# 0. Import libraries
import tensorflow as tf

# 1. Load dataset
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

# 2. Modeling
model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation='softmax')
])

# 3. Select loss function, optimizer
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# 4. Train and test
model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test,  y_test, verbose=2)

# 0. Import libraries
import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model

# 1. Load dataset
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

# 2. Add channel dimension (x: [height, width, channel])
x_train = x_train[..., tf.newaxis]
x_test = x_test[..., tf.newaxis]

# 3. Shuffle dataset and generate batch
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)

# 4. Modeling using subclass API
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()

# 5. Select loss function and optimizer
loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
optimizer = tf.keras.optimizers.Adam()

# 6. Select metrics
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')

# 7. Train function
@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)

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

# 9. Train and test
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)
  print('에포크: {}, 손실: {}, 정확도: {}, 테스트 손실: {}, 테스트 정확도: {}'.format(
    epoch+1,
    train_loss.result(),
    train_accuracy.result()*100,
    test_loss.result(),
    test_accuracy.result()*100))