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Created 4 years ago
# 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' # Contains training images
TEST_DIR = DATA_DIR + '/test' # Contains test images
TRAIN_CSV = DATA_DIR + '/train.csv' # Contains real labels for training images
TEST_CSV = '../input/jovian-pytorch-z2g/submission.csv' # Contains dummy labels for test image
!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