Keras 모델 구현하는 방법

Sequential

# Sequential

seq_model = keras.Sequential([
  keras.Input(shape=(28,28)),
  layers.Flatten(),
  layers.Dense(300, activation='relu'),
  layers.Dense(10, activation='softmax')
])

seq_model.summary()

functional API

#functional API

inp = keras.Input(shape=(28,28))
x = layers.Flatten()(inp)
x = layers.Dense(300, activation='relu')(x)
out = layers.Dense(10, activation='softmax')(x)

func_model = keras.Model(inputs=inp , outputs=out)

func_model.summary()

Subclassing API

#Subclassing API

class model(keras.Model):
  def __init__(self):
    super().__init__()
    self.flatten = layers.Flatten()
    self.Dense1 = layers.Dense(300, activation='relu')
    self.Dense2 = layers.Dense(10, activation='softmax')

    
  def call(self, inputs):
    x = self.flatten(inputs)
    x = self.Dense1(x)
    x = self.Dense2(x)

    return x
    
cls_model = model()
cls_model.build((None,28,28))

cls_model.summary()

모델의 구조를 시각화해보기