Classification of Flowers by Using ResNet

# 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

project_name = 'Course_Project_Flower_Classification'

import torch
import torchvision
import numpy as np 
import pandas as pd 
import os
import tarfile
import torchvision
from torch.utils.data import random_split
from torchvision.datasets import ImageFolder
from torchvision import transforms
from torchvision.transforms import ToTensor
from torch.utils.data.dataloader import DataLoader
import torch.nn as nn
from torchvision.utils import make_grid
import torchvision.models as models
import torch.nn.functional as F
import matplotlib.pyplot as plt
%matplotlib inline