[TensorFlow 2.0] Optimizer 및 Training (Expert)

TensorFlow 공식 홈페이지에서 설명하는 Expert 버전을 사용해봅니다.

Load Packages

import tensorflow as tf
from tensorflow.keras import layers

from tensorflow.keras import datasets

학습 과정 돌아보기

Build Model

input_shape = (28, 28, 1)
num_classes = 10

inputs = layers.Input(input_shape, dtype=tf.float64)
net = layers.Conv2D(32, (3, 3), padding='SAME')(inputs)
net = layers.Activation('relu')(net)
net = layers.Conv2D(32, (3, 3), padding='SAME')(net)
net = layers.Activation('relu')(net)
net = layers.MaxPooling2D(pool_size=(2, 2))(net)
net = layers.Dropout(0.5)(net)

net = layers.Conv2D(64, (3, 3), padding='SAME')(net)
net = layers.Activation('relu')(net)
net = layers.Conv2D(64, (3, 3), padding='SAME')(net)
net = layers.Activation('relu')(net)
net = layers.MaxPooling2D(pool_size=(2, 2))(net)
net = layers.Dropout(0.5)(net)

net = layers.Flatten()(net)
net = layers.Dense(512)(net)
net = layers.Activation('relu')(net)
net = layers.Dropout(0.5)(net)
net = layers.Dense(num_classes)(net)
net = layers.Activation('softmax')(net)

model = tf.keras.Model(inputs=inputs, outputs=net, name='Basic_CNN')

Preprocess

TensorFlow 공식 홈페이지에서 말한 expert 방법을 사용합니다.

mnist = tf.keras.datasets.mnist

# Load Data from MNIST
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# Channel 차원 추가
x_train = x_train[..., tf.newaxis]
x_test = x_test[..., tf.newaxis]

# Data Normailzation
x_train, x_test = x_train / 255.0, x_test / 255.0

tf.data 사용

• from_tensor_slices()
• shuffle()
• batch()

train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train))
train_ds = train_ds.shuffle(1000)
train_ds = train_ds.batch(32)

test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test))
test_ds = test_ds.batch(32)

Visualize Data

matplotlib 불러와서 데이터를 시각화합니다.

import matplotlib.pyplot as plt
%matplotlib inline

for image, label in train_ds.take(2):
    plt.title(label[0].shape)
    plt.imshow(image[0, :, :, 0], 'gray')
    plt.show()

Training (Keras)

Keras로 학습 할 때는 기존과 같지만, train_ds는 generator라서 그래도 넣을 수 있습니다.

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
model.fit(train_ds, epochs=1000)

Optimization

• Loss Function
• Optimizer
• Metrics

loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
optimizer = tf.keras.optimizers.Adam()

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')

Training

@tf.function : 기존 session 열었던 것 처럼 바로 작동 안 하고, 그래프만 만들고 학습이 시작되면 돌아가도록 합니다.

@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, lebels):
    predictions = model(images)
    t_loss = loss_object(labels, predictions)

    test_loss(t_loss)
    test_accuracy(labels, predictions)

for epoch in range(2):
    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 = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss {}, Test Accuracy: {}'
    print(template.format(epoch+1,
                          train_loss.result(),
                          train_accuracy.result() * 100,
                          test_loss.result(),
                          test_accuracy.result() * 100))

결과

Start Training
Epoch 1, Loss: 0.04196552559733391, Accuracy: 98.74666595458984, Test Loss 0.043360475450754166, Test Accuracy: 98.72000122070312
Start Training
Epoch 2, Loss: 0.033374134451150894, Accuracy: 99.0050048828125, Test Loss 0.03336939960718155, Test Accuracy: 98.95500183105469