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# 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 in 
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import cv2
import tensorflow as tf
!pip install seaborn
import seaborn as sns
from sklearn.model_selection import  train_test_split
from sklearn.utils import shuffle
from sklearn.metrics import cohen_kappa_score, accuracy_score
from keras.applications import DenseNet121
from keras import layers
from keras.models import Sequential
from keras.optimizers import Adam
from keras.callbacks import Callback, ModelCheckpoint
from keras.preprocessing.image import ImageDataGenerator
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
import os
print(os.listdir("../input"))
img_size = 512   # setting our image size to a standard value 
# Any results you write to the current directory are saved as output.