05_cnn.py

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

import matplotlib.pyplot as plt

cifar10 = keras.datasets.cifar10

(train_images, train_labels), (test_images, test_labels) = cifar10.load_data()

print(train_images.shape) # 50000, 32, 32, 3

# Normalize: 0,255 -> 0,1
train_images, test_images = train_images / 255.0, test_images / 255.0

class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
               'dog', 'frog', 'horse', 'ship', 'truck']

def show():
    plt.figure(figsize=(10,10))
    for i in range(16):
        plt.subplot(4,4,i+1)
        plt.xticks([])
        plt.yticks([])
        plt.grid(False)
        plt.imshow(train_images[i], cmap=plt.cm.binary)
        # The CIFAR labels happen to be arrays, 
        # which is why you need the extra index
        plt.xlabel(class_names[train_labels[i][0]])
    plt.show()

show()

# model...
model = keras.models.Sequential()
model.add(layers.Conv2D(32, (3,3), strides=(1,1), padding="valid", activation='relu', input_shape=(32,32,3)))
model.add(layers.MaxPool2D((2,2)))
model.add(layers.Conv2D(32, 3, activation='relu'))
model.add(layers.MaxPool2D((2,2)))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10))
print(model.summary())
#import sys; sys.exit()

# loss and optimizer
loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)
optim = keras.optimizers.Adam(lr=0.001)
metrics = ["accuracy"]

model.compile(optimizer=optim, loss=loss, metrics=metrics)

# training
batch_size = 64
epochs = 5

model.fit(train_images, train_labels, epochs=epochs,
          batch_size=batch_size, verbose=2)

# evaulate
model.evaluate(test_images,  test_labels, batch_size=batch_size, verbose=2)