add a script to compute the perplexity of test data #56
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Adding eval.py and updates to util.py and models.py to allow for calculating the perplexity of test files. I also modified the vocabulary to have start, end and unknown character tokens.
| count_pairs = sorted(counter.items(), key=lambda x: -x[1]) | ||
| self.chars, _ = zip(*count_pairs) | ||
| self.vocab_size = len(self.chars) | ||
| self.vocab = dict(zip(self.chars, range(len(self.chars)))) | ||
| with open(vocab_file, 'wb') as f: | ||
| cPickle.dump(self.chars, f) | ||
| self.tensor = np.array(list(map(self.vocab.get, data))) | ||
| self.tensor = np.array(list(map(self.vocab.get, ['<S>'] + list(data) + ['</S>']))) |
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Do you think it would be a better idea to write this after line 59, self.tensor = self.tensor[:self.num_batches * self.batch_size * self.seq_length], since it's unlikely that you will get the </S> character
| @@ -58,6 +58,29 @@ def loop(prev, _): | |||
| optimizer = tf.train.AdamOptimizer(self.lr) | |||
| self.train_op = optimizer.apply_gradients(zip(grads, tvars)) | |||
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| def eval(self, sess, chars, vocab, text): | |||
| batch_size = 200 | |||
| @@ -58,6 +58,29 @@ def loop(prev, _): | |||
| optimizer = tf.train.AdamOptimizer(self.lr) | |||
| self.train_op = optimizer.apply_gradients(zip(grads, tvars)) | |||
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| def eval(self, sess, chars, vocab, text): | |||
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It's probably better to move this to eval.py
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@ajaech This PR has merge conflicts. |
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The eval.py script can be used to compute perplexity of test data.