4
Time elapsed: 56.88 min
Epoch: 006/200 | Batch 0000/1500 | Cost: 1.0951
Epoch: 006/200 | Batch 0050/1500 | Cost: 0.6303
Epoch: 006/200 | Batch 0100/1500 | Cost: 1.0992
Epoch: 006/200 | Batch 0150/1500 | Cost: 1.1395
Epoch: 006/200 | Batch 0200/1500 | Cost: 1.0264
Epoch: 006/200 | Batch 0250/1500 | Cost: 1.3133
Epoch: 006/200 | Batch 0300/1500 | Cost: 0.8998
Epoch: 006/200 | Batch 0350/1500 | Cost: 1.8916
Epoch: 006/200 | Batch 0400/1500 | Cost: 0.6497
Epoch: 006/200 | Batch 0450/1500 | Cost: 0.9942
Epoch: 006/200 | Batch 0500/1500 | Cost: 0.8815
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Epoch: 006/200 | Batch 0900/1500 | Cost: 1.5820
Epoch: 006/200 | Batch 0950/1500 | Cost: 1.6708
Epoch: 006/200 | Batch 1000/1500 | Cost: 1.3390
Epoch: 006/200 | Batch 1050/1500 | Cost: 1.6583
Epoch: 006/200 | Batch 1100/1500 | Cost: 1.2774
Epoch: 006/200 | Batch 1150/1500 | Cost: 0.9357
Epoch: 006/200 | Batch 1200/1500 | Cost: 1.0118
Epoch: 006/200 | Batch 1250/1500 | Cost: 0.8798
Epoch: 006/200 | Batch 1300/1500 | Cost: 1.1207
Epoch: 006/200 | Batch 1350/1500 | Cost: 0.6203
Epoch: 006/200 | Batch 1400/1500 | Cost: 0.9617
Epoch: 006/200 | Batch 1450/1500 | Cost: 1.3394
MAE/RMSE/ACCURACY: | Current Valid: 0.28/0.55/0.73 Ep. 5 | Best Valid : 0.28/0.55/0.73 Ep. 5
Time elapsed: 68.24 min
Epoch: 007/200 | Batch 0000/1500 | Cost: 0.6953
Epoch: 007/200 | Batch 0050/1500 | Cost: 1.3022
Epoch: 007/200 | Batch 0100/1500 | Cost: 1.4460
Epoch: 007/200 | Batch 0150/1500 | Cost: 1.1264
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Epoch: 007/200 | Batch 0300/1500 | Cost: 1.0372
Epoch: 007/200 | Batch 0350/1500 | Cost: 1.2270
Epoch: 007/200 | Batch 0400/1500 | Cost: 1.4308
Epoch: 007/200 | Batch 0450/1500 | Cost: 0.7160
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Epoch: 007/200 | Batch 1000/1500 | Cost: 0.8125
Epoch: 007/200 | Batch 1050/1500 | Cost: 0.8662
Epoch: 007/200 | Batch 1100/1500 | Cost: 0.9374
Epoch: 007/200 | Batch 1150/1500 | Cost: 1.2742
Epoch: 007/200 | Batch 1200/1500 | Cost: 0.6870
Epoch: 007/200 | Batch 1250/1500 | Cost: 0.5415
Epoch: 007/200 | Batch 1300/1500 | Cost: 0.6802
Epoch: 007/200 | Batch 1350/1500 | Cost: 0.5171
Epoch: 007/200 | Batch 1400/1500 | Cost: 1.0099
Epoch: 007/200 | Batch 1450/1500 | Cost: 0.8548
MAE/RMSE/ACCURACY: | Current Valid: 0.29/0.55/0.72 Ep. 6 | Best Valid : 0.28/0.55/0.73 Ep. 5
Time elapsed: 79.61 min
Epoch: 008/200 | Batch 0000/1500 | Cost: 1.1758
Epoch: 008/200 | Batch 0050/1500 | Cost: 0.7954
Epoch: 008/200 | Batch 0100/1500 | Cost: 1.5911
Epoch: 008/200 | Batch 0150/1500 | Cost: 1.1034
Epoch: 008/200 | Batch 0200/1500 | Cost: 1.0045
Epoch: 008/200 | Batch 0250/1500 | Cost: 1.1391
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Epoch: 008/200 | Batch 0450/1500 | Cost: 1.1029
Epoch: 008/200 | Batch 0500/1500 | Cost: 1.8482
Epoch: 008/200 | Batch 0550/1500 | Cost: 0.5814
