const model = tf.sequential();
model.add(tf.layers.conv2d({
  inputShape: [28, 28, 1],
  filters: 32,
  kernelSize: 3,
  activation: 'relu',
}));
model.add(tf.layers.conv2d({
  filters: 32,
  kernelSize: 3,
  activation: 'relu',
}));
model.add(tf.layers.maxPooling2d({poolSize: [2, 2]}));
model.add(tf.layers.conv2d({
  filters: 64,
  kernelSize: 3,
  activation: 'relu',
}));
model.add(tf.layers.conv2d({
  filters: 64,
  kernelSize: 3,
  activation: 'relu',
}));
model.add(tf.layers.maxPooling2d({poolSize: [2, 2]}));
model.add(tf.layers.flatten());
model.add(tf.layers.dropout({rate: 0.25})); ------ 과대적합을 막기 위해 드롭아웃 층을 추가합니다.
model.add(tf.layers.dense({units: 512, activation: 'relu'}));
model.add(tf.layers.dropout({rate: 0.5}));
model.add(tf.layers.dense({units: 10, activation: 'softmax'}));

model.summary();
model.compile({
  optimizer: 'rmsprop',
  loss: 'categoricalCrossentropy',
  metrics: ['accuracy'],
});