Learn practical skills, build real-world projects, and advance your career
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
import os
import torch
import pandas as pd
import numpy as np
from torch.utils.data import Dataset, random_split, DataLoader
from PIL import Image
import torchvision.models as models
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
from sklearn.metrics import f1_score
import torch.nn.functional as F
import torch.nn as nn
from torchvision.utils import make_grid
%matplotlib inline
DATA_DIR = '../input/jovian-pytorch-z2g/Human protein atlas'

TRAIN_DIR = DATA_DIR + '/train'                          
TEST_DIR = DATA_DIR + '/test'                             

TRAIN_CSV = DATA_DIR + '/train.csv'                       
TEST_CSV = '../input/jovian-pytorch-z2g/submission.csv' 
!head "{TRAIN_CSV}"
Image,Label 19567,9 29993,6 4 17186,1 4 29600,6 2 701,3 4 26562,9 1080,4 27886,4 30721,6
!head "{TEST_CSV}"
Image,Label 24117,0 15322,0 14546,0 8079,0 13192,0 25927,0 3372,0 21781,0 2847,0