from keras.datasets import cifar10
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
# x_train shape: (50000, 32, 32, 3)

num_classes = 10
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
# y_train[444] -> [0,0,0,0,0,0,0,0,0,1]  (class 9)

x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

keras.layers.Conv2D(filters, kernel_size, strides=(1, 1), padding='valid', ...)

keras.layers.MaxPooling2D(pool_size=(2, 2), strides=None, padding='valid')

model_1 = Sequential()
model_1.add(Conv2D(32, kernel_size=(5,5), strides=(2,2), padding='same',
                    input_shape=x_train.shape[1:]))
model_1.add(Activation('relu'))
model_1.add(Conv2D(32, (5,5), strides=(2,2), activation="relu"))
model_1.add(MaxPooling2D(pool_size=(2, 2)))
model_1.add(Dropout(0.25))
model_1.add(Flatten())
model_1.add(Dense(512))
model_1.add(Activation('relu'))
model_1.add(Dropout(0.5))
model_1.add(Dense(num_classes))
model_1.add(Activation('softmax'))

model_1.compile(loss='categorical_crossentropy',
                 optimizer='rmsprop',
                 metrics=['accuracy'])

model_1.fit(x_train, y_train, batch_size=32, epochs=15,
            validation_data=(x_test, y_test), shuffle=True)

Epoch 1/15  accuracy: 0.3136  val_accuracy: 0.4506
Epoch 8/15  accuracy: 0.5748  val_accuracy: 0.6114
Epoch 15/15 accuracy: 0.5731  val_accuracy: 0.5863

from tensorflow.keras.preprocessing.image import ImageDataGenerator

datagen = ImageDataGenerator(
    rotation_range=20,
    width_shift_range=0.1,
    height_shift_range=0.1,
    horizontal_flip=False,
    shear_range=0.1,
    zoom_range=0.1,
    fill_mode='nearest'
)

model_1.fit(
    datagen.flow(x_train, y_train, batch_size=batch_size),
    epochs=15,
    validation_data=(x_test, y_test)
)

Epoch 1/15   accuracy: 0.3195  val_accuracy: 0.5205
Epoch 9/15   accuracy: 0.6074  val_accuracy: 0.6604
Epoch 15/15  accuracy: 0.6221  val_accuracy: 0.6285