我是堆栈,我需要stackoverflow的智慧 .
我使用Functional API在Keras中实现了两个输入神经网络,输入形状为:
X.shape, X_size.shape, y.shape
((123, 9), (123, 2), (123, 9, 10))
所以,我的问题是我想从LSTM获得输出形状有三维形状,以便使用我的 y 张量 . 我知道,我可以将我的形状重塑为2-D形状,但我想将它用作3-D阵列 .
from keras.models import Model
from keras import layers
from keras import Input
# first input
list_input = Input(shape=(None,), dtype='int32', name='li')
embedded_list = layers.Embedding(100,90)(list_input)
encoded_list = layers.LSTM(4, name = "lstm1")(embedded_list)
# second input
size_input = Input(shape=(None,), dtype='int32', name='si')
embedded_size = layers.Embedding(100,10)(size_input)
encoded_size = layers.LSTM(4, name = "lstm2")(embedded_size)
# concatenate
concatenated = layers.concatenate([encoded_size, encoded_list], axis=-1)
answer = layers.Dense(90, activation='sigmoid', name = 'outpuy_layer')(concatenated)
model = Model([list_input, size_input], answer)
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=[f1])
型号摘要:
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
si (InputLayer) (None, None) 0
____________________________________________________________________________________________________
li (InputLayer) (None, None) 0
____________________________________________________________________________________________________
embedding_16 (Embedding) (None, None, 10) 1000 si[0][0]
____________________________________________________________________________________________________
embedding_15 (Embedding) (None, None, 90) 9000 li[0][0]
____________________________________________________________________________________________________
lstm2 (LSTM) (None, 4) 240 embedding_16[0][0]
____________________________________________________________________________________________________
lstm1 (LSTM) (None, 4) 1520 embedding_15[0][0]
____________________________________________________________________________________________________
concatenate_8 (Concatenate) (None, 8) 0 lstm2[0][0]
lstm1[0][0]
____________________________________________________________________________________________________
outpuy_layer (Dense) (None, 90) 810 concatenate_8[0][0]
====================================================================================================
Total params: 12,570
Trainable params: 12,570
Non-trainable params: 0
再一次,问题是:
How to get output shape from LSTMs like (None, None, None/10)?
1 回答
Keras默认忽略除最后一个输出之外的每个时间步输出,这将创建一个2D数组 . 要获得3D数组(意味着您获得每个时间步的输出),请将
return_sequences
设置为True
来实例化该层 . 以你的情况为例: