# 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

from __future__ import print_function
import keras
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D, BatchNormalization, MaxPool2D
import os

import numpy as np

import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt

from sklearn.metrics import confusion_matrix, classification_report
import itertools

%matplotlib inline

# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout,Flatten
from tensorflow.keras.optimizers import Adam, SGD
from tensorflow.keras.losses import SparseCategoricalCrossentropy
from tensorflow.keras.callbacks import TensorBoard,EarlyStopping
%load_ext tensorboard

# Helper libraries
import numpy as np
import random
import matplotlib.pyplot as plt
import datetime

print(tf.__version__)

import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))

batch_size = 32 # The default batch size of keras.
num_classes = 10  # Number of class for the dataset
epochs = 100
data_augmentation = False