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Code Duplication    Length = 15-15 lines in 2 locations

pyActLearn/learning/nn/lstm.py 2 locations

@@ 625-639 (lines=15) @@
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                feed_dict={self.x: batch_x, self.y_: batch_y, self.length: batch_size,
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                           self.init_state: np.zeros(2 * self.num_units)})
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            # Summary
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            if summaries_dir is not None and (i % summary_interval == 0):
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                summary, loss, accuracy = session.run(
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                    [self.merged, self.loss, self.accuracy],
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                    feed_dict={self.x: x, self.y_: y, self.length: num_samples - valid_set_len - num_skip,
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                               self.init_state: np.zeros(2 * self.num_units)}
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                )
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                train_writer.add_summary(summary, i)
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                logger.info('Step %d, train_set accuracy %g, loss %g' % (i, accuracy, loss))
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                if (test_x is not None) and (test_y is not None):
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                    merged, accuracy = session.run(
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                        [self.merged, self.accuracy],
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                        feed_dict={self.x: test_x, self.y_: test_y, self.length: test_x.shape[0] - num_skip,
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                                   self.init_state: np.zeros(2*self.num_units)})
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                    test_writer.add_summary(merged, i)
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                    logger.info('test_set accuracy %g' % accuracy)
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            # Get Summary
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            if i == x.shape[0] - num_skip:
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                i = 0
@@ 127-141 (lines=15) @@
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        i = 0
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        while _criterion.continue_learning():
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            batch_x, batch_y = injector.next_batch()
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            if summaries_dir is not None and (i % summary_interval == 0):
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                summary, loss, accuracy = session.run(
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                    [self.merged, self.loss, self.accuracy],
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                    feed_dict={self.x: train_seq_x, self.y_: train_seq_y,
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                               self.init_state: np.zeros((train_seq_x.shape[0], 2 * self.num_units))}
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                )
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                train_writer.add_summary(summary, i)
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                logger.info('Step %d, train_set accuracy %g, loss %g' % (i, accuracy, loss))
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                if (test_x is not None) and (test_y is not None):
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                    merged, accuracy = session.run(
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                        [self.merged, self.accuracy],
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                        feed_dict={self.x: test_seq_x, self.y_: test_seq_y,
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                                   self.init_state: np.zeros((test_seq_x.shape[0], 2*self.num_units))})
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                    test_writer.add_summary(merged, i)
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                    logger.info('test_set accuracy %g' % accuracy)
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            loss, accuracy, _ = session.run(
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                [self.loss, self.accuracy, self.fit_step],
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                feed_dict={self.x: batch_x, self.y_: batch_y,