tutorial22_quickStartForExpert.py

# https://www.tensorflow.org/tutorials/quickstart/advanced?hl=ko

import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model

mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

# 채널 자원 추가
x_train = x_train[... , tf.newaxis]
x_test = x_test[... , tf.newaxis]

# tf.data 를 사용하여 데이터셋을 섞고 배치 생성
train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(32)
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)

# 케라스의 모델 서브 클래싱 API 를 사용하여 tf.keras 모델 생성

class MyModel(Model):
    def __init__(self):
        super(MyModel, self).__init__()
        self.conv1 = Conv2D(32, 3, activation = 'relu')
        self.flatten = Flatten()
        self.d1 = Dense(128, activation = 'relu')
        self.d2 = Dense(10, activation = 'softmax')
        
    def call(self, x):
        x = self.conv1(x)
        x = self.flatten(x)
        x = self.d1(x)
        return self.d2(x)

model = MyModel()

# 훈련에 필요한 옵티마이저와 손실 함수 선택
loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
optimizer = tf.keras.optimizers.Adam()

# 모델의 손실과 성능을 측정할 지표 선택
train_loss = tf.keras.metrics.Mean(name = 'train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name = 'train_accuracy')

test_loss = tf.keras.metrics.Mean(name = 'test_loss')
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name = 'test_accuracy')

# tf.GradientTape 를 사용하여 모델 훈련
@tf.function
def train_step(images, labels):
    with tf.GradientTape() as tape:
        predictions = model(images)
        loss = loss_object(labels, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    
    train_loss(loss)
    train_accuracy(labels, predictions)

@tf.function
def test_step(images, labels):
    predictions = model(images)
    t_loss = loss_object(labels, predictions)
    
    test_loss(t_loss)
    test_accuracy(labels, predictions)

EPOCHS = 5

for epoch in range(EPOCHS):
    for images, labels in train_ds:
        train_step(images, labels)
        
    for test_images, test_labels in test_ds:
        test_step(test_images, test_labels)
        
    template = '에포크 : {}, 손실 : {}, 정확도 : {}, 테스트 손실 : {}, 테스트 정확도 : {}'
    print(template.format(epoch + 1, train_loss.result(), train_accuracy.result() * 100,
            test_loss.result(), test_accuracy.result() * 100))