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I am having issues with using the 'stateful' feature when building stacked RNN using LSTMCell object. I am following the instructions on tensorflow on how to set 'stateful = True' by passing the 'batch_shape' to the input layer and the 'batch_input_shape' to the first cell, and also tried to set it in the second cell but it still does not work; tried various combinations but nothing works. The code only works with one cell but not with more than one. I am not sure what I am missing.

The problem I am working on is Sentiment Analysis. 292 is the sequence length I am passing to predict if the sentiment is 1 or 0.

I realize I could use the Sequential model of Keras but I would like to learn the finer features of tensorflow.

import tensorflow as tf

tf.reset_default_graph()

batch_size = 10
embed_size = 5
dropout = 0.5
n_unique_words = 102966
ntimesteps = 292


x = tf.placeholder(dtype = tf.int32, shape = (batch_size, ntimesteps), name='tf_x')
print(x)

y = tf.placeholder(dtype = tf.float32, shape = (batch_size), name = 'tf_y')
print(y)

embed_variable = tf.get_variable(name = 'embedding', shape = [n_unique_words, embed_size], initializer=tf.glorot_uniform_initializer)
print(embed_variable)

embed_x = tf.nn.embedding_lookup(embed_variable, x, name = 'embedded_x')
print(embed_x)

cell1 = tf.keras.layers.LSTMCell(units = 256, batch_input_shape = (10,292,5), dtype = tf.float32)#, dropout=dropout, name = 'lstm1')
print(cell1)


cell2 = tf.keras.layers.LSTMCell(units = 512, batch_input_shape = (10,292,5), dtype = tf.float32)#, name='lstm2', recurrent_dropout=0.5) 
print(cell2)


rnn_input = tf.keras.Input(batch_shape = (batch_size, 292, 5), dtype = tf.float32) #shape = (batch_size, 292))#
rnn_object = tf.keras.layers.RNN(cell = [cell1, cell2], batch_size = 10, return_sequences = True, 
 return_state = True, stateful = True, dtype = tf.float32)(rnn_input)


print(rnn_object)

Output:

Tensor("tf_x:0", shape=(10, 292), dtype=int32)
Tensor("tf_y:0", shape=(10,), dtype=float32)
<tf.Variable 'embedding:0' shape=(102966, 5) dtype=float32_ref>
Tensor("embedded_x/Identity:0", shape=(10, 292, 5), dtype=float32)
<tensorflow.python.keras.layers.recurrent.LSTMCell object at 0x7fd8088a3b38>
<tensorflow.python.keras.layers.recurrent.LSTMCell object at 0x7fd8088a3fd0>
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-3-641a99b06a11> in <module>
 32 rnn_input = tf.keras.Input(batch_shape = (batch_size, 292, 5), dtype = tf.float32) #shape = (batch_size, 292))#
 33 rnn_object = tf.keras.layers.RNN(cell = [cell1, cell2], batch_size = 10, return_sequences = True, 
---> 34 return_state = True, stateful = True, dtype = tf.float32)(rnn_input)
 35 
 36 

~/anaconda3/envs/condapy36/lib/python3.6/site-packages/tensorflow/python/keras/layers/recurrent.py in __call__(self, inputs, initial_state, constants, **kwargs)
 699 
 700 if initial_state is None and constants is None:
--> 701 return super(RNN, self).__call__(inputs, **kwargs)
 702 
 703 # If any of `initial_state` or `constants` are specified and are Keras

~/anaconda3/envs/condapy36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
 536 if not self.built:
 537 # Build layer if applicable (if the `build` method has been overridden).
--> 538 self._maybe_build(inputs)
 539 # We must set self.built since user defined build functions are not
 540 # constrained to set self.built.

~/anaconda3/envs/condapy36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in _maybe_build(self, inputs)
 1601 # Only call `build` if the user has manually overridden the build method.
 1602 if not hasattr(self.build, '_is_default'):
-> 1603 self.build(input_shapes)
 1604 
 1605 def __setattr__(self, name, value):

~/anaconda3/envs/condapy36/lib/python3.6/site-packages/tensorflow/python/keras/layers/recurrent.py in build(self, input_shape)
 634 ]
 635 if self.stateful:
--> 636 self.reset_states()
 637 self.built = True
 638 

~/anaconda3/envs/condapy36/lib/python3.6/site-packages/tensorflow/python/keras/layers/recurrent.py in reset_states(self, states)
 904 K.set_value(state,
 905 np.zeros([batch_size] +
--> 906 tensor_shape.as_shape(dim).as_list()))
 907 else:
 908 K.set_value(self.states[0], np.zeros(

~/anaconda3/envs/condapy36/lib/python3.6/site-packages/tensorflow/python/keras/backend.py in set_value(x, value)
 2831 (of the same shape).
 2832 """
-> 2833 value = np.asarray(value, dtype=dtype(x))
 2834 if ops.executing_eagerly_outside_functions():
 2835 x.assign(value)

~/anaconda3/envs/condapy36/lib/python3.6/site-packages/tensorflow/python/keras/backend.py in dtype(x)
 1013 ```
 1014 """
-> 1015 return x.dtype.base_dtype.name
 1016 
 1017 

AttributeError: 'list' object has no attribute 'dtype'