message: [Speech commands] Add `num_classes=label_count` when constructing the confusion matrix
  This seems like it should fix an issue where you get unlucky, the batch doesn't contain the largest label, and the returned matrix is smaller than other runs.
diff: @@ -156,7 +156,7 @@ def main(_):
  predicted_indices = tf.argmax(logits, 1)
  expected_indices = tf.argmax(ground_truth_input, 1)
  correct_prediction = tf.equal(predicted_indices, expected_indices)
  - confusion_matrix = tf.confusion_matrix(expected_indices, predicted_indices)
  + confusion_matrix = tf.confusion_matrix(expected_indices, predicted_indices, num_classes=label_count)
  evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  tf.summary.scalar('accuracy', evaluation_step)
  