如何在Keras中使用categorical_hinge? [英] How do I use categorical_hinge in Keras?

问题描述

也许是一个非常愚蠢的问题，但是我找不到在Keras中如何使用categorical_hinge的示例.我进行分类，我的目标是shape(,1)，其值为[-1,0,1]，所以我有3个类别.使用功能性API，我像这样设置了输出层:

Maybe a very dumb question but I can't find an example how to use categorical_hinge in Keras. I do classification and my target is shape(,1) with values [-1,0,1] so I have 3 categories. Using the functional API I have set up my output layer like this:

output = Dense(1，name ='output'，activation ='tanh'， kernel_initializer ='lecun_normal')(输出1)

output = Dense(1, name='output', activation='tanh', kernel_initializer='lecun_normal')(output1)

然后我申请:

model.compile(optimizer = adam，loss = {'output':'categorical_hinge'}， metrics = ['accuracy'])

model.compile(optimizer=adam, loss={'output': 'categorical_hinge'}, metrics=['accuracy'])

结果是模型正在收敛，但准确性接近0.我该怎么办?

The result is that the model is converging but accuracy goes towards to 0. What do I do wrong?

推荐答案

虽然[-1, 0, 1]是tanh激活函数的有效目标范围，但经验表明Keras模型不适用于二进制输出中的分类.考虑使用带有softmax分类器的三个单热点向量.如果我正确解释此错误报告，则分类铰链可以与热向量.

While [-1, 0, 1] is a valid target range for your tanh activation function, experience tells that Keras models don't work well with classification in a binary output. Consider using three one-hot vectors with a softmax classifier instead. If I interpret this bug report correctly, categorical hinge is built to work with one-hot vectors anyway.

因此:将标签转换为一键式，然后将输出更改为以下内容:

So: Convert your labels to one-hots and change your output to something along the lines of:

output = Dense(3, name='output', activation='softmax', kernel_initializer='lecun_normal')(output1)

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