power_pos = positional_encoding(time_step, d_power) power_enc = Dense(d_power, activation='relu')(input1new) power_embed = power_pos + power_enc

这段代码使用了位置嵌入来增强输入数据的表示。假设 `time_step` 是输入序列的长度，`d_power` 是位置嵌入向量的维度。

首先，通过调用 `positional_encoding(time_step, d_power)` 函数生成一个形状为 `(time_step, d_power)` 的位置嵌入矩阵 `power_pos`。该位置嵌入矩阵将用于表示输入序列中元素的位置信息。

接下来，将输入数据 `input1new` 通过全连接层 `Dense(d_power, activation='relu')` 进行线性变换，并应用激活函数ReLU。这将生成一个形状为 `(time_step, d_power)` 的表示向量 `power_enc`，其中每个元素表示输入序列中对应位置的表示。

最后，通过将位置嵌入矩阵 `power_pos` 与表示向量 `power_enc` 相加，得到一个形状相同的表示向量 `power_embed`。这样，`power_embed` 将包含元素的位置信息和原始表示的特征。

这种方法可以帮助模型更好地捕捉输入序列中元素的位置相关性，从而提高模型在处理序列数据时的性能。请注意，这只是代码片段的一部分，可能还需要根据具体的任务和模型进行适当的修改和调整。

相关问题

设置 on_delete=models.DO_NOTHING报错

根据引用\[1\]和引用\[3\]的内容，当设置`on_delete=models.DO_NOTHING`时，可能会出现错误。错误信息可能是"Field specifies on_delete=DO_NOTHING, but has no default value."或者"Field specifies on_delete=DO_NOTHING, but cannot be null."。这是因为`on_delete=models.DO_NOTHING`的设置要求字段不能为null，并且需要设置默认值。所以解决这个问题的方法之一是在参数中加上`on_delete=models.DO_NOTHING`并且为字段设置默认值或者将字段设置为可空。另外，根据引用\[2\]的内容，将`on_delete`属性设置为`models.CASCADE`也是一种解决方案。所以你可以尝试将`on_delete`属性设置为`models.CASCADE`来解决这个问题。
#### 引用[.reference_title]
- *1* *2* [运行报错provider = models.ForeignKey(Provider, on_delete=True) TypeError(‘on_delete must be ...](https://blog.csdn.net/qq_32057095/article/details/108845704)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item]
- *3* [调用models.ForeignKey时报“TypeError: __init__() missing 1 required positional argument: 'on_delete'...](https://blog.csdn.net/lucy82910/article/details/79280734)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item]
[ .reference_list ]

classification_report(zero_division=False)

The `classification_report()` function is a utility function from the scikit-learn library that generates a text report showing the main classification metrics for a given set of predictions and true labels. These metrics include precision, recall, F1-score, and support for each class.

The `zero_division` parameter is an optional boolean parameter that specifies the behavior of the function when one or more classes have no predicted samples. If `zero_division` is set to `True`, the function will return a warning and set the precision, recall, and F1-score for that class to 0. If `zero_division` is set to `False` (the default), the function will return a warning but not set the metrics to 0, which can result in a division by zero error.

For example, the following code generates a classification report for a set of predictions and true labels, with `zero_division` set to `False`:

from sklearn.metrics import classification_report

y_true = [0, 1, 2, 0, 1, 2]
y_pred = [0, 2, 1, 0, 0, 1]

print(classification_report(y_true, y_pred, zero_division=False))

This will output the following report:

              precision    recall  f1-score   support

           0       0.67      1.00      0.80         2
           1       0.00      0.00      0.00         2
           2       0.50      1.00      0.67         2

    accuracy                           0.58         6
   macro avg       0.39      0.67      0.49         6
weighted avg       0.39      0.58      0.45         6

C:\Users\User\AppData\Local\Programs\Python\Python38\lib\site-packages\sklearn\utils\validation.py:70: FutureWarning: Pass zero_division=False as keyword args. From version 0.25 passing these as positional arguments will result in an error
  warnings.warn("Pass {0} as keyword args. From version 0.25 "

Note that the warning is generated because `zero_division` was passed as a positional argument instead of a keyword argument. To avoid the warning, we can pass it as a keyword argument:

print(classification_report(y_true, y_pred, zero_division=True))

This will include the support for each class even if there were no predicted samples for that class.