title: Localisation CNN in Keras
body: <p>I have a machine learning CNN which is supposed to classify and localise objects in present in a image. The input is a RGB image of dimensions (448, 448, 3), and the output is a 24 (for the bounding box coordinates, as there are multiple objects in a image, a maximum of 6, and 6 id's of the image present. Here is how I defined the model:</p>
  <pre><code>def define_model():
  
      input = Input(shape = (448,448,3)
      x = Conv2D(32, (3,3), activation = 'relu')(input)
      x = MaxPooling2D((3,3))(x)
      x = Conv2D(64, (3,3), activation = 'relu')(x)
      x = MaxPooling2D((3,3))(x)
      x = Conv2D(64, (3,3), activation = 'relu')(x)
      x = Conv2D(32, (3,3), activation = 'relu')(x)
      x = GlobalAveragePooling2D()(x)
      
      classification_output_head = Flatten()(x)
      classification_output_head = Dropout(0.1)(classification_output_head)
      classification_output_head = Dense(6, activation = 'sigmoid', name = 'class_of_objs')(classification_output_head)
      
      regression_for_bounding_box_output_head = Flatten()(x)
      regression_for_bounding_box_output_head = Dense(64, activation = 'relu')((regression_for_bounding_box_output_head))
      regression_for_bounding_box_output_head = Dense(32, activation = 'relu')(regression_for_bounding_box_output_head)
      regression_for_bounding_box_output_head = Dense(24, activation =  'sigmoid', name = 'bounding_box')(regression_for_bounding_box_output_head)
  
  return Model(inputs=[input], outputs=[classification_output_head, regression_for_bounding_box_output_head])
  </code></pre>
  <p>I then define the model:</p>
  <pre><code>model = define_model()
  </code></pre>
  <p>Heres the rest:</p>
  <pre><code>losses = {'class_of_objs': 'sparse_categorical_crossentropy',
            'bounding_box': 'mse'}
  model.compile('adam', loss=losses, metrics=['mae'])
  tloader = tf.data.Dataset.from_tensor_slices((x, y_class, y_bbox))
  model.fit(tloader,epochs=100)
  </code></pre>
  <p>When I run model.fit I get this error:</p>
  <pre><code>Epoch 1/100
  
  WARNING:tensorflow:Model was constructed with shape (None, 448, 448, 3) for input Tensor(&quot;input_25:0&quot;, shape=(None, 448, 448, 3), dtype=float32), but it was called on an input with incompatible shape (448, 448, 3).
  --------------------------------------------------------------------------- ValueError                                Traceback (most recent call last) &lt;ipython-input-308-70dc0cc6919c&gt; in &lt;module&gt;
  ----&gt; 1 model.fit(tloader,epochs=100)
  
  ~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
      106   def _method_wrapper(self, *args, **kwargs):
      107     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
  --&gt; 108       return method(self, *args, **kwargs)
      109 
      110     # Running inside `run_distribute_coordinator` already.
  
  ~/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)    1096                 batch_size=batch_size):   1097               callbacks.on_train_batch_begin(step)
  -&gt; 1098               tmp_logs = train_function(iterator)    1099               if data_handler.should_sync:    1100                 context.async_wait()
  
  ~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
      778       else:
      779         compiler = &quot;nonXla&quot;
  --&gt; 780         result = self._call(*args, **kwds)
      781 
      782       new_tracing_count = self._get_tracing_count()
  
  ~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
      821       # This is the first call of __call__, so we have to initialize.
      822       initializers = []
  --&gt; 823       self._initialize(args, kwds, add_initializers_to=initializers)
      824     finally:
      825       # 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)
      694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
      695     self._concrete_stateful_fn = (
  --&gt; 696         self._stateful_fn._get_concrete_function_internal_garbage_collected( 
  # pylint: disable=protected-access
      697             *args, **kwds))
      698 
  
  ~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args,
  **kwargs)    2853       args, kwargs = None, None    2854     with self._lock:
  -&gt; 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)    2856     return graph_function    2857 
  
  ~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)    3211     3212       self._function_cache.missed.add(call_context_key)
  -&gt; 3213       graph_function = self._create_graph_function(args, kwargs)    3214       self._function_cache.primary[cache_key] = graph_function    3215       return graph_function, args, kwargs
  
  ~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)    3063     arg_names = base_arg_names + missing_arg_names    3064     graph_function = ConcreteFunction(
  -&gt; 3065         func_graph_module.func_graph_from_py_func(    3066             self._name,    3067             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)
      984         _, original_func = tf_decorator.unwrap(python_func)
      985 
  --&gt; 986       func_outputs = python_func(*func_args, **func_kwargs)
      987 
      988       # 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)
      598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
      599         # the function a weak reference to itself to avoid a reference cycle.
  --&gt; 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
      601     weak_wrapped_fn = weakref.ref(wrapped_fn)
      602 
  
  ~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
      971           except Exception as e:  # pylint:disable=broad-except
      972             if hasattr(e, &quot;ag_error_metadata&quot;):
  --&gt; 973               raise e.ag_error_metadata.to_exception(e)
      974             else:
      975               raise
  </code></pre>
  <p>How do I fix it</p>