Epoch: 008/200 | Batch 0600/1500 | Cost: 1.0846
Epoch: 008/200 | Batch 0650/1500 | Cost: 1.0729
Epoch: 008/200 | Batch 0700/1500 | Cost: 0.9325
Epoch: 008/200 | Batch 0750/1500 | Cost: 1.4422
Epoch: 008/200 | Batch 0800/1500 | Cost: 1.5651
Epoch: 008/200 | Batch 0850/1500 | Cost: 0.6237
Epoch: 008/200 | Batch 0900/1500 | Cost: 1.9212
Epoch: 008/200 | Batch 0950/1500 | Cost: 0.8874
Epoch: 008/200 | Batch 1000/1500 | Cost: 1.2977
Epoch: 008/200 | Batch 1050/1500 | Cost: 1.0800
Epoch: 008/200 | Batch 1100/1500 | Cost: 1.0705
Epoch: 008/200 | Batch 1150/1500 | Cost: 2.1280
Epoch: 008/200 | Batch 1200/1500 | Cost: 0.5802
Epoch: 008/200 | Batch 1250/1500 | Cost: 1.0587
Epoch: 008/200 | Batch 1300/1500 | Cost: 0.7288
Epoch: 008/200 | Batch 1350/1500 | Cost: 1.3603
Epoch: 008/200 | Batch 1400/1500 | Cost: 0.7984
Epoch: 008/200 | Batch 1450/1500 | Cost: 1.1053
MAE/RMSE/ACCURACY: | Current Valid: 0.23/0.49/0.78 Ep. 7 | Best Valid : 0.23/0.49/0.78 Ep. 7
Time elapsed: 91.04 min
Epoch: 009/200 | Batch 0000/1500 | Cost: 0.7327
Epoch: 009/200 | Batch 0050/1500 | Cost: 0.8817
Epoch: 009/200 | Batch 0100/1500 | Cost: 1.0298
Epoch: 009/200 | Batch 0150/1500 | Cost: 0.3904
Epoch: 009/200 | Batch 0200/1500 | Cost: 1.1569
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Epoch: 009/200 | Batch 0400/1500 | Cost: 1.2713
Epoch: 009/200 | Batch 0450/1500 | Cost: 1.0607
Epoch: 009/200 | Batch 0500/1500 | Cost: 1.3043
Epoch: 009/200 | Batch 0550/1500 | Cost: 0.6275
Epoch: 009/200 | Batch 0600/1500 | Cost: 1.1402
Epoch: 009/200 | Batch 0650/1500 | Cost: 1.1561
Epoch: 009/200 | Batch 0700/1500 | Cost: 0.8567
Epoch: 009/200 | Batch 0750/1500 | Cost: 1.0305
Epoch: 009/200 | Batch 0800/1500 | Cost: 1.0667
Epoch: 009/200 | Batch 0850/1500 | Cost: 0.6089
Epoch: 009/200 | Batch 0900/1500 | Cost: 0.7945
Epoch: 009/200 | Batch 0950/1500 | Cost: 1.3089
Epoch: 009/200 | Batch 1000/1500 | Cost: 1.0175
Epoch: 009/200 | Batch 1050/1500 | Cost: 0.8514
Epoch: 009/200 | Batch 1100/1500 | Cost: 0.7617
Epoch: 009/200 | Batch 1150/1500 | Cost: 0.9538
Epoch: 009/200 | Batch 1200/1500 | Cost: 0.4103
Epoch: 009/200 | Batch 1250/1500 | Cost: 0.6535
Epoch: 009/200 | Batch 1300/1500 | Cost: 0.6013
Epoch: 009/200 | Batch 1350/1500 | Cost: 0.4742
Epoch: 009/200 | Batch 1400/1500 | Cost: 1.0963
Epoch: 009/200 | Batch 1450/1500 | Cost: 1.0523
MAE/RMSE/ACCURACY: | Current Valid: 0.25/0.51/0.76 Ep. 8 | Best Valid : 0.23/0.49/0.78 Ep. 7
Time elapsed: 102.39 min
Epoch: 010/200 | Batch 0000/1500 | Cost: 0.3490
Epoch: 010/200 | Batch 0050/1500 | Cost: 0.6077
Epoch: 010/200 | Batch 0100/1500 | Cost: 0.1332
Epoch: 010/200 | Batch 0150/1500 | Cost: 1.0140
Epoch: 010/200 | Batch 0200/1500 | Cost: 1.3829
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Epoch: 010/200 | Batch 0400/1500 | Cost: 0.8810
Epoch: 010/200 | Batch 0450/1500 | Cost: 0.5306
