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?
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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