import numpy as np
from tensorflow import keras
from keras.constraints import maxnorm
from keras.utils import np_utils

seed = 21

from keras.datasets import cifar10
'''
The CIFAR-10 dataset 
(Canadian Institute for Advanced Research, 10 classes) 
is a subset of the Tiny Images dataset and 
consists of 60000 32x32 color images.
'''

# loading the data
(x_train, y_train), (x_test, y_test) = cifar10.load_data()

#Normalize the inputs from 0-255 to between 0 and 1 by dividing by 255
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train = x_train/255.0
x_test = x_test/255.0

# One-hot encode outputs
'''
Another thing we'll need to do to get the data ready for the network is to one-hot encode the values. 
Lets not go into the specifics of one-hot encoding here, but for now know that the images can't be used by the network as they are, 
they need to be encoded first and 
one-hot encoding is best used when doing binary classification.
'''
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
class_num = y_test.shape[1]


model = keras.Sequential()
model.add(keras.layers.layer1)
model.add(keras.layers.layer2)
model.add(keras.layers.layer3)

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy', 'val_accuracy'])

print(model.summary())