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Epoch: 010/200 | Batch 0550/1500 | Cost: 0.7673
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Epoch: 010/200 | Batch 0650/1500 | Cost: 0.3879
Epoch: 010/200 | Batch 0700/1500 | Cost: 0.5214
Epoch: 010/200 | Batch 0750/1500 | Cost: 1.0997
Epoch: 010/200 | Batch 0800/1500 | Cost: 0.9025
Epoch: 010/200 | Batch 0850/1500 | Cost: 0.8491
Epoch: 010/200 | Batch 0900/1500 | Cost: 0.6977
Epoch: 010/200 | Batch 0950/1500 | Cost: 0.4429
Epoch: 010/200 | Batch 1000/1500 | Cost: 0.7808
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Epoch: 010/200 | Batch 1100/1500 | Cost: 0.5487
Epoch: 010/200 | Batch 1150/1500 | Cost: 1.0994
Epoch: 010/200 | Batch 1200/1500 | Cost: 0.9693
Epoch: 010/200 | Batch 1250/1500 | Cost: 0.7951
Epoch: 010/200 | Batch 1300/1500 | Cost: 0.7117
Epoch: 010/200 | Batch 1350/1500 | Cost: 0.8788
Epoch: 010/200 | Batch 1400/1500 | Cost: 0.7846
Epoch: 010/200 | Batch 1450/1500 | Cost: 0.8126
MAE/RMSE/ACCURACY: | Current Valid: 0.20/0.46/0.80 Ep. 9 | Best Valid : 0.20/0.46/0.80 Ep. 9
Time elapsed: 113.78 min
Epoch: 011/200 | Batch 0000/1500 | Cost: 1.1548
Epoch: 011/200 | Batch 0050/1500 | Cost: 0.5902
Epoch: 011/200 | Batch 0100/1500 | Cost: 0.7698
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Epoch: 011/200 | Batch 0400/1500 | Cost: 0.8752
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Epoch: 011/200 | Batch 0500/1500 | Cost: 0.8947
Epoch: 011/200 | Batch 0550/1500 | Cost: 0.9732
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Epoch: 011/200 | Batch 0650/1500 | Cost: 1.0815
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Epoch: 011/200 | Batch 0750/1500 | Cost: 0.8816
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Epoch: 011/200 | Batch 0850/1500 | Cost: 0.7338
Epoch: 011/200 | Batch 0900/1500 | Cost: 0.9092
Epoch: 011/200 | Batch 0950/1500 | Cost: 0.7397
Epoch: 011/200 | Batch 1000/1500 | Cost: 0.5666
Epoch: 011/200 | Batch 1050/1500 | Cost: 1.0979
Epoch: 011/200 | Batch 1100/1500 | Cost: 0.9313
Epoch: 011/200 | Batch 1150/1500 | Cost: 1.2998
Epoch: 011/200 | Batch 1200/1500 | Cost: 0.6610
Epoch: 011/200 | Batch 1250/1500 | Cost: 0.8083
Epoch: 011/200 | Batch 1300/1500 | Cost: 0.8758
Epoch: 011/200 | Batch 1350/1500 | Cost: 0.9279
Epoch: 011/200 | Batch 1400/1500 | Cost: 0.7858
Epoch: 011/200 | Batch 1450/1500 | Cost: 1.0599
MAE/RMSE/ACCURACY: | Current Valid: 0.18/0.43/0.82 Ep. 10 | Best Valid : 0.18/0.43/0.82 Ep. 10
Time elapsed: 125.14 min
Epoch: 012/200 | Batch 0000/1500 | Cost: 0.8985
Epoch: 012/200 | Batch 0050/1500 | Cost: 0.6211
Epoch: 012/200 | Batch 0100/1500 | Cost: 0.9756
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Epoch: 012/200 | Batch 0400/1500 | Cost: 0.9471
Epoch: 012/200 | Batch 0450/1500 | Cost: 0.4729
Epoch: 012/200 | Batch 0500/1500 | Cost: 0.5824
Epoch: 012/200 | Batch 0550/1500 | Cost: 0.6028
