问题
I have the following sequential model that works with variable length inputs:
m = Sequential()
m.add(Embedding(len(chars), 4, name="embedding"))
m.add(Bidirectional(LSTM(16, unit_forget_bias=True, name="lstm")))
m.add(Dense(len(chars),name="dense"))
m.add(Activation("softmax"))
m.summary()
Gives the following summary:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, None, 4) 204
_________________________________________________________________
bidirectional_2 (Bidirection (None, 32) 2688
_________________________________________________________________
dense (Dense) (None, 51) 1683
_________________________________________________________________
activation_2 (Activation) (None, 51) 0
=================================================================
Total params: 4,575
Trainable params: 4,575
Non-trainable params: 0
However when I try to implement the same model in functional API I don't know whatever I try as Input layer shape doesn't seem to be the same as the sequential model. Here is one of my tries:
charinput = Input(shape=(4,),name="input",dtype='int32')
embedding = Embedding(len(chars), 4, name="embedding")(charinput)
lstm = Bidirectional(LSTM(16, unit_forget_bias=True, name="lstm"))(embedding)
dense = Dense(len(chars),name="dense")(lstm)
output = Activation("softmax")(dense)
And here is the summary:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input (InputLayer) (None, 4) 0
_________________________________________________________________
embedding (Embedding) (None, 4, 4) 204
_________________________________________________________________
bidirectional_1 (Bidirection (None, 32) 2688
_________________________________________________________________
dense (Dense) (None, 51) 1683
_________________________________________________________________
activation_1 (Activation) (None, 51) 0
=================================================================
Total params: 4,575
Trainable params: 4,575
Non-trainable params: 0
回答1:
Use shape=(None,) in the input layer, in your case:
charinput = Input(shape=(None,),name="input",dtype='int32')
回答2:
Try adding the argument input_length=None to the embeddinglayer.
来源：https://stackoverflow.com/questions/45460227/keras-embedding-layer-with-variable-length-in-functional-api