Traceback (most recent call last): File "D:\tensorflow2-book\data\cat-dog\diaoqu.py", line 41, in <module> pre=model.predict(nim) ^^^^^^^^^^^^^^^^^^ File "C:\Users\17732\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler raise e.with_traceback(filtered_tb) from None File "C:\Users\17732\AppData\Local\Temp\__autograph_generated_filevg4phta4.py", line 15, in tf__predict_function retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope) ^^^^^ ValueError: in user code: File "C:\Users\17732\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\engine\training.py", line 2169, in predict_function * return step_function(self, iterator) File "C:\Users\17732\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\engine\training.py", line 2155, in step_function ** outputs = model.distribute_strategy.run(run_step, args=(data,)) File "C:\Users\17732\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\engine\training.py", line 2143, in run_step ** outputs = model.predict_step(data) File "C:\Users\17732\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\engine\training.py", line 2111, in predict_step return self(x, training=False) File "C:\Users\17732\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler raise e.with_traceback(filtered_tb) from None File "C:\Users\17732\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\engine\input_spec.py", line 298, in assert_input_compatibility raise ValueError( ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 128, 128, 3), found shape=(32, 128, 3)

根据你提供的错误信息，可以看出问题出现在输入数据的形状上。模型期望的输入形状是`(None, 128, 128, 3)`，但实际提供的输入形状是`(32, 128, 3)`。

这个错误通常是由于输入数据与模型定义不匹配导致的。请确保输入数据的形状与模型定义中的期望形状一致。

如果你的输入数据是图像数据，可以使用 `numpy` 或者 `cv2` 库来加载图像，并确保对图像进行预处理以匹配模型的输入形状。

以下是一个示例，演示如何使用 `cv2` 加载图像，并将其调整为模型期望的形状：

import cv2
import numpy as np

# 加载图像并调整形状
image = cv2.imread('your_image_path.jpg')
image = cv2.resize(image, (128, 128))
image = np.expand_dims(image, axis=0)

# 进行预测
output = model.predict(image)

请将 `'your_image_path.jpg'` 替换为你的图像路径，并根据需要进行其他预处理操作。

希望这能帮助你解决问题！如果还有其他疑问，请随时问我。