CODEWITHSUNDEEP
pythontensorflow
sdgaw erzswer
sdgaw erzswer

Reputation: 2382

Tensorflow multi class classification loss

I've been recently trying to implement a multi-class classification LSTM architecture, based on this example: biLSTM example

After I changed 

self.label = tf.placeholder(tf.int32, [None])

to 

self.label = tf.placeholder(tf.int32, [None,self.n_class)

The model seems to train normally, yet I am having trouble with this step:

    self.loss = tf.reduce_mean(
        tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y_hat, labels=self.label))

    # prediction
    self.prediction = tf.argmax(tf.nn.softmax(y_hat), 1)

As, even though the model learns normally, the predictions does not seem to work for multiple variables. I was wondering how should one code the self.prediction object, so that it emits a vector of predictions for individual instances?

Thank you very much.

Upvotes: 0

Views: 198

Answers (1)

Tim
Tim

Reputation: 10709

I was wondering how should one code the self.prediction object, so that it emits a vector of predictions for individual instances?

In general tf.nn.softmax returns a vector of probabilities. You just can't see them because your are using tf.argmax, which returns the index of the largest value. Therefore you will just get one number. Just remove tf.argmax and you should be fine.

Upvotes: 1

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