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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'