CIFAR 10 Classification

Let's get used to the sensorflow framework and understand how CNN is work!
IMPORT LIBRARY

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

LOAD DATA

cifar10 = tf.keras.datasets.cifar10
#bring CIFAR10 dataset from tensorflow
(train_images,train_labels),(test_images,test_labels) = cifar10.load_data()
#load data separately in to train_images,train_label,test_images,test_data

ANALISYS DATA

#check each dataset shape and number of data size 
print(train_images.shape)
print(len(train_images))

print(test_images.shape)
print(len(test_images))

#data visualization
plt.figure(figsize=(3,3)) # draw figure
for i in range(9):
	plt.subplot(3,3,i+1)  # subplot into 3*3 array shape
	plt.imshow(train_images[i])
	plt.colorbar()
	plt.grid(False)
	plt.show()

MODEL TRAIN

#model building
model = tf.keras.Sequential([
tf.keras.layers.experimental.preprocessing.Rescaling(1./255),
# normalize dataset values to between 0-1
tf.keras.layers.Conv2D(32, 3, activation='relu'),
# convolutional layer with 32 many and 3*3 sized kernels , use ReLu for activation function
tf.keras.layers.MaxPooling2D(),
# maxpooling layers 
tf.keras.layers.Conv2D(64, 3, activation='relu'),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Conv2D(128, 3, activation='relu'),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Flatten(),
# flatten layers from n dimension array form to 1 dimension
tf.keras.layers.Dense(128, activation='relu'),
# connect 128 nodes using dense layer 
tf.keras.layers.Dense(10)
# because we want 10 different classes to be defined connect to 10 nodes 

])

#model.summary()
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
# compile model using 'adam' for optimizer, crossentropy for loss function

model.fit(train_images, train_labels, epochs=15)
# actually train model epochs(=15) times

test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
# check the result value of loss and accuracy
print('\nTest accuracy:', test_acc)