pdfcoffee

Recurrent Neural Networks
We can also use model.predict() to retrieve our predictions and compare them
individually to the labels and use external tools (from scikit-learn, for example) to
compute our results:
labels, predictions = [], []
idx2word[0] = "PAD"
is_first_batch = True
for test_batch in test_dataset:
inputs_b, labels_b = test_batch
pred_batch = best_model.predict(inputs_b)
predictions.extend([(1 if p > 0.5 else 0) for p in pred_batch])
labels.extend([l for l in labels_b])
if is_first_batch:
# print first batch of label, prediction, and sentence
for rid in range(inputs_b.shape[0]):
words = [idx2word[idx] for idx in inputs_b[rid].numpy()]
words = [w for w in words if w != "PAD"]
sentence = " ".join(words)
print("{:d}\t{:d}\t{:s}".format(
labels[rid], predictions[rid], sentence))
is_first_batch = False
print("accuracy score: {:.3f}".format(accuracy_score(labels,
predictions)))
print("confusion matrix")
print(confusion_matrix(labels, predictions)
For the first batch of 64 sentences in our test dataset, we reconstruct the sentence
and display the label (first column) as well as the prediction from the model
(second column). Here we show the top 10 sentences. As you can see, the model
gets it right for most sentences in this list:
LBL PRED SENT
1 1 one of my favorite purchases ever
1 1 works great
1 1 our waiter was very attentive friendly and informative
0 0 defective crap
0 1 and it was way to expensive
0 0 don't waste your money
0 0 friend's pasta also bad he barely touched it
1 1 it's a sad movie but very good
0 0 we recently witnessed her poor quality of management towards
other guests as well
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