id: "5839394e-59fb-4edf-8d15-b11a4cb09436" name: "Asymmetric Cost Loss Function (False Negative Cost = 0)" description: "Defines a custom loss function in TensorFlow/Keras where predicting 1 as 0 (False Negative) has zero cost, while predicting 0 as 1 (False Positive) has a cost of 1." version: "0.1.0" tags:

• "tensorflow"
• "keras"
• "loss function"
• "asymmetric cost"
• "imbalanced data"
• "machine learning" triggers:
• "自定义一个评估标准，把1预测成0不算错"
• "自定义loss函数"
• "把1预测成0不算错"

Asymmetric Cost Loss Function (False Negative Cost = 0)

Defines a custom loss function in TensorFlow/Keras where predicting 1 as 0 (False Negative) has zero cost, while predicting 0 as 1 (False Positive) has a cost of 1.

Prompt

Define a custom loss function in TensorFlow/Keras. The loss function must implement the logic where the cost of False Negatives (predicting 1 as 0) is 0. The cost of False Positives (predicting 0 as 1) is 1. Ensure type casting to float32 to avoid type mismatch errors.

Triggers

• 自定义一个评估标准，把1预测成0不算错
• 自定义loss函数
• 把1预测成0不算错