OperatorNotAllowedInGraphError when running the example Usage code

[homerobse]

Hello!

Thanks for posting this code online. Trying to use it, when I run the code of ESN-usage.ipynb I get the following error:

---------------------------------------------------------------------------
OperatorNotAllowedInGraphError            Traceback (most recent call last)
<ipython-input-51-ec63cda2780d> in <module>
     19 # put all together in a keras sequential model
     20 model = keras.models.Sequential()
---> 21 model.add(recurrent_layer)
     22 model.add(output)

~/software/miniconda3/envs/recirc/lib/python3.8/site-packages/tensorflow/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
    515     self._self_setattr_tracking = False  # pylint: disable=protected-access
    516     try:
--> 517       result = method(self, *args, **kwargs)
    518     finally:
    519       self._self_setattr_tracking = previous_value  # pylint: disable=protected-access

~/software/miniconda3/envs/recirc/lib/python3.8/site-packages/tensorflow/python/keras/engine/sequential.py in add(self, layer)
    206           # and create the node connecting the current layer
    207           # to the input layer we just created.
--> 208           layer(x)
    209           set_inputs = True
    210 

~/software/miniconda3/envs/recirc/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py in __call__(self, inputs, initial_state, constants, **kwargs)
    658 
    659     if initial_state is None and constants is None:
--> 660       return super(RNN, self).__call__(inputs, **kwargs)
    661 
    662     # If any of `initial_state` or `constants` are specified and are Keras

~/software/miniconda3/envs/recirc/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
    949     # >> model = tf.keras.Model(inputs, outputs)
    950     if _in_functional_construction_mode(self, inputs, args, kwargs, input_list):
--> 951       return self._functional_construction_call(inputs, args, kwargs,
    952                                                 input_list)
    953 

~/software/miniconda3/envs/recirc/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py in _functional_construction_call(self, inputs, args, kwargs, input_list)
   1088           layer=self, inputs=inputs, build_graph=True, training=training_value):
   1089         # Check input assumptions set after layer building, e.g. input shape.
-> 1090         outputs = self._keras_tensor_symbolic_call(
   1091             inputs, input_masks, args, kwargs)
   1092 

~/software/miniconda3/envs/recirc/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py in _keras_tensor_symbolic_call(self, inputs, input_masks, args, kwargs)
    820       return nest.map_structure(keras_tensor.KerasTensor, output_signature)
    821     else:
--> 822       return self._infer_output_signature(inputs, args, kwargs, input_masks)
    823 
    824   def _infer_output_signature(self, inputs, args, kwargs, input_masks):

~/software/miniconda3/envs/recirc/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py in _infer_output_signature(self, inputs, args, kwargs, input_masks)
    861           # TODO(kaftan): do we maybe_build here, or have we already done it?
    862           self._maybe_build(inputs)
--> 863           outputs = call_fn(inputs, *args, **kwargs)
    864 
    865         self._handle_activity_regularization(inputs, outputs)

~/software/miniconda3/envs/recirc/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py in call(self, inputs, mask, training, initial_state, constants)
    792           new_states = [new_states]
    793         return output, new_states
--> 794     last_output, outputs, states = K.rnn(
    795         step,
    796         inputs,

~/software/miniconda3/envs/recirc/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py in wrapper(*args, **kwargs)
    199     """Call target, and fall back on dispatchers if there is a TypeError."""
    200     try:
--> 201       return target(*args, **kwargs)
    202     except (TypeError, ValueError):
    203       # Note: convert_to_eager_tensor currently raises a ValueError, not a

~/software/miniconda3/envs/recirc/lib/python3.8/site-packages/tensorflow/python/keras/backend.py in rnn(step_function, inputs, initial_states, go_backwards, mask, constants, unroll, input_length, time_major, zero_output_for_mask)
   4346     # output_time_zero is used to determine the cell output shape and its dtype.
   4347     # the value is discarded.
-> 4348     output_time_zero, _ = step_function(
   4349         input_time_zero, tuple(initial_states) + tuple(constants))
   4350     output_ta = tuple(

