import tensorflow as tf

      #print(predictions1)
      predictions2 = tf.argmax(input=predictions1, axis=1)
      predictions = sess.run(predictions2)
      top1 += batch_size - (np.count_nonzero(predictions - np_labels))
      #print(top1/num_processed_images)
      #print(num_processed_images) 
      #print(predictions)
      #accuracy1 = tf.reduce_sum(
      #                         tf.nn.in_top_k(tf.cast(tf.Variable(predictions2), tf.float32),
      #                         tf.cast((tf.constant(np_labels), 1), tf.float32)))
      accuracy1 = tf.reduce_sum(
          input_tensor=tf.cast(tf.nn.in_top_k(predictions=tf.constant(predictions1),
              targets=tf.constant(np_labels), k=1), tf.float32))

      accuracy5 = tf.reduce_sum(
          input_tensor=tf.cast(tf.nn.in_top_k(predictions=tf.constant(predictions1),
              targets=tf.constant(np_labels), k=5), tf.float32))
      np_accuracy1, np_accuracy5 =  sess.run([accuracy1, accuracy5])

      ##print(labels)
      total_accuracy1 += np_accuracy1
      total_accuracy5 += np_accuracy5
      print("Processed %d images. (Top1 accuracy, Top5 accuracy) = (%0.4f, %0.4f)" \
          % (num_processed_images, total_accuracy1/num_processed_images,
