title: AssertionError: Could not compute output Tensor
body: <p>I am trying to build a model that takes multiple inputs and multiple outputs using a functional API. I followed <a href="https://www.tensorflow.org/guide/keras/functional" rel="noreferrer">this</a> to create the code.</p>
  
  <pre><code>def create_model_multiple():
      input1 = tf.keras.Input(shape=(13,), name = 'I1')
      input2 = tf.keras.Input(shape=(6,), name = 'I2')
      hidden1 = tf.keras.layers.Dense(units = 4, activation='relu')(input1)
      hidden2 = tf.keras.layers.Dense(units = 4, activation='relu')(input2)
      merge = tf.keras.layers.concatenate([hidden1, hidden2])
      hidden3 = tf.keras.layers.Dense(units = 3, activation='relu')(merge)
      output1 = tf.keras.layers.Dense(units = 2, activation='softmax', name ='O1')(hidden3)
      output2 = tf.keras.layers.Dense(units = 2, activation='softmax', name = 'O2')(hidden3)
      model = tf.keras.models.Model(inputs = [input1,input2], outputs = [output1,output2])
      model.compile(optimizer='adam',
                    loss='sparse_categorical_crossentropy',
                    metrics=['accuracy'])
      return model
  </code></pre>
  
  <p>My model.fit command looks like this:</p>
  
  <pre><code>history = model.fit({'I1':train_data, 'I2':new_train_data},
                      {'O1':train_labels, 'O2': new_target_label},
                      validation_data=(val_data,val_labels),
                      epochs=100,
                      verbose = 1)
  </code></pre>
  
  <p>The shapes of input data are as follows:<br>
  train_data is (192,13)<br>
  new_train_data is (192,6)<br>
  train-labels,new_target_labels is (192,)<br>
  The code runs for a few steps then raises this error:</p>
  
  <pre><code>Epoch 1/100
  1/6 [====&gt;.........................] - ETA: 0s - loss: 360.3317 - O1_loss: 127.8019 - O2_loss: 232.5298 - O1_accuracy: 0.3438 - O2_accuracy: 0.4062
  ---------------------------------------------------------------------------
  AssertionError                            Traceback (most recent call last)
  &lt;ipython-input-29-db61ad0a9d8b&gt; in &lt;module&gt;
        3                     validation_data=(val_data,val_labels),
        4                     epochs=100,
  ----&gt; 5                     verbose = 1)
  
  c:\users\aniket\documents\aniket\learning-ml\ml_env\lib\site-packages\tensorflow\python\keras\engine\training.py in _method_wrapper(self, *args, **kwargs)
       64   def _method_wrapper(self, *args, **kwargs):
       65     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
  ---&gt; 66       return method(self, *args, **kwargs)
       67 
       68     # Running inside `run_distribute_coordinator` already.
  
  c:\users\aniket\documents\aniket\learning-ml\ml_env\lib\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
      870               workers=workers,
      871               use_multiprocessing=use_multiprocessing,
  --&gt; 872               return_dict=True)
      873           val_logs = {'val_' + name: val for name, val in val_logs.items()}
      874           epoch_logs.update(val_logs)
  
  c:\users\aniket\documents\aniket\learning-ml\ml_env\lib\site-packages\tensorflow\python\keras\engine\training.py in _method_wrapper(self, *args, **kwargs)
       64   def _method_wrapper(self, *args, **kwargs):
       65     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
  ---&gt; 66       return method(self, *args, **kwargs)
       67 
       68     # Running inside `run_distribute_coordinator` already.
  
  c:\users\aniket\documents\aniket\learning-ml\ml_env\lib\site-packages\tensorflow\python\keras\engine\training.py in evaluate(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, return_dict)
     1079                 step_num=step):
     1080               callbacks.on_test_batch_begin(step)
  -&gt; 1081               tmp_logs = test_function(iterator)
     1082               # Catch OutOfRangeError for Datasets of unknown size.
     1083               # This blocks until the batch has finished executing.
  
