I am using this code for a multilabel problem classification.

from __future__ import print_function

from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Embedding
from keras.layers import LSTM
from keras.models import Model
from keras.datasets import imdb

max_features = len(vocabDic)
maxlen = 500  # cut texts after this number of words (among top max_features most common words)
batch_size = 32

train_set = sequence.pad_sequences(train_set, maxlen=maxlen)
test_set = sequence.pad_sequences(test_set, maxlen=maxlen)

print('train_set shape:', train_set.shape)
print('test_set shape:', test_set.shape)

print('Build model...')
model = Sequential()
model.add(Embedding(max_features, 128))
model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(90, activation='sigmoid'))

# try using different optimizers and different optimizer configs

model.fit(train_set, train_labels,
          validation_data=(test_set, test_labels))
score, acc = model.evaluate(test_set, test_labels,
print('Test score:', score)
print('Test accuracy:', acc)

My problem is there are 90 classes and the accuracy is too high from the second epoch. I suspect keras is computing something incorrectly. Any clues?

Edit: I calculated the total recall of the model. It is barely 5%( 5 times better than random). Is it a normal behavior for such a problem?

  • $\begingroup$ It may happen and is not strange. depending on your data you may have even better results in first epoch. I've seen the similar thing in mnist $\endgroup$ – Media Dec 17 '17 at 21:21
  • $\begingroup$ I see you're using binary cross-entropy for your cost function. For multi-class classification you could look into categorical cross-entropy and categorical accuracy for your loss and metric, and troubleshoot with sklearn.metrics.classification_report on your test set $\endgroup$ – redhqs Dec 18 '17 at 11:07
  • $\begingroup$ Just to clarify: are you talking about multi-label (individual samples may belong to more than one classes) or multi-class (individual samples belong to one and only one class) classification here? $\endgroup$ – desertnaut Dec 19 '17 at 16:20

Why are you using binary_crossentropy? You should be using categorical_crossentropy. However, if you insist on using binary_crossentropy change your metric to metrics=['binary_accuracy', 'categorical_accuracy'] (this will display both accuracies).

  • 6
    $\begingroup$ Using binary_crossentropy is in fact correct. If the problem is a multi-label classification problem, it turns into K binary classification problems. Using softmax would be wrong, as doing that would result in raising the probability on one class lowering others. $\endgroup$ – Oliver Ni Nov 18 '18 at 2:11
  • 1
    $\begingroup$ This comment by @OliverNi is correct. $\endgroup$ – cgnorthcutt Jan 21 '19 at 6:22
  • $\begingroup$ The answer has 3 upvotes and at the same time, a comment which is pointing a mistake @Tophat should update the answer or clarify the comment. Especially this point "Why are you using binary_crossentropy? You should be using categorical_crossentropy" $\endgroup$ – 10xAI Jun 19 '20 at 7:17

I have answered a similar question here.

Your problem is that Accuracy is not the right metric for multi-label tasks. Try something different like AUC, precision, recall, accuracy@k, precision@recall. The choice of binary_crossentropy is correct since you are predicting each label independently.


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