我为我的计算机视觉问题构建了一个类似DenseNet/Unet的结构。但是当我将图像和掩码输入到model.fit()方法时，后台的Tensorflow在某个地方移除了我的输入的通道部分，从而向第一个卷积层发送了形状不正确的张量。

我的Image_list对象的形状为（切片数 x 1 x 512 x 512）。因此，每个切片的理想输入应该是1x512x512。

错误是：

WARNING:tensorflow:Model was constructed with shape (None, 1, 512, 512) for input Tensor("input_1:0", shape=(None, 1, 512, 512), dtype=float32), but it was called on an input with incompatible shape (None, 512, 512).

这让我认为可能输入层存在某些错误？

ValueError                                Traceback (most recent call last)<ipython-input-8-a8d0a842563e> in <module>()      1 #obj= ImageSequence(None, None,1)----> 2 model.fit(image_list,mask_list, epochs=1 )10 frames/usr/local/lib/python3.6/dist-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--> 108       return method(self, *args, **kwargs)    109     110     # Running inside `run_distribute_coordinator` already./usr/local/lib/python3.6/dist-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)-> 1098               tmp_logs = train_function(iterator)   1099               if data_handler.should_sync:   1100                 context.async_wait()/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)    778       else:    779         compiler = "nonXla"--> 780         result = self._call(*args, **kwds)    781     782       new_tracing_count = self._get_tracing_count()/usr/local/lib/python3.6/dist-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 = []--> 823       self._initialize(args, kwds, add_initializers_to=initializers)    824     finally:    825       # At this point we know that the initialization is complete (or less/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)    695     self._concrete_stateful_fn = (    696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access--> 697             *args, **kwds))    698     699     def invalid_creator_scope(*unused_args, **unused_kwds):/usr/local/lib/python3.6/dist-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:-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)   2856     return graph_function   2857 /usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)   3211    3212       self._function_cache.missed.add(call_context_key)-> 3213       graph_function = self._create_graph_function(args, kwargs)   3214       self._function_cache.primary[cache_key] = graph_function   3215       return graph_function, args, kwargs/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)   3073             arg_names=arg_names,   3074             override_flat_arg_shapes=override_flat_arg_shapes,-> 3075             capture_by_value=self._capture_by_value),   3076         self._function_attributes,   3077         function_spec=self.function_spec,/usr/local/lib/python3.6/dist-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 --> 986       func_outputs = python_func(*func_args, **func_kwargs)    987     988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,/usr/local/lib/python3.6/dist-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.--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)    601     weak_wrapped_fn = weakref.ref(wrapped_fn)    602 /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)    971           except Exception as e:  # pylint:disable=broad-except    972             if hasattr(e, "ag_error_metadata"):--> 973               raise e.ag_error_metadata.to_exception(e)    974             else:    975               raiseValueError: in user code:    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:806 train_function  *        return step_function(self, iterator)    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:796 step_function  **        outputs = model.distribute_strategy.run(run_step, args=(data,))    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 run        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica        return self._call_for_each_replica(fn, args, kwargs)    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica        return fn(*args, **kwargs)    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:789 run_step  **        outputs = model.train_step(data)    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:747 train_step        y_pred = self(x, training=True)    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:985 __call__        outputs = call_fn(inputs, *args, **kwargs)    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py:386 call        inputs, training=training, mask=mask)    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py:508 _run_internal_graph        outputs = node.layer(*args, **kwargs)    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:976 __call__        self.name)    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/input_spec.py:196 assert_input_compatibility        str(x.shape.as_list()))    ValueError: Input 0 of layer conv2d is incompatible with the layer: : expected min_ndim=4, found ndim=3. Full shape received: [None, 512, 512]

我的代码可以在这里找到

样本数据集：这里

回答：

你的数据生成器返回一个NumPy数组列表，一个数组列表可以被解释为多个输入。在调用model.fit()之前尝试将你的数据转换为张量形式：

`image_list = tf.convert_to_tensor(image_list)mask_list = tf.convert_to_tensor(mask_list)`