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我具有与可变长度的输入工作下列顺序模型：与功能API可变长度Keras埋入层
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()
提供了以下总结：
_________________________________________________________________
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
然而，当我尝试实施功能API中的相同模型我不知道我尝试的任何输入层的形状看起来与顺序模型不一样。这里是我的尝试之一：
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)
这里是概要：
_________________________________________________________________
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