我在使用Keras训练深度神经网络。我使用train_on_batch函数来训练我的模型。我的模型有两个输出。我的目的是通过为每个样本指定一个特定的值来修改每个样本的损失。根据Keras的文档这里
我需要为sample_weight参数分配两个不同的权重。以下是我的代码示例，其中每个批次包含四个训练样本：
wights=[12,10,31,1]; mod_loss = mymodel.train_on_batch([X_train], [Y1, Y2],sample_weight=[wights,[1.0,1.0,1.0,1.0]])我使用sample_weight来只对第一个输出进行加权，而不对第二个输出进行加权。当我运行代码时，我得到了以下错误：
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/engine/training.py", line 1211, in train_on_batch class_weight=class_weight) File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/engine/training.py", line 801, in _standardize_user_data feed_sample_weight_modes) File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/engine/training.py", line 799, in <listcomp> for (ref, sw, cw, mode) in File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/engine/training_utils.py", line 470, in standardize_weights if sample_weight is not None and len(sample_weight.shape) != 1:AttributeError: 'list' object has no attribute 'shape'这个错误让我想到，如果我将分配给sample_weight的值改为numpy数组，问题可能会解决。所以我将代码改成了这样：
wights=[12,10,31,1]; mod_loss = mymodel.train_on_batch([X_train], [Y1, Y2],sample_weight=numpy.array([wights,[1.0,1.0,1.0,1.0]]))然后我得到了这个错误：
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/engine/training.py", line 1211, in train_on_batch class_weight=class_weight) File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/engine/training.py", line 794, in _standardize_user_data sample_weight, feed_output_names) File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/engine/training_utils.py", line 200, in standardize_sample_weights 'sample_weight') File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/engine/training_utils.py", line 188, in standardize_sample_or_class_weights str(x_weight))TypeError: The model has multiple outputs, so `sample_weight` should be either a list or a dict. Provided `sample_weight` type not understood: [[12.0 10.0 31.0 1.0] [ 1. 1. 1. 1. ]]我有点困惑，我不确定这是否是Keras实现中的一个错误。我几乎在网上找不到任何与此相关的工作或问题。有什么想法吗？
回答：
我通过另一种方式解决了这个问题。如果输出是Y1和Y2，它们的层名称分别为y1_layername和y2_layername，假设你只想对y2应用一个权重向量（例如，y2是一个长度为4的向量），你可以这样写你的代码：
wights=[12,10,31,1]; mod_loss = mymodel.train_on_batch([X_train], [Y1, Y2],sample_weight={"y2_layername":wights})我测试过，这个方法工作得很好
