import os
import numpy as np
import pandas as pd
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
from torchvision.datasets import ImageFolder
from torch.utils.data import Dataset, random_split, DataLoader
import torchvision.transforms as tt
import torchvision.models as models
from torchvision.utils import make_grid
from PIL import Image
from tqdm.notebook import tqdm
from sklearn.metrics import f1_score
import matplotlib.pyplot as plt
%matplotlib inline

project_name='dogs-cat-resnet'

# Checking the files along with number of samples
train_dir = '../input/dogs-vs-cats/train/train'
test_dir =  '../input/dogs-vs-cats/test/test'
train_files = os.listdir(train_dir) 
test_files = os.listdir(test_dir)
print(len(train_files),'Training Samples')
print(len(test_files),'Testing Samples')

#labeling train dataset
category=[]
for i in train_files:
    catg=i.split('.')[0]
    if catg=='cat':
        category.append(0) 
    else:
        category.append(1) 
#category = torch.tensor(category)        
category[0:5]