keras.layers.Flatten

0.10009336676746607​

# flatten

# 출력값을 1차원으로 풀어준다. ( =ravel())
(X_train,y_train),(X_test,y_test)=mnist.load_data()
X_train=X_train/255
X_test=X_test/255
y_train=np.eye(10)[y_train]
y_test=np.eye(10)[y_test]
from keras.layers import Flatten
model=Sequential()
model.add(Flatten(input_shape=(28,28)))
model.add(Dense(128,activation='relu'))
model.add(Dense(256,activation='relu'))
model.add(Dense(256,activation='relu'))
model.add(Dense(10,activation='softmax'))

model.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_7 (Flatten)          (None, 784)               0         
_________________________________________________________________
dense_58 (Dense)             (None, 128)               100480    
_________________________________________________________________
dense_59 (Dense)             (None, 256)               33024     
_________________________________________________________________
dense_60 (Dense)             (None, 256)               65792     
_________________________________________________________________
dense_61 (Dense)             (None, 10)                2570      
=================================================================
Total params: 201,866
Trainable params: 201,866
Non-trainable params: 0
_________________________________________________________________

 

# score test
pred_y_test=model.predict(X_test)
(pred_y_test*y_test).sum()/len(y_test)