return step_function(self, iterator)
时间: 2023-11-02 08:06:22 浏览: 22
这是一个 Python 语言中的函数调用语句,其中包含以下部分:
- `step_function`:是一个函数的名称或函数对象,表示要调用的函数。
- `self`:表示对象本身,用于调用函数的对象。
- `iterator`:是传递给函数的参数,可以是一个或多个参数。在这个例子中,`iterator` 是作为唯一的参数传递给 `step_function` 函数。
该语句的作用是调用 `step_function` 函数,并将 `iterator` 参数传递给该函数进行处理。在函数执行完毕后,该语句将返回函数的返回值。
相关问题
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。
ValueError Traceback (most recent call last) Cell In[29], line 91 88 model.summary() 89 #模型训练 ---> 91 history = model.fit( 92 normed_train_data, train_labels, 93 epochs=100, validation_split=0.2, verbose=0) #verbose=表示不输出训练记录 94 #输出训练的各项指标值 95 hist = pd.DataFrame(history.history) File ~\anaconda3\lib\site-packages\keras\utils\traceback_utils.py:70, in filter_traceback.<locals>.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_file1dq9vkey.py:15, in outer_factory.<locals>.inner_factory.<locals>.tf__train_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: File "C:\Users\lenovo\anaconda3\lib\site-packages\keras\engine\training.py", line 1284, in train_function * return step_function(self, iterator) File "C:\Users\lenovo\anaconda3\lib\site-packages\keras\engine\training.py", line 1268, in step_function ** outputs = model.distribute_strategy.run(run_step, args=(data,)) File "C:\Users\lenovo\anaconda3\lib\site-packages\keras\engine\training.py", line 1249, in run_step ** outputs = model.train_step(data) File "C:\Users\lenovo\anaconda3\lib\site-packages\keras\engine\training.py", line 1050, in train_step y_pred = self(x, training=True) File "C:\Users\lenovo\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\lenovo\anaconda3\lib\site-packages\keras\engine\input_spec.py", line 298, in assert_input_compatibility raise ValueError( ValueError: Input 0 of layer "sequential_21" is incompatible with the layer: expected shape=(None, 14), found shape=(32, 15)
这个错误的原因是您的模型期望的输入形状与实际输入形状不匹配。具体来说,您的模型期望的输入形状是(None, 14),但您提供给模型的输入形状是(32, 15)。这意味着您的输入数据中有32个样本,每个样本有15个特征,而您的模型期望每个样本只有14个特征。
为了解决这个问题,您需要检查您的输入数据,并确保它们的形状与模型期望的形状匹配。您可以使用NumPy的reshape()函数来改变输入数据的形状,以确保其与模型期望的形状匹配。例如,如果您的输入数据是一个形状为(32, 15)的NumPy数组,您可以使用reshape()函数将其转换为形状为(32, 14)的数组,如下所示:
```
train_data = train_data.reshape((32, 14))
```
这样,您就可以将新的train_data输入到您的模型中了,而不会遇到形状不匹配的错误。