Python Deep Learning Projects

Evaluation of the LSTM model

Now, let's again compute the metrics and compare the performance:

logits = sess.run(model.logits, feed_dict={model.X:str_idx(test_X,dictionary,maxlen)})print(metrics.classification_report(test_Y, np.argmax(logits,1), target_names = trainset.target_names))

The computed outputs are shown as follows:

So, we can clearly see the boost in the performance of the model! Now, with the LSTM, the f1-score is bumped to 72% whereas, in our previous basic RNN model, it was 66%, which is quite a good improvement of 7%.