  c:\users\aniket\documents\aniket\learning-ml\ml_env\lib\site-packages\tensorflow\python\eager\def_function.py in __call__(self, *args, **kwds)
      578         xla_context.Exit()
      579     else:
  --&gt; 580       result = self._call(*args, **kwds)
      581 
      582     if tracing_count == self._get_tracing_count():
  
  c:\users\aniket\documents\aniket\learning-ml\ml_env\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
      616       # In this case we have not created variables on the first call. So we can
      617       # run the first trace but we should fail if variables are created.
  --&gt; 618       results = self._stateful_fn(*args, **kwds)
      619       if self._created_variables:
      620         raise ValueError("Creating variables on a non-first call to a function"
  
  c:\users\aniket\documents\aniket\learning-ml\ml_env\lib\site-packages\tensorflow\python\eager\function.py in __call__(self, *args, **kwargs)
     2417     """Calls a graph function specialized to the inputs."""
     2418     with self._lock:
  -&gt; 2419       graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
     2420     return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
     2421 
  
  c:\users\aniket\documents\aniket\learning-ml\ml_env\lib\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self, args, kwargs)
     2772           and self.input_signature is None
     2773           and call_context_key in self._function_cache.missed):
  -&gt; 2774         return self._define_function_with_shape_relaxation(args, kwargs)
     2775 
     2776       self._function_cache.missed.add(call_context_key)
  
  c:\users\aniket\documents\aniket\learning-ml\ml_env\lib\site-packages\tensorflow\python\eager\function.py in _define_function_with_shape_relaxation(self, args, kwargs)
     2704         relaxed_arg_shapes)
     2705     graph_function = self._create_graph_function(
  -&gt; 2706         args, kwargs, override_flat_arg_shapes=relaxed_arg_shapes)
     2707     self._function_cache.arg_relaxed[rank_only_cache_key] = graph_function
     2708 
  
  c:\users\aniket\documents\aniket\learning-ml\ml_env\lib\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
     2665             arg_names=arg_names,
     2666             override_flat_arg_shapes=override_flat_arg_shapes,
  -&gt; 2667             capture_by_value=self._capture_by_value),
     2668         self._function_attributes,
     2669         # Tell the ConcreteFunction to clean up its graph once it goes out of
  
  c:\users\aniket\documents\aniket\learning-ml\ml_env\lib\site-packages\tensorflow\python\framework\func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
      979         _, original_func = tf_decorator.unwrap(python_func)
      980 
  --&gt; 981       func_outputs = python_func(*func_args, **func_kwargs)
      982 
      983       # invariant: `func_outputs` contains only Tensors, CompositeTensors,
  
  c:\users\aniket\documents\aniket\learning-ml\ml_env\lib\site-packages\tensorflow\python\eager\def_function.py in wrapped_fn(*args, **kwds)
      439         # __wrapped__ allows AutoGraph to swap in a converted function. We give
      440         # the function a weak reference to itself to avoid a reference cycle.
  --&gt; 441         return weak_wrapped_fn().__wrapped__(*args, **kwds)
      442     weak_wrapped_fn = weakref.ref(wrapped_fn)
      443 
  
  c:\users\aniket\documents\aniket\learning-ml\ml_env\lib\site-packages\tensorflow\python\framework\func_graph.py in wrapper(*args, **kwargs)
      966           except Exception as e:  # pylint:disable=broad-except
      967             if hasattr(e, "ag_error_metadata"):
  --&gt; 968               raise e.ag_error_metadata.to_exception(e)
      969             else:
      970               raise
  
  AssertionError: in user code:
  
      c:\users\aniket\documents\aniket\learning-ml\ml_env\lib\site-packages\tensorflow\python\keras\engine\training.py:941 test_function  *
          outputs = self.distribute_strategy.run(
      c:\users\aniket\documents\aniket\learning-ml\ml_env\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:951 run  **
          return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
      c:\users\aniket\documents\aniket\learning-ml\ml_env\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2290 call_for_each_replica
          return self._call_for_each_replica(fn, args, kwargs)
      c:\users\aniket\documents\aniket\learning-ml\ml_env\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2649 _call_for_each_replica
          return fn(*args, **kwargs)
      c:\users\aniket\documents\aniket\learning-ml\ml_env\lib\site-packages\tensorflow\python\keras\engine\training.py:909 test_step  **
          y_pred = self(x, training=False)
      c:\users\aniket\documents\aniket\learning-ml\ml_env\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:927 __call__
          outputs = call_fn(cast_inputs, *args, **kwargs)
      c:\users\aniket\documents\aniket\learning-ml\ml_env\lib\site-packages\tensorflow\python\keras\engine\network.py:719 call
          convert_kwargs_to_constants=base_layer_utils.call_context().saving)
      c:\users\aniket\documents\aniket\learning-ml\ml_env\lib\site-packages\tensorflow\python\keras\engine\network.py:899 _run_internal_graph
          assert str(id(x)) in tensor_dict, 'Could not compute output ' + str(x)
  
      AssertionError: Could not compute output Tensor("O1_6/Identity:0", shape=(None, 2), dtype=float32)
  </code></pre>
  
  <p>The jupyter-notebook with complete code is <a href="https://github.com/aniketbote/learning-ML/blob/master/Class/6_test.ipynb" rel="noreferrer">here</a></p>