使用模型进行预测... WARNING:tensorflow:Model was constructed with shape (None, 3989, 10) for input KerasTensor(type_spec=TensorSpec(shape=(None, 3989, 10), dtype=tf.float32, name='dense_input'), name='dense_input', description="created by layer 'dense_input'"), but it was called on an input with incompatible shape (None, 10). 1/1 [==============================] - 0s 36ms/step --------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[20], line 14 11 predicted = model.predict(unknown, verbose=1) 13 # 将预测结果保存到新的 CSV 文件中 ---> 14 result = pd.DataFrame(predicted, columns=['prediction']) 15 result.to_csv('predicted_result.csv', index=False) 16 print("输入的数据为: ") File ~\AppData\Roaming\Python\Python39\site-packages\pandas\core\frame.py:757, in DataFrame.__init__(self, data, index, columns, dtype, copy) 746 mgr = dict_to_mgr( 747 # error: Item "ndarray" of "Union[ndarray, Series, Index]" has no 748 # attribute "name" (...) 754 copy=_copy, 755 ) 756 else: --> 757 mgr = ndarray_to_mgr( 758 data, 759 index, 760 columns, 761 dtype=dtype, 762 copy=copy, 763 typ=manager, 764 ) 766 # For data is list-like, or Iterable (will consume into list) 767 elif is_list_like(data): File ~\AppData\Roaming\Python\Python39\site-packages\pandas\core\internals\construction.py:337, in ndarray_to_mgr(values, index, columns, dtype, copy, typ) 332 # _prep_ndarraylike ensures that values.ndim == 2 at this point 333 index, columns = _get_axes( 334 values.shape[0], values.shape[1], index=index, columns=columns 335 ) --> 337 _check_values_indices_shape_match(values, index, columns) 339 if typ == "array": 340 if issubclass(values.dtype.type, str): File ~\AppData\Roaming\Python\Python39\site-packages\pandas\core\internals\construction.py:408, in _check_values_indices_shape_match(values, index, columns) 406 passed = values.shape 407 implied = (len(index), len(columns)) --> 408 raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (1, 3), indices imply (1, 1)该怎么修改代码

根据错误提示，可以看出预测数据的形状不匹配。模型的输入形状是 `(None, 3989, 10)`，而传入的数据的形状是 `(1, 10)`，因此需要对数据进行相应的处理。

假设你要预测的数据只有一组，可以通过以下代码进行处理：

import pandas as pd
import numpy as np
from tensorflow.keras.models import load_model

# 加载模型
print("使用模型进行预测...")
filepath = "./best_model2222.h5"
model = load_model(filepath)

# 读取 CSV 文件
data = pd.read_csv('shixiongshuju.csv')
# 将数据转换为 NumPy 数组
unknown = data.values.astype(np.float32)

# 将预测数据的形状转换为 (1, 3989, 10)
unknown = np.expand_dims(unknown[0], axis=0)

# 进行预测
predicted = model.predict(unknown, verbose=1)

# 将预测结果保存到新的 CSV 文件中
result = pd.DataFrame(predicted, columns=['prediction'])
result.to_csv('predicted_result.csv', index=False)
print("预测结果已保存到 predicted_result.csv 文件中。")

这段代码将会把预测结果保存在名为 `predicted_result.csv` 的文件中。请注意，代码中的列名 `prediction` 可以根据实际情况进行修改。