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Initializing GRU (or RNN in general) inside nested models in Keras

I need to build two Keras models:

gru_model

inital_state

wrapper_model

gru_model

gru_model

According to the output gru_model is built and called successfully, however wrapper_model fails during the call in the provided setting. Interestingly, if the initial_state param is not passed to GRU layer in gru_model, wrapper_model works as well.

gru_model

wrapper_model

initial_state

gru_model

wrapper_model

Please advise me on this issue. Thanks!

import numpy as np
from keras import Input, Model
from keras.layers import GRU

BATCH_SIZE = 2
SEQ_LEN = 3
EMB_SIZE = 4
HIDDEN_DIM = 5


def build_gru_model():
gru_input = Input(shape=(SEQ_LEN, EMB_SIZE), dtype='float32', name='gru_input')
gru_hs = Input(shape=(HIDDEN_DIM, ), dtype='float32', name='gru_hs')

output, updated_hs = GRU(
units=HIDDEN_DIM,
name='gru',
return_state=True
)(gru_input, initial_state=gru_hs) # wrapper_model fails to predict if inital_state is defined here

return Model(
inputs=[gru_input, gru_hs,],
outputs=[output, updated_hs],
name='gru_model')


def build_wrapper_model():
gru_input = Input(shape=(SEQ_LEN, EMB_SIZE), dtype='float32', name='caller_gru_input')
gru_hs = Input(shape=(HIDDEN_DIM, ), dtype='float32', name='caller_gru_hs')

output, updated_hs = gru_model(inputs=[gru_input, gru_hs])

return Model(
inputs=[gru_input, gru_hs],
outputs=[output, updated_hs],
name='caller_model')


if __name__ == '__main__':
ids_input = np.ones((BATCH_SIZE, SEQ_LEN, EMB_SIZE), dtype=np.float32)
hs_input = np.zeros((BATCH_SIZE, HIDDEN_DIM), dtype=np.float32)

gru_model = build_gru_model()
res_output, res_updated_hs = gru_model.predict([ids_input, hs_input]) # Works
print('ngru_model outputs:n'.format(res_output))
print('ngru_model hs:n'.format(res_updated_hs))

caller_model = build_wrapper_model()
res_output, res_updated_hs = caller_model.predict([ids_input, hs_input]) # Fails if inital_state is passed to GRU
print('nwrapper_model outputs:n'.format(res_output))
print('nwrapper_model hs:n'.format(res_updated_hs))


/home/nicolas/Code/test_gru_init.py
Using TensorFlow backend.
2018-08-08 16:44:55.013947: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

gru_model outputs:
[[-0.6254787 0.5550256 -0.14170723 -0.51687145 0.09609278]
[-0.6254787 0.5550256 -0.14170723 -0.51687145 0.09609278]]

gru_model hs:
[[-0.6254787 0.5550256 -0.14170723 -0.51687145 0.09609278]
[-0.6254787 0.5550256 -0.14170723 -0.51687145 0.09609278]]

Traceback (most recent call last):
File "/home/nicolas/Code/Replika/cakechat-internal/tools/test_gru_init.py", line 49, in <module>
res_output, res_updated_hs = caller_model.predict([ids_input, hs_input]) # Fails if inital_state is passed to GRU
File "/home/nicolas/Code/keras/engine/training.py", line 1172, in predict
steps=steps)
File "/home/nicolas/Code/keras/engine/training_arrays.py", line 297, in predict_loop
batch_outs = f(ins_batch)
File "/home/nicolas/Code/keras/backend/tensorflow_backend.py", line 2661, in __call__
return self._call(inputs)
File "/home/nicolas/Code/keras/backend/tensorflow_backend.py", line 2631, in _call
fetched = self._callable_fn(*array_vals)
File "/home/nicolas/Code/tensorflow/python/client/session.py", line 1454, in __call__
self._session._session, self._handle, args, status, None)
File "/home/nicolas/Code/tensorflow/python/framework/errors_impl.py", line 519, in __exit__
c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'gru_hs' with dtype float and shape [?,5]
[[Node: gru_hs = Placeholder[dtype=DT_FLOAT, shape=[?,5], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

Process finished with exit code 1


--

Apparently there is a bug in Keras that causes this problem. For the details see https://github.com/keras-team/keras/issues/9385

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