我正在使用一个多输出张量流模型,仅用于学习目的,该模型应输出 1 个数值数据和 1 个分类数据(6 个类别)。
一切似乎都很好,定义良好,但是当我适合模型时,它会错误地说“ValueError:参数target
并且output
必须具有相同的排名(ndim)。收到:target.shape =(None,),output.shape =(None , 6)”
为简单起见,在输出层我有:
layers.Dense(1, activation='relu', name="output_numerical")(x)
layers.Dense(6, activation='softmax', name="output_categorical")(x)
模型编译为:
losses = {'output_numerical': 'mse', 'output_categorical': 'categorical_crossentropy'}
loss_weights = {'output_numerical': 1.0, 'output_categorical': 1.0}
model.compile(optimizer=optimizer, loss=losses, loss_weights=loss_weights)
最后是特征 x 和标签 y 的样本:
input_numerical | input_categorical
1.0 | 1
1.1 | 0
1.2 | 3
1.5 | 5
....
output_numerical| output_categorical
2.0 | 5
1.1 | 4
3.2 | 3
4.5 | 2
....
谁能帮我解决这个问题吗?
我预计 softmax 仅输出 1 个数字,即预期类别,但我怀疑它正在输出每个类别的概率。
1
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(None, 1)
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