Following this tutorial, I have a doubt about the evaluation part in:# test the modeln_batches = int(mnist.test.num_examples/batch_size)total_correct_preds = 0for i in range(n_batches):    X_batch, Y_batch = mnist.test.next_batch(batch_size)    _, loss_batch, logits_batch = sess.run([optimizer, loss, logits], feed_dict={X: X_batch, Y:Y_batch})     preds = tf.nn.softmax(logits_batch)    correct_preds = tf.equal(tf.argmax(preds, 1), tf.argmax(Y_batch, 1))    accuracy = tf.reduce_sum(tf.cast(correct_preds, tf.float32)) # need numpy.count_nonzero(boolarr) :(    total_correct_preds += sess.run(accuracy)   print 'Accuracy {0}'.format(total_correct_preds/mnist.test.num_examples)Note that this is done on the test set, so the goal is purely to obtain the accuracy using a previously trained model. However isn't calling the line:_, loss_batch, logits_batch = sess.run([optimizer, loss, logits], feed_dict={X: X_batch, Y:Y_batch}) equivalent to re-optimizing the model using the test data (and labels)?Shouldn't we avoid re-running optimizer and loss and just compute the predictions?
Evaluating Logistic Regression Model in Tensorflow
machine learning;tensorflow;logistic regression;training;evaluation
I think you are correct. The line should beloss_batch, logits_batch = sess.run([loss, logits], feed_dict={X: X_batch, Y:Y_batch}) 