Epoch: 012/200 | Batch 0600/1500 | Cost: 0.9681
Epoch: 012/200 | Batch 0650/1500 | Cost: 2.1702
Epoch: 012/200 | Batch 0700/1500 | Cost: 1.7792
Epoch: 012/200 | Batch 0750/1500 | Cost: 1.2717
Epoch: 012/200 | Batch 0800/1500 | Cost: 1.1872
Epoch: 012/200 | Batch 0850/1500 | Cost: 0.8509
Epoch: 012/200 | Batch 0900/1500 | Cost: 1.0214
Epoch: 012/200 | Batch 0950/1500 | Cost: 0.5876
Epoch: 012/200 | Batch 1000/1500 | Cost: 0.9550
Epoch: 012/200 | Batch 1050/1500 | Cost: 0.6638
Epoch: 012/200 | Batch 1100/1500 | Cost: 0.8051
Epoch: 012/200 | Batch 1150/1500 | Cost: 0.7511
Epoch: 012/200 | Batch 1200/1500 | Cost: 0.8754
Epoch: 012/200 | Batch 1250/1500 | Cost: 1.1337
Epoch: 012/200 | Batch 1300/1500 | Cost: 0.7311
Epoch: 012/200 | Batch 1350/1500 | Cost: 0.7643
Epoch: 012/200 | Batch 1400/1500 | Cost: 0.6223
Epoch: 012/200 | Batch 1450/1500 | Cost: 0.9704
MAE/RMSE/ACCURACY: | Current Valid: 0.21/0.46/0.80 Ep. 11 | Best Valid : 0.18/0.43/0.82 Ep. 10
Time elapsed: 136.54 min
Epoch: 013/200 | Batch 0000/1500 | Cost: 1.2266
Epoch: 013/200 | Batch 0050/1500 | Cost: 0.6827
Epoch: 013/200 | Batch 0100/1500 | Cost: 0.6942
Epoch: 013/200 | Batch 0150/1500 | Cost: 0.4784
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Epoch: 013/200 | Batch 0300/1500 | Cost: 0.8660
Epoch: 013/200 | Batch 0350/1500 | Cost: 0.6769
Epoch: 013/200 | Batch 0400/1500 | Cost: 0.9126
Epoch: 013/200 | Batch 0450/1500 | Cost: 0.5791
Epoch: 013/200 | Batch 0500/1500 | Cost: 1.4292
Epoch: 013/200 | Batch 0550/1500 | Cost: 0.8759
Epoch: 013/200 | Batch 0600/1500 | Cost: 0.9385
Epoch: 013/200 | Batch 0650/1500 | Cost: 1.1471
Epoch: 013/200 | Batch 0700/1500 | Cost: 0.8247
Epoch: 013/200 | Batch 0750/1500 | Cost: 0.5946
Epoch: 013/200 | Batch 0800/1500 | Cost: 0.9410
Epoch: 013/200 | Batch 0850/1500 | Cost: 0.5913
Epoch: 013/200 | Batch 0900/1500 | Cost: 0.8854
Epoch: 013/200 | Batch 0950/1500 | Cost: 0.6987
Epoch: 013/200 | Batch 1000/1500 | Cost: 0.8432
Epoch: 013/200 | Batch 1050/1500 | Cost: 0.7528
Epoch: 013/200 | Batch 1100/1500 | Cost: 0.6837
Epoch: 013/200 | Batch 1150/1500 | Cost: 1.0569
Epoch: 013/200 | Batch 1200/1500 | Cost: 0.4143
Epoch: 013/200 | Batch 1250/1500 | Cost: 1.2102
Epoch: 013/200 | Batch 1300/1500 | Cost: 0.5975
Epoch: 013/200 | Batch 1350/1500 | Cost: 0.6435
Epoch: 013/200 | Batch 1400/1500 | Cost: 0.6582
Epoch: 013/200 | Batch 1450/1500 | Cost: 1.2709
MAE/RMSE/ACCURACY: | Current Valid: 0.30/0.57/0.70 Ep. 12 | Best Valid : 0.18/0.43/0.82 Ep. 10
Time elapsed: 147.91 min
Epoch: 014/200 | Batch 0000/1500 | Cost: 0.8529
Epoch: 014/200 | Batch 0050/1500 | Cost: 0.7845
Epoch: 014/200 | Batch 0100/1500 | Cost: 0.4070
Epoch: 014/200 | Batch 0150/1500 | Cost: 1.0220
Epoch: 014/200 | Batch 0200/1500 | Cost: 1.5465
Epoch: 014/200 | Batch 0250/1500 | Cost: 1.0655
Epoch: 014/200 | Batch 0300/1500 | Cost: 0.7022
