Keras multi-label time-series classification considering time-series as an input image vector
model.predict(X)


Will return an array of probabilities across your classes, the effective discrete prediction can be achieved by:

np.argmax(model.predict(X))


Accuracy is only one measure of the fit of a classifier, and can be strongly affected by properties of the evaluation set such as class imbalance. Does one class represent a large proportion of your observations? If so, this accuracy measure could be misleading you as to its performance.

Otherwise, with the predictions gathered as shown above, you can further analyze the performance of your network using confusion matrices, muticlass log loss (cross-entropy) and/or others.