import tensorflow as tf
from keras import layers
from keras import models
import matplotlib as plt
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
model.summary()

from keras.datasets import cifar10
from keras.utils import to_categorical
(train_images, train_labels), (test_images, test_labels) = cifar10.load_data()

# print(len(train_images))
# print(len(test_labels))
train_images = train_images.reshape((50000, 32, 32, 3))
train_images = train_images.astype('float32') / 255
test_images = test_images.reshape((10000, 32, 32, 3))
test_images = test_images.astype('float32') / 255
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5, batch_size=64)

test_loss, test_acc = model.evaluate(test_images, test_labels)
print('test_acc = ', test_acc)