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Epoch: 014/200 | Batch 0400/1500 | Cost: 0.7484
Epoch: 014/200 | Batch 0450/1500 | Cost: 0.8637
Epoch: 014/200 | Batch 0500/1500 | Cost: 0.5084
Epoch: 014/200 | Batch 0550/1500 | Cost: 0.5692
Epoch: 014/200 | Batch 0600/1500 | Cost: 0.7255
Epoch: 014/200 | Batch 0650/1500 | Cost: 1.2822
Epoch: 014/200 | Batch 0700/1500 | Cost: 0.6839
Epoch: 014/200 | Batch 0750/1500 | Cost: 1.1601
Epoch: 014/200 | Batch 0800/1500 | Cost: 1.2975
Epoch: 014/200 | Batch 0850/1500 | Cost: 0.4686
Epoch: 014/200 | Batch 0900/1500 | Cost: 0.6376
Epoch: 014/200 | Batch 0950/1500 | Cost: 0.2384
Epoch: 014/200 | Batch 1000/1500 | Cost: 0.6763
Epoch: 014/200 | Batch 1050/1500 | Cost: 0.7045
Epoch: 014/200 | Batch 1100/1500 | Cost: 1.0331
Epoch: 014/200 | Batch 1150/1500 | Cost: 1.0152
Epoch: 014/200 | Batch 1200/1500 | Cost: 0.5595
Epoch: 014/200 | Batch 1250/1500 | Cost: 0.8204
Epoch: 014/200 | Batch 1300/1500 | Cost: 0.6379
Epoch: 014/200 | Batch 1350/1500 | Cost: 0.6151
Epoch: 014/200 | Batch 1400/1500 | Cost: 1.0476
Epoch: 014/200 | Batch 1450/1500 | Cost: 0.7369
MAE/RMSE/ACCURACY: | Current Valid: 0.15/0.39/0.86 Ep. 13 | Best Valid : 0.15/0.39/0.86 Ep. 13
Time elapsed: 159.30 min
Epoch: 015/200 | Batch 0000/1500 | Cost: 0.6660
Epoch: 015/200 | Batch 0050/1500 | Cost: 0.7864
Epoch: 015/200 | Batch 0100/1500 | Cost: 0.8331
Epoch: 015/200 | Batch 0150/1500 | Cost: 0.5423
Epoch: 015/200 | Batch 0200/1500 | Cost: 0.5954
Epoch: 015/200 | Batch 0250/1500 | Cost: 0.6142
Epoch: 015/200 | Batch 0300/1500 | Cost: 0.9292
Epoch: 015/200 | Batch 0350/1500 | Cost: 0.8704
Epoch: 015/200 | Batch 0400/1500 | Cost: 0.7513
Epoch: 015/200 | Batch 0450/1500 | Cost: 0.6930
Epoch: 015/200 | Batch 0500/1500 | Cost: 0.7528
Epoch: 015/200 | Batch 0550/1500 | Cost: 1.0551
Epoch: 015/200 | Batch 0600/1500 | Cost: 0.5235
Epoch: 015/200 | Batch 0650/1500 | Cost: 0.4682
Epoch: 015/200 | Batch 0700/1500 | Cost: 0.4062
Epoch: 015/200 | Batch 0750/1500 | Cost: 0.7409
Epoch: 015/200 | Batch 0800/1500 | Cost: 0.5945
Epoch: 015/200 | Batch 0850/1500 | Cost: 0.6403
Epoch: 015/200 | Batch 0900/1500 | Cost: 0.7589
Epoch: 015/200 | Batch 0950/1500 | Cost: 1.0115
Epoch: 015/200 | Batch 1000/1500 | Cost: 0.1908
Epoch: 015/200 | Batch 1050/1500 | Cost: 1.1863
Epoch: 015/200 | Batch 1100/1500 | Cost: 0.4779
Epoch: 015/200 | Batch 1150/1500 | Cost: 0.5332
Epoch: 015/200 | Batch 1200/1500 | Cost: 0.6489
Epoch: 015/200 | Batch 1250/1500 | Cost: 0.5240
Epoch: 015/200 | Batch 1300/1500 | Cost: 0.7022
Epoch: 015/200 | Batch 1350/1500 | Cost: 0.8903
Epoch: 015/200 | Batch 1400/1500 | Cost: 0.4872
Epoch: 015/200 | Batch 1450/1500 | Cost: 0.6031
MAE/RMSE/ACCURACY: | Current Valid: 0.16/0.41/0.84 Ep. 14 | Best Valid : 0.15/0.39/0.86 Ep. 13
Time elapsed: 170.68 min
Epoch: 016/200 | Batch 0000/1500 | Cost: 0.3443
Epoch: 016/200 | Batch 0050/1500 | Cost: 0.7994
