Model
Sequential()- 서브 클래싱(Subclassing)
- 함수형 API
Sequential()
- 모델이 순차적인 구조로 진행할 때 사용
- 간단한 방법
- Sequential 객체 생성 후
add()를 이용한 방법 - Sequential 인자에 한 번에 추가 방법
- Sequential 객체 생성 후
- 다중 입력 및 출력이 존재하는 복잡한 모델을 구성할 수 없음
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from tensorflow.keras.layers import Dense, Input, Flatten
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.utils import plot_model
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# add 사용
model = Sequential()
model.add(Input(shape=(28, 28)))
model.add(Dense(units=300, activation='relu'))
model.add(Dense(units=100, activation='relu'))
model.add(Dense(units=10, activation='softmax'))
model.summary()
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Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_1 (Dense) (None, 28, 300) 8700
dense_2 (Dense) (None, 28, 100) 30100
dense_3 (Dense) (None, 28, 10) 1010
=================================================================
Total params: 39,810
Trainable params: 39,810
Non-trainable params: 0
_________________________________________________________________
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plot_model(model)
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# 인자에 리스트로 추가
model = Sequential([Input(shape=(28, 28)),
Dense(units=300, activation='relu', name='Dense1'),
Dense(units=100, activation='relu', name='Dense2'),
Dense(units=10, activation='softmax', name='Ouput')])
model.summary()
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Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
Dense1 (Dense) (None, 28, 300) 8700
Dense2 (Dense) (None, 28, 100) 30100
Ouput (Dense) (None, 28, 10) 1010
=================================================================
Total params: 39,810
Trainable params: 39,810
Non-trainable params: 0
_________________________________________________________________