Keras对每个时间步应用不同的Dense层

问题描述

我有(-1, 10)形状的训练数据，并且我想在每个时间步上应用不同的Dense层.目前，我试图通过将输入重塑为(-1, 20, 1)，然后在顶部使用TimeDistributed(Dense(10))层来实现此目的.但是，这似乎将相同的密集"层应用于每个时间步，因此时间步共享权重.有什么办法吗?

I have training data in the shape of (-1, 10) and I want to apply a different Dense layer to each timestep. Currently, I tried to achieve this by reshaping input to (-1, 20, 1) and then using a TimeDistributed(Dense(10)) layer on top. However, that appears to apply the same Dense layer to each timestep, so timesteps share the weights. Any way to do that?

推荐答案

您可以应用200宽的矢量的密集层，该矢量是通过将输入复制20次而创建的，如下所示:

You can apply a dense layer of a vector 200-wide which is created by copying the input 20 times, like so:

from tensorflow.python import keras
from keras.models import Sequential
from keras.layers import *

model = Sequential()
model.add(RepeatVector(20, input_shape=(10,)))
model.add(Reshape((200,)))
model.add(Dense(1))
model.compile('sgd', 'mse')
model.summary()

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