Tensorflow2 dropout

# tf.keras.models.Sequential()

model = keras.models.Sequential()
model.add(keras.layers.Flatten(input_shape=[28, 28]))
for _ in range(20):
    model.add(keras.layers.Dense(100, activation="selu"))
model.add(keras.layers.AlphaDropout(rate=0.5))
# AlphaDropout: 1. 均值和方差不變 2. 歸一化性質也不變
# model.add(keras.layers.Dropout(rate=0.5))
model.add(keras.layers.Dense(10, activation="softmax"))

model.compile(loss="sparse_categorical_crossentropy",
              optimizer = "sgd",
              metrics = ["accuracy"])

一般在最后幾層使用dropout來防止過擬合