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I have data with integer target class in the range 1-5 where one is the lowest and five the highest. In this case, should I consider it as regression problem and have one node in the output layer?

My way of handling it is:

1- first I convert the labels to binary class matrix

labels = to_categorical(np.asarray(labels))

2- in the output layer, I have five nodes 

main_output = Dense(5, activation='sigmoid', name='main_output')(x)

3- I use 'categorical_crossentropy with mean_squared_error when compiling

model.compile(optimizer='rmsprop',loss='categorical_crossentropy',metrics=['mean_squared_error'],loss_weights=[0.2])

Also, can anyone tells me: what is the difference between using categorical_accuracy and 'mean_squared_error in this case?

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Regression and classification are vastly different things. If you reimagine this as a regression task than the difference of predicting 2 when the ground truth is 4 will be rated more than if you predict 3 instead of 4. If you have class like car, animal, person you do not care for the ranking between those classes. Predicting car is just as wrong as animal, iff the image shows a person.

Metrics do not impact your learning at all. It is just something that is computed additionally to the loss to show the performance of the model. Here the accuracy makes sense, because this is mostly the metric that we care about. Mean squared error does not tell you how well your model performs. If you get something like 0.0015 mean squared error it sounds good, but it is hard to visualize just how well this performs. In contrast using accuracy and achieving 95% accuracy for example is meaningful.

One last thing you should use softmax instead of sigmoid as your final output to get a probability distribution in your final layer. Softmax will output percentages for every class that sum up to 1. Then crossentropy calculates the difference of the probability distribution of your network output and the ground truth.

于 2017-03-07T08:11:04.507 回答