retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)

这是一个使用 TensorFlow 的 AutoGraph 转换的代码片段。`converted_call` 函数用于将 `step_function` 转换为 TensorFlow 图形计算图的形式。`ld` 函数用于加载变量，并将其传递给 `converted_call` 函数进行计算。`retval_` 可能是 `step_function` 的返回值，但需要查看完整的代码才能确定。

相关问题

ValueError Traceback (most recent call last) Cell In[17], line 5 3 model.load_weights("unet_membrane.hdf5") 4 #results = model.predict_generator(testGene,67,verbose=1) ----> 5 results = model.predict(testGene,verbose=1) 6 saveResult("data/results",results) File d:\Software\Anaconda\Ana_qinghua\lib\site-packages\keras\utils\traceback_utils.py:70, in filter_traceback..error_handler(*args, **kwargs) 67 filtered_tb = _process_traceback_frames(e.__traceback__) 68 # To get the full stack trace, call: 69 # `tf.debugging.disable_traceback_filtering()` ---> 70 raise e.with_traceback(filtered_tb) from None 71 finally: 72 del filtered_tb File ~\AppData\Local\Temp\__autograph_generated_file8h3jf8qv.py:15, in outer_factory..inner_factory..tf__predict_function(iterator) 13 try: 14 do_return = True ---> 15 retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope) 16 except: 17 do_return = False ValueError: in user code: ... Call arguments received by layer 'model_7' (type Functional): • inputs=tf.Tensor(shape=(None, None, None), dtype=float32) • training=False • mask=None Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...

这个错误信息是在使用 Keras 模型进行预测时出现的。它表明预测过程中出现了错误，具体原因是模型的某个名为 "model_7" 的 Functional 层接收到了不符合要求的调用参数。该层要求接收一个形状为 (None, None, None) 的 float32 类型的输入张量，但实际上传入的输入数据与其形状不匹配，导致了该错误的发生。可能的原因是输入数据的形状与模型的输入形状不匹配或者数据类型不正确。需要检查输入数据的形状和数据类型是否正确，并且与模型的输入层相匹配。

Traceback (most recent call last): File "D:\ANACONDA3\lib\site-packages\IPython\core\interactiveshell.py", line 3505, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-20-10043336366a>", line 52, in <module> model.fit(train_data, train_labels, epochs=10, batch_size=32) File "D:\ANACONDA3\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler raise e.with_traceback(filtered_tb) from None File "C:\Users\CXY\AppData\Local\Temp\__autograph_generated_filej56unrey.py", line 15, in tf__train_function retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope) ValueError: in user code: File "D:\ANACONDA3\lib\site-packages\keras\engine\training.py", line 1160, in train_function * return step_function(self, iterator) File "D:\ANACONDA3\lib\site-packages\keras\engine\training.py", line 1146, in step_function ** outputs = model.distribute_strategy.run(run_step, args=(data,)) File "D:\ANACONDA3\lib\site-packages\keras\engine\training.py", line 1135, in run_step ** outputs = model.train_step(data) File "D:\ANACONDA3\lib\site-packages\keras\engine\training.py", line 993, in train_step y_pred = self(x, training=True) File "D:\ANACONDA3\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler raise e.with_traceback(filtered_tb) from None File "D:\ANACONDA3\lib\site-packages\keras\engine\input_spec.py", line 295, in assert_input_compatibility raise ValueError( ValueError: Input 0 of layer "sequential_3" is incompatible with the layer: expected shape=(None, 32, 32, 3), found shape=(None, 80, 160, 3)

这个错误是由于在模型的输入层中指定的形状与实际提供的数据形状不匹配导致的模型的输入层期望是一个形状为(None, 32, 32, 3)的张量，但实际提供的数据形状是(None, 80, 160, 3)。

你可以尝试调整输入数据的形状，使其与模型的期望输入形状匹配。或者，你可以修改模型的输入层，使其适应提供的数据形状。这取决于你的具体需求和数据。

如果你需要进一步的帮助，请提供更多关于你的模型和数据的信息，我将尽力提供更准确的解决方案。