Python 为什么可以'；我不能在这张图片上运行tensorflow CNN模型吗？

我从头创建了一个TensorFlow CNN来识别某些类型的动物。我相信该模型是有效的，因为我正在获取有关培训数据的数据，并且在运行代码时在我的目录中看到一个新文件夹。当我试图运行代码来预测一个新的单一图像时，我得到了这个错误。我是TensorFlow的新手，所以我不确定自己做错了什么。该映像位于主目录中，是一个.jpg映像。如果你需要更多信息，请告诉我。谢谢

CATEGORIES = ["cane", "cavallo", "elefante", "farfalla", "gallina",
      "gatto", "mucca", "pecora", "ragno", "scoiattolo"]
def prepare(file):
IMG_SIZE = 50
img_array = cv2.imread(file, cv2.IMREAD_GRAYSCALE)
new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
return new_array.reshape(-1, IMG_SIZE, IMG_SIZE, 1)
model = tf.keras.models.load_model("CNN.model")
from PIL import Image
import numpy as np
from skimage import transform
image = load('test.jpg')
model.predict(image)
prediction = model.predict([image])
prediction = list(prediction[0])
print(CATEGORIES[prediction.index(max(prediction))])

这就是错误所在

ValueError                                Traceback (most recent call 
last)
<ipython-input-4-5c3fc0a5d50b> in <module>
     14 from skimage import transform
     15 image = load('test.jpg')
---> 16 model.predict(image)
     17 prediction = model.predict([image])
     18 prediction = list(prediction[0])

~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
     86       raise ValueError('{} is not supported in multi-worker mode.'.format(
     87           method.__name__))
---> 88     return method(self, *args, **kwargs)
     89 
     90   return tf_decorator.make_decorator(

~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
   1266           for step in data_handler.steps():
   1267             callbacks.on_predict_batch_begin(step)
-> 1268             tmp_batch_outputs = predict_function(iterator)
   1269             # Catch OutOfRangeError for Datasets of unknown size.
   1270             # This blocks until the batch has finished executing.

~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
    578         xla_context.Exit()
    579     else:
--> 580       result = self._call(*args, **kwds)
    581 
    582     if tracing_count == self._get_tracing_count():

~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
    625       # This is the first call of __call__, so we have to initialize.
    626       initializers = []
--> 627       self._initialize(args, kwds, add_initializers_to=initializers)
    628     finally:
    629       # At this point we know that the initialization is complete (or less

~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
    504     self._concrete_stateful_fn = (
    505         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
--> 506             *args, **kwds))
    507 
    508     def invalid_creator_scope(*unused_args, **unused_kwds):

~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
   2444       args, kwargs = None, None
   2445     with self._lock:
-> 2446       graph_function, _, _ = self._maybe_define_function(args, kwargs)
   2447     return graph_function
   2448 

~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
   2775 
   2776       self._function_cache.missed.add(call_context_key)
-> 2777       graph_function = self._create_graph_function(args, kwargs)
   2778       self._function_cache.primary[cache_key] = graph_function
   2779       return graph_function, args, kwargs

~/opt/anaconda3/lib/python3.7/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,
-> 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

~/opt/anaconda3/lib/python3.7/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 
--> 981       func_outputs = python_func(*func_args, **func_kwargs)
    982 
    983       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/opt/anaconda3/lib/python3.7/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.
--> 441         return weak_wrapped_fn().__wrapped__(*args, **kwds)
    442     weak_wrapped_fn = weakref.ref(wrapped_fn)
    443 

~/opt/anaconda3/lib/python3.7/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"):
--> 968               raise e.ag_error_metadata.to_exception(e)
    969             else:
    970               raise

ValueError: in user code:

    /Users/rin/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:1147 predict_function  *
        outputs = self.distribute_strategy.run(
    /Users/ron/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:951 run  **
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /Users/rn/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /Users/romin/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica
        return fn(*args, **kwargs)
    /Users/rin/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:1122 predict_step  **
        return self(x, training=False)
    /Users/rn/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py:886 __call__
        self.name)
    /Users/rkin/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/input_spec.py:216 assert_input_compatibility
        ' but received input with shape ' + str(shape))

    ValueError: Input 0 of layer sequential_10 is incompatible with the layer: expected axis -1 of input shape to have value 1 but received input with shape [None, 256, 256, 3]

---

因此，错误很明显

expected axis -1 of input shape to have value 1 but received input with shape [None, 256, 256, 3]

错误表明，由于RGB图像的原因，提供给模型的输入通道为

3

，但您的模型需要通道为

1

的图像

这意味着您的模型需要一个

灰度

图像，并且您在预测时提供了一个

RGB

图像

您应该提供一个具有

[None，256，256，1]

的图像，其中

None

表示

batch\u size

您能通过添加型号代码来确认这一点吗

更新：

你刚刚更新了你的问题，所以在这里我可以看到你正在用灰度图像训练你的模型

X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)

---

但是我想，当

预测时

您正在使用RGB图像呼叫。

我想您只是忘记应用def prepare（文件）

你能试试吗

model.predict(prepare(image))

---

这就是为什么RGB和扩展灰度不匹配的原因。因为加载它的方式是RGB，因为没有应用prepare函数。申请后，它应该是灰度图像，应该可以工作。

请提供完整的代码，然后我想错误消息“ValueError:in user code:”在您的帖子中被截断了，在“：”之后出现了一些内容？抱歉，它被切断了。我刚刚更新了它。我想，与模型的设置相比，在加载图像时，您的图像预处理中缺少了一些东西。为了追踪它，最好有完整的代码。好的，我会添加它。给我两个minutes@StatTistician它是更新的否，这是不正确的，因为这是在def prepare（文件）中完成的。谢谢，我现在明白了。这是实际的问题，您对函数调用是正确的，但这并不意味着我的答案是错误的。好吧，但这是误导/不是问题。无意冒犯，很高兴你在这里！结果如何？

model.predict(prepare(image))