Epoch: 016/200 | Batch 0100/1500 | Cost: 1.0167
Epoch: 016/200 | Batch 0150/1500 | Cost: 0.4250
Epoch: 016/200 | Batch 0200/1500 | Cost: 0.7777
Epoch: 016/200 | Batch 0250/1500 | Cost: 0.7790
Epoch: 016/200 | Batch 0300/1500 | Cost: 0.7620
Epoch: 016/200 | Batch 0350/1500 | Cost: 0.7791
Epoch: 016/200 | Batch 0400/1500 | Cost: 0.6177
Epoch: 016/200 | Batch 0450/1500 | Cost: 0.8440
Epoch: 016/200 | Batch 0500/1500 | Cost: 0.7664
Epoch: 016/200 | Batch 0550/1500 | Cost: 0.5714
Epoch: 016/200 | Batch 0600/1500 | Cost: 0.6457
Epoch: 016/200 | Batch 0650/1500 | Cost: 1.1470
Epoch: 016/200 | Batch 0700/1500 | Cost: 0.8477
Epoch: 016/200 | Batch 0750/1500 | Cost: 1.0389
Epoch: 016/200 | Batch 0800/1500 | Cost: 1.0338
Epoch: 016/200 | Batch 0850/1500 | Cost: 0.4844
Epoch: 016/200 | Batch 0900/1500 | Cost: 0.6539
Epoch: 016/200 | Batch 0950/1500 | Cost: 0.7755
Epoch: 016/200 | Batch 1000/1500 | Cost: 0.3191
Epoch: 016/200 | Batch 1050/1500 | Cost: 0.6382
Epoch: 016/200 | Batch 1100/1500 | Cost: 0.3369
Epoch: 016/200 | Batch 1150/1500 | Cost: 0.7347
Epoch: 016/200 | Batch 1200/1500 | Cost: 0.6816
Epoch: 016/200 | Batch 1250/1500 | Cost: 0.6242
Epoch: 016/200 | Batch 1300/1500 | Cost: 0.9128
Epoch: 016/200 | Batch 1350/1500 | Cost: 0.9098
Epoch: 016/200 | Batch 1400/1500 | Cost: 0.6769
Epoch: 016/200 | Batch 1450/1500 | Cost: 0.5297
MAE/RMSE/ACCURACY: | Current Valid: 0.17/0.41/0.83 Ep. 15 | Best Valid : 0.15/0.39/0.86 Ep. 13
Time elapsed: 182.06 min
Epoch: 017/200 | Batch 0000/1500 | Cost: 1.1221
Epoch: 017/200 | Batch 0050/1500 | Cost: 0.5580
Epoch: 017/200 | Batch 0100/1500 | Cost: 0.3280
Epoch: 017/200 | Batch 0150/1500 | Cost: 0.4604
Epoch: 017/200 | Batch 0200/1500 | Cost: 0.7905
Epoch: 017/200 | Batch 0250/1500 | Cost: 0.4437
Epoch: 017/200 | Batch 0300/1500 | Cost: 0.2617
Epoch: 017/200 | Batch 0350/1500 | Cost: 0.7839
Epoch: 017/200 | Batch 0400/1500 | Cost: 1.1746
Epoch: 017/200 | Batch 0450/1500 | Cost: 0.6676
Epoch: 017/200 | Batch 0500/1500 | Cost: 0.6462
Epoch: 017/200 | Batch 0550/1500 | Cost: 1.1650
Epoch: 017/200 | Batch 0600/1500 | Cost: 0.2235
Epoch: 017/200 | Batch 0650/1500 | Cost: 0.4938
Epoch: 017/200 | Batch 0700/1500 | Cost: 1.0009
Epoch: 017/200 | Batch 0750/1500 | Cost: 0.5821
Epoch: 017/200 | Batch 0800/1500 | Cost: 0.7409
Epoch: 017/200 | Batch 0850/1500 | Cost: 0.5974
Epoch: 017/200 | Batch 0900/1500 | Cost: 0.6149
Epoch: 017/200 | Batch 0950/1500 | Cost: 0.4537
Epoch: 017/200 | Batch 1000/1500 | Cost: 0.5583
Epoch: 017/200 | Batch 1050/1500 | Cost: 0.9739
Epoch: 017/200 | Batch 1100/1500 | Cost: 0.9201
Epoch: 017/200 | Batch 1150/1500 | Cost: 0.8645
Epoch: 017/200 | Batch 1200/1500 | Cost: 0.6729
Epoch: 017/200 | Batch 1250/1500 | Cost: 0.7447
Epoch: 017/200 | Batch 1300/1500 | Cost: 0.5516
Epoch: 017/200 | Batch 1350/1500 | Cost: 0.6489
Epoch: 017/200 | Batch 1400/1500 | Cost: 0.5394