~/software/miniconda3/envs/recirc/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py in step(inputs, states)
    788       def step(inputs, states):
    789         states = states[0] if len(states) == 1 and is_tf_rnn_cell else states
--> 790         output, new_states = cell_call_fn(inputs, states, **kwargs)
    791         if not nest.is_nested(new_states):
    792           new_states = [new_states]

~/software/miniconda3/envs/recirc/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
   1010         with autocast_variable.enable_auto_cast_variables(
   1011             self._compute_dtype_object):
-> 1012           outputs = call_fn(inputs, *args, **kwargs)
   1013 
   1014         if self._activity_regularizer:

~/software/miniconda3/envs/recirc/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py in wrapper(*args, **kwargs)
    665       try:
    666         with conversion_ctx:
--> 667           return converted_call(f, args, kwargs, options=options)
    668       except Exception as e:  # pylint:disable=broad-except
    669         if hasattr(e, 'ag_error_metadata'):

~/software/miniconda3/envs/recirc/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py in converted_call(f, args, kwargs, caller_fn_scope, options)
    348   if conversion.is_in_allowlist_cache(f, options):
    349     logging.log(2, 'Allowlisted %s: from cache', f)
--> 350     return _call_unconverted(f, args, kwargs, options, False)
    351 
    352   if ag_ctx.control_status_ctx().status == ag_ctx.Status.DISABLED:

~/software/miniconda3/envs/recirc/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py in _call_unconverted(f, args, kwargs, options, update_cache)
    476 
    477   if kwargs is not None:
--> 478     return f(*args, **kwargs)
    479   return f(*args)
    480 

/mnt/d/projects/recirculation/esn/ESN.py in call(self, inputs, states)
    195 
    196         rkernel = self.setAlpha(self.recurrent_kernel_init)
--> 197         if self.alpha != self.alpha_store:
    198             self.clip_variables()
    199             self.echo_ratio.assign(self.echoStateRatio(rkernel))

~/software/miniconda3/envs/recirc/lib/python3.8/site-packages/tensorflow/python/framework/ops.py in __bool__(self)
    883       `TypeError`.
    884     """
--> 885     self._disallow_bool_casting()
    886 
    887   def __nonzero__(self):

~/software/miniconda3/envs/recirc/lib/python3.8/site-packages/tensorflow/python/framework/ops.py in _disallow_bool_casting(self)
    490     else:
    491       # Default: V1-style Graph execution.
--> 492       self._disallow_in_graph_mode("using a `tf.Tensor` as a Python `bool`")
    493 
    494   def _disallow_iteration(self):

~/software/miniconda3/envs/recirc/lib/python3.8/site-packages/tensorflow/python/framework/ops.py in _disallow_in_graph_mode(self, task)
    477 
    478   def _disallow_in_graph_mode(self, task):
--> 479     raise errors.OperatorNotAllowedInGraphError(
    480         "{} is not allowed in Graph execution. Use Eager execution or decorate"
    481         " this function with @tf.function.".format(task))

OperatorNotAllowedInGraphError: using a `tf.Tensor` as a Python `bool` is not allowed in Graph execution. Use Eager execution or decorate this function with @tf.function.

I tried running tf.executing_eagerly() and it returns True. And I also tried to use @tf.function above call function, but it still did not work.

Should I change the if self.alpha != self.alpha_store: condition to something else using tf.cond? Thank you for your help.

[homerobse]

I added dynamic=True (as suggested here for another error) and the code now appears to be working
recurrent_layer = tf.keras.layers.RNN(esn_cell, return_sequences=True, name="rnn", dynamic=True)

[homerobse]

I closed the issue, but it would be good to update the notebook if that is something essential for all current versions of Tensorflow