title: ValueError: Dimensions must be equal keras
body: <p>I have a model of this structure:</p>
  <pre><code>filter_size = (3,3)
  filters = 32
  pool = 2
  
  input_layer = keras.Input(shape=(100,300,1))
  
  conv_extractor = layers.Conv2D(filters,filter_size, activation='relu')(input_layer)
  conv_extractor = layers.MaxPooling2D(pool_size=(pool, pool))(conv_extractor)
  conv_extractor = layers.Conv2D(filters,filter_size, activation='relu')(conv_extractor)
  conv_extractor = layers.MaxPooling2D(pool_size=(pool, pool))(conv_extractor)
  
  #conv_extractor = layers.Reshape(target_shape=(100 // (pool ** 2), (100 // (pool ** 2)) * filters))(conv_extractor)
  shape = ((100 // 4), (300 // 4) * 32)
  #conv_extractor = layers.Dense(512, activation='relu')(conv_extractor)
  conv_extractor = layers.Reshape(target_shape=(23,2336))(conv_extractor)
  
  gru_1 = GRU(512, return_sequences=True)(conv_extractor)
  gru_1b = GRU(512, return_sequences=True, go_backwards=True)(conv_extractor)
  gru1_merged = add([gru_1, gru_1b])
  gru_2 = GRU(512, return_sequences=True)(gru1_merged)
  gru_2b = GRU(512, return_sequences=True, go_backwards=True)(gru1_merged)
  
  inner = layers.Dense(30, activation='LeakyReLU')(concatenate([gru_2, gru_2b]))
  inner = layers.Dense(10, activation='LeakyReLU')(inner)
  inner = layers.Dense(3, activation='LeakyReLU')(inner)
  model = Model(input_layer,inner)
  model.compile(loss = &quot;poisson&quot;, optimizer = optimizers.Adam(2e-4), metrics=['accuracy'])
  </code></pre>
  <p>All of the above seems to work, when trying to train using <code>model.fit(x_train, y_train,epochs=3)</code> I get the following error:</p>
  <pre><code>ValueError                                Traceback (most recent call last)
  /var/folders/nc/c4mgwn897qbg8g52tp3mhbjr0000gp/T/ipykernel_3907/1977739458.py in &lt;module&gt;
  ----&gt; 1 model.fit(x_train, y_train,epochs=3)
  
  ~/opt/anaconda3/lib/python3.8/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)
     1181                 _r=1):
     1182               callbacks.on_train_batch_begin(step)
  -&gt; 1183               tmp_logs = self.train_function(iterator)
     1184               if data_handler.should_sync:
     1185                 context.async_wait()
  
  ~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
      887 
      888       with OptionalXlaContext(self._jit_compile):
  --&gt; 889         result = self._call(*args, **kwds)
      890 
      891       new_tracing_count = self.experimental_get_tracing_count()
  
  ~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
      931       # This is the first call of __call__, so we have to initialize.
      932       initializers = []
  --&gt; 933       self._initialize(args, kwds, add_initializers_to=initializers)
      934     finally:
      935       # At this point we know that the initialization is complete (or less
  
  ~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
      761     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
      762     self._concrete_stateful_fn = (
  --&gt; 763         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
      764             *args, **kwds))
      765 
  
  ~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
     3048       args, kwargs = None, None
     3049     with self._lock:
  -&gt; 3050       graph_function, _ = self._maybe_define_function(args, kwargs)
     3051     return graph_function
     3052 
  
  ~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
     3442 
     3443           self._function_cache.missed.add(call_context_key)
  -&gt; 3444           graph_function = self._create_graph_function(args, kwargs)
     3445           self._function_cache.primary[cache_key] = graph_function
     3446 
  
  ~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
     3277     arg_names = base_arg_names + missing_arg_names
     3278     graph_function = ConcreteFunction(
  -&gt; 3279         func_graph_module.func_graph_from_py_func(
     3280             self._name,
     3281             self._python_function,
  
  ~/opt/anaconda3/lib/python3.8/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)
      997         _, original_func = tf_decorator.unwrap(python_func)
      998 
  --&gt; 999       func_outputs = python_func(*func_args, **func_kwargs)
     1000 
     1001       # invariant: `func_outputs` contains only Tensors, CompositeTensors,
  
  ~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
      670         # the function a weak reference to itself to avoid a reference cycle.
      671         with OptionalXlaContext(compile_with_xla):
  --&gt; 672           out = weak_wrapped_fn().__wrapped__(*args, **kwds)
      673         return out
      674 
  
  ~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
      984           except Exception as e:  # pylint:disable=broad-except
      985             if hasattr(e, &quot;ag_error_metadata&quot;):
  --&gt; 986               raise e.ag_error_metadata.to_exception(e)
      987             else:
      988               raise
  
  ValueError: in user code:
  
      /Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:855 train_function  *
          return step_function(self, iterator)
      /Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:845 step_function  **
          outputs = model.distribute_strategy.run(run_step, args=(data,))
      /Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:1285 run
          return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
      /Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2833 call_for_each_replica
          return self._call_for_each_replica(fn, args, kwargs)
      /Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:3608 _call_for_each_replica
          return fn(*args, **kwargs)
      /Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:838 run_step  **
          outputs = model.train_step(data)
      /Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:800 train_step
          self.compiled_metrics.update_state(y, y_pred, sample_weight)
      /Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/compile_utils.py:460 update_state
          metric_obj.update_state(y_t, y_p, sample_weight=mask)
      /Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/utils/metrics_utils.py:86 decorated
          update_op = update_state_fn(*args, **kwargs)
      /Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/metrics.py:177 update_state_fn
          return ag_update_state(*args, **kwargs)
      /Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/metrics.py:664 update_state  **
          matches = ag_fn(y_true, y_pred, **self._fn_kwargs)
      /Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:206 wrapper
          return target(*args, **kwargs)
      /Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/metrics.py:3485 sparse_categorical_accuracy
          return math_ops.cast(math_ops.equal(y_true, y_pred), backend.floatx())
      /Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:206 wrapper
          return target(*args, **kwargs)
      /Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/math_ops.py:1729 equal
          return gen_math_ops.equal(x, y, name=name)
      /Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/gen_math_ops.py:3228 equal
          _, _, _op, _outputs = _op_def_library._apply_op_helper(
      /Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/op_def_library.py:748 _apply_op_helper
          op = g._create_op_internal(op_type_name, inputs, dtypes=None,
      /Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py:599 _create_op_internal
          return super(FuncGraph, self)._create_op_internal(  # pylint: disable=protected-access
      /Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:3557 _create_op_internal
          ret = Operation(
      /Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:2041 __init__
          self._c_op = _create_c_op(self._graph, node_def, inputs,
      /Users/jr123456jr987654321/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:1883 _create_c_op
          raise ValueError(str(e))
  
      ValueError: Dimensions must be equal, but are 3 and 23 for '{{node Equal}} = Equal[T=DT_FLOAT, incompatible_shape_error=true](Cast_1, Cast_2)' with input shapes: [?,3], [?,23].
  </code></pre>
  <p>FYI: the shape of x_train is <code>2000,100,300,1</code> and y_train is <code>2000,3</code></p>