我已经开始研究Keras，我的水平很基本，我在代码中收到无效的参数错误，正在使用Keras加载权重并进行一些预测。我尝试使用Keras进行更新是因为我认为这是Keras版本的问题，但这不是因为Keras版本问题出现在以前的版本中，而该版本的版本已经相当枯竭。然后我尝试更新TensorFlow和其他库，因为从教程中我知道可能会出现这些问题。我找到了此链接https://github.com/raghakot/keras-vis/pull/120/files/74f2d086feb9556cbb2f1cfd90d6d4f8c21f520d，但没有从该链接中学到如何解决此错误。我希望您能帮助我解决这个无效的参数错误，到目前为止我还没有任何信息。 如果可以的话，请帮助我，因为我确实需要这样做，所以我被困在这里。
img_width, img_height=300,300
# predicting images
img = image.load_img('omnglot.jpg', target_size=(img_width, img_height))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
images = np.vstack([x])
print(images)
classes = model.predict_classes(images, batch_size=32)
#print(classes)
# predicting multiple images at once
img = image.load_img('omnglot2.jpg', target_size=(img_width, img_height))
y = image.img_to_array(img)
y = np.expand_dims(y, axis=0)
# pass the list of multiple images np.vstack()
images = np.vstack([x, y])
classes = model.predict_classes(images, batch_size=32)
# print the classes, the images belong to
print(classes)
print(classes[0])
print(classes[0][0])
InvalidArgumentError Traceback (most recent call last)
<ipython-input-10-4fda4804f4b7> in <module>
7 images = np.vstack([x])
8 print(images)
----> 9 classes = model.predict_classes(images, batch_size=32)
10 #print(classes)
11
C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\sequential.py in predict_classes(self, x, batch_size, verbose)
265 A numpy array of class predictions.
266 """
--> 267 proba = self.predict(x, batch_size=batch_size, verbose=verbose)
268 if proba.shape[-1] > 1:
269 return proba.argmax(axis=-1)
C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py in predict(self, x, batch_size, verbose, steps)
1167 batch_size=batch_size,
1168 verbose=verbose,
-> 1169 steps=steps)
1170
1171 def train_on_batch(self, x, y,
C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training_arrays.py in predict_loop(model, f, ins, batch_size, verbose, steps)
292 ins_batch[i] = ins_batch[i].toarray()
293
--> 294 batch_outs = f(ins_batch)
295 batch_outs = to_list(batch_outs)
296 if batch_index == 0:
C:\ProgramData\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py in __call__(self, inputs)
2713 return self._legacy_call(inputs)
2714
-> 2715 return self._call(inputs)
2716 else:
2717 if py_any(is_tensor(x) for x in inputs):
C:\ProgramData\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py in _call(self, inputs)
2669 feed_symbols,
2670 symbol_vals,
-> 2671 session)
2672 if self.run_metadata:
2673 fetched = self._callable_fn(*array_vals, run_metadata=self.run_metadata)
C:\ProgramData\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py in _make_callable(self, feed_arrays, feed_symbols, symbol_vals, session)
2621 callable_opts.run_options.CopyFrom(self.run_options)
2622 # Create callable.
-> 2623 callable_fn = session._make_callable_from_options(callable_opts)
2624 # Cache parameters corresponding to the generated callable, so that
2625 # we can detect future mismatches and refresh the callable.
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _make_callable_from_options(self, callable_options)
1469 """
1470 self._extend_graph()
-> 1471 return BaseSession._Callable(self, callable_options)
1472
1473
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in __init__(self, session, callable_options)
1423 with errors.raise_exception_on_not_ok_status() as status:
1424 self._handle = tf_session.TF_SessionMakeCallable(
-> 1425 session._session, options_ptr, status)
1426 finally:
1427 tf_session.TF_DeleteBuffer(options_ptr)
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
526 None, None,
527 compat.as_text(c_api.TF_Message(self.status.status)),
--> 528 c_api.TF_GetCode(self.status.status))
529 # Delete the underlying status object from memory otherwise it stays alive
530 # as there is a reference to status from this from the traceback due to
InvalidArgumentError: sequential_6_input:0 is both fed and fetched.