title: Tensorflow error: Minimum tensor rank: 1 but got: 1
body: <p>I'm getting the following error trying to eval my model.</p>
  
  <p><code>
  tensorflow.python.framework.errors.InvalidArgumentError: Minimum tensor rank: 1 but got: 1 [[Node: ArgMax_1 = ArgMax[T=DT_INT64, _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_Placeholder_1_0, ArgMax_1/dimension/_40)]]
  </code></p>
  
  <p>Here is the relevant code</p>
  
  <pre><code>        # Predictions for the current training minibatch.
          train_prediction = tf.nn.softmax(logits)
          correct_prediction = tf.equal(tf.argmax(train_prediction, 1), tf.argmax(train_labels, 1))
          accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  
          sess.run(tf.initialize_all_variables())
          for i in range(1000000):
              start_time = time()
  
              images, labels = get_batch(fifo_queue, FLAGS.batch_size)
  
              feed_dict = {
                  train_images: images,
                  train_labels: labels
              }
  
              _, loss_value, learn_rate, predictions = sess.run(
                  [train_step, cross_entropy, learning_rate, train_prediction],
                  feed_dict=feed_dict)
  
              duration = time() - start_time
                if i % 1 == 0:
                  # Print status to stdout.
                   print('Step %d: loss = %.3f (%.3f sec)' % (i, loss_value, duration))
  
                   train_accuracy = accuracy.eval(feed_dict={
                       train_images: images, train_labels: labels, keep_prob: 1.0})
                   print("step %d, training accuracy %g"%(i, train_accuracy))
                   train_step.run(feed_dict={train_images: images[0], train_labels: labels[1], keep_prob: 0.5})
  </code></pre>
  
  <p>`</p>
  
  <p>I haven't been able to try much yet because I'm just getting my first model eval-ing and this error (indicating expecting 1 and got 1) is not overly helpful.  </p>