Tensorflow 如何打印张量流中的预测概率'；什么是再培训的例子？

我在自己的数据集上使用了Tensor Flow的示例。最终测试评估输出最终测试精度和误分类图像的名称：

test_accuracy, predictions = eval_session.run(
  [evaluation_step, prediction],
  feed_dict={
      bottleneck_input: test_bottlenecks,
      ground_truth_input: test_ground_truth
  })
  tf.logging.info('Final test accuracy = %.1f%% (N=%d)' %
              (test_accuracy * 100, len(test_bottlenecks)))

  if FLAGS.print_misclassified_test_images:
    tf.logging.info('=== MISCLASSIFIED TEST IMAGES ===')
    for i, test_filename in enumerate(test_filenames):
      if predictions[i] != test_ground_truth[i]:
        tf.logging.info('%70s  %s' % (test_filename, list(image_lists.keys())[predictions[i]]))

如何打印与所有类的预测相关联的概率

例如：

图1-A:0.5；B:0