merging recurrent layers with dense layer in Keras

问题:

I want to build a neural network where the two first layers are feedforward and the last one is recurrent. here is my code :

model = Sequential()
model.add(Dense(150, input_dim=23,init='normal',activation='relu'))
model.add(Dense(80,activation='relu',init='normal'))
model.add(SimpleRNN(2,init='normal')) 
adam =OP.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
model.compile(loss="mean_squared_error", optimizer="rmsprop")  

and I get this error :

Exception: Input 0 is incompatible with layer simplernn_11: expected  ndim=3, found ndim=2.
model.compile(loss='mse', optimizer=adam)

回答1:

It is correct that in Keras, RNN layer expects input as (nb_samples, time_steps, input_dim). However, if you want to add RNN layer after a Dense layer, you still can do that after reshaping the input for the RNN layer. Reshape can be used both as a first layer and also as an intermediate layer in a sequential model. Examples are given below:

Reshape as first layer in a Sequential model

model = Sequential()
model.add(Reshape((3, 4), input_shape=(12,)))
# now: model.output_shape == (None, 3, 4)
# note: `None` is the batch dimension

Reshape as an intermediate layer in a Sequential model

model.add(Reshape((6, 2)))
# now: model.output_shape == (None, 6, 2)

For example, if you change your code in the following way, then there will be no error. I have checked it and the model compiled without any error reported. You can change the dimension as per your need.

from keras.models import Sequential
from keras.layers import Dense, SimpleRNN, Reshape
from keras.optimizers import Adam

model = Sequential()
model.add(Dense(150, input_dim=23,init='normal',activation='relu'))
model.add(Dense(80,activation='relu',init='normal'))
model.add(Reshape((1, 80)))
model.add(SimpleRNN(2,init='normal')) 
adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
model.compile(loss="mean_squared_error", optimizer="rmsprop")

回答2:

In Keras, you cannot put a Reccurrent layer after a Dense layer because the Dense layer gives output as (nb_samples, output_dim). However, a Recurrent layer expects input as (nb_samples, time_steps, input_dim). So, a Dense layer gives a 2-D output, but a Recurrent layer expects a 3-D input. However, you can do the reverse, i.e., put a Dense layer after a Recurrent layer.