Epoch: 017/200 | Batch 1450/1500 | Cost: 0.8003
MAE/RMSE/ACCURACY: | Current Valid: 0.17/0.43/0.83 Ep. 16 | Best Valid : 0.15/0.39/0.86 Ep. 13
Time elapsed: 193.42 min
Epoch: 018/200 | Batch 0000/1500 | Cost: 0.7671
Epoch: 018/200 | Batch 0050/1500 | Cost: 1.5519
Epoch: 018/200 | Batch 0100/1500 | Cost: 0.9046
Epoch: 018/200 | Batch 0150/1500 | Cost: 0.5993
Epoch: 018/200 | Batch 0200/1500 | Cost: 0.9571
Epoch: 018/200 | Batch 0250/1500 | Cost: 0.5003
Epoch: 018/200 | Batch 0300/1500 | Cost: 0.2906
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Epoch: 018/200 | Batch 0450/1500 | Cost: 0.5803
Epoch: 018/200 | Batch 0500/1500 | Cost: 0.8342
Epoch: 018/200 | Batch 0550/1500 | Cost: 1.0594
Epoch: 018/200 | Batch 0600/1500 | Cost: 1.2759
Epoch: 018/200 | Batch 0650/1500 | Cost: 0.7642
Epoch: 018/200 | Batch 0700/1500 | Cost: 0.8638
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Epoch: 018/200 | Batch 0800/1500 | Cost: 0.8033
Epoch: 018/200 | Batch 0850/1500 | Cost: 0.5296
Epoch: 018/200 | Batch 0900/1500 | Cost: 0.7182
Epoch: 018/200 | Batch 0950/1500 | Cost: 0.5923
Epoch: 018/200 | Batch 1000/1500 | Cost: 0.3104
Epoch: 018/200 | Batch 1050/1500 | Cost: 0.6603
Epoch: 018/200 | Batch 1100/1500 | Cost: 0.5983
Epoch: 018/200 | Batch 1150/1500 | Cost: 0.8228
Epoch: 018/200 | Batch 1200/1500 | Cost: 0.4631
Epoch: 018/200 | Batch 1250/1500 | Cost: 0.5738
Epoch: 018/200 | Batch 1300/1500 | Cost: 0.8629
Epoch: 018/200 | Batch 1350/1500 | Cost: 0.4221
Epoch: 018/200 | Batch 1400/1500 | Cost: 0.6621
Epoch: 018/200 | Batch 1450/1500 | Cost: 0.6605
MAE/RMSE/ACCURACY: | Current Valid: 0.13/0.37/0.87 Ep. 17 | Best Valid : 0.13/0.37/0.87 Ep. 17
Time elapsed: 204.78 min
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MAE/RMSE/ACCURACY: | Current Valid: 0.16/0.41/0.84 Ep. 18 | Best Valid : 0.13/0.37/0.87 Ep. 17
Time elapsed: 216.17 min
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MAE/RMSE/ACCURACY: | Current Valid: 0.13/0.36/0.87 Ep. 19 | Best Valid : 0.13/0.36/0.87 Ep. 19
Time elapsed: 227.54 min
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MAE/RMSE/ACCURACY: | Current Valid: 0.11/0.34/0.89 Ep. 20 | Best Valid : 0.11/0.34/0.89 Ep. 20
Time elapsed: 238.92 min
Epoch: 022/200 | Batch 0000/1500 | Cost: 0.4041
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MAE/RMSE/ACCURACY: | Current Valid: 0.13/0.37/0.87 Ep. 21 | Best Valid : 0.11/0.34/0.89 Ep. 20
Time elapsed: 250.29 min
Epoch: 023/200 | Batch 0000/1500 | Cost: 0.6007
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Epoch: 023/200 | Batch 1450/1500 | Cost: 0.7138
MAE/RMSE/ACCURACY: | Current Valid: 0.16/0.40/0.84 Ep. 22 | Best Valid : 0.11/0.34/0.89 Ep. 20
Time elapsed: 261.65 min
Epoch: 024/200 | Batch 0000/1500 | Cost: 0.2997
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Epoch: 024/200 | Batch 1450/1500 | Cost: 0.4708
MAE/RMSE/ACCURACY: | Current Valid: 0.11/0.34/0.89 Ep. 23 | Best Valid : 0.11/0.34/0.89 Ep. 23
Time elapsed: 273.03 min
Epoch: 025/200 | Batch 0000/1500 | Cost: 0.3489
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Epoch: 025/200 | Batch 1450/1500 | Cost: 0.6532
MAE/RMSE/ACCURACY: | Current Valid: 0.09/0.30/0.91 Ep. 24 | Best Valid : 0.09/0.30/0.91 Ep. 24
Time elapsed: 284.45 min
Epoch: 026/200 | Batch 0000/1500 | Cost: 0.6920
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Epoch: 026/200 | Batch 1450/1500 | Cost: 0.7116
MAE/RMSE/ACCURACY: | Current Valid: 0.10/0.32/0.90 Ep. 25 | Best Valid : 0.09/0.30/0.91 Ep. 24
Time elapsed: 295.83 min
Epoch: 027/200 | Batch 0000/1500 | Cost: 0.6230
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Epoch: 027/200 | Batch 1450/1500 | Cost: 0.5800
MAE/RMSE/ACCURACY: | Current Valid: 0.11/0.33/0.89 Ep. 26 | Best Valid : 0.09/0.30/0.91 Ep. 24
Time elapsed: 307.20 min
Epoch: 028/200 | Batch 0000/1500 | Cost: 0.2867
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Epoch: 028/200 | Batch 1450/1500 | Cost: 0.5625
MAE/RMSE/ACCURACY: | Current Valid: 0.09/0.31/0.91 Ep. 27 | Best Valid : 0.09/0.30/0.91 Ep. 24
Time elapsed: 318.58 min
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MAE/RMSE/ACCURACY: | Current Valid: 0.09/0.30/0.91 Ep. 28 | Best Valid : 0.09/0.30/0.91 Ep. 24
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In [17]:
test_dataset = DatasetAge(csv_path=TEST_CSV_PATH,
                              img_dir="data/",
                              split="test",
                              transform=val_transforms)

test_loader = DataLoader(dataset=test_dataset,
                         batch_size=BATCH_SIZE,
                         shuffle=False,
                         num_workers=NUM_WORKERS)
In [18]:
map_ = {
    0: '0-10',
    1: '10-20',
    2: '20-30',
    3: '30-40',
    4: '40-50',
    5: '50-60',
    6: '60-70',
    7: '70-80',
    8: '80-90',
    9: '90-100'
}
In [ ]:
model = AgeModel(10)

model.load_state_dict(torch.load("out_finalv2/best_model.pt", map_location='cpu'))
model.eval()
model.to(DEVICE)
########## SAVE PREDICTIONS ######

all_pred = []
all_probas = []
with torch.set_grad_enabled(False):
    for batch_idx, (features, id_) in enumerate(test_loader):
        features = features.to(DEVICE)
        logits, probas = model(features)
        all_probas.append(probas)
        predict_levels = probas > 0.5
        predicted_labels = torch.sum(predict_levels, dim=1)
        lst = [(id_, map_[int(i)]) for i, id_ in zip(predicted_labels, id_)]
        all_pred.extend(lst)
Loaded pretrained weights for efficientnet-b6
In [ ]:
pd.DataFrame(all_pred, columns=["ImageID", "age"]).to_csv("assets/submission.csv")
In [ ]:
%aicrowd notebook submit -c age-prediction -a assets --no-verify
In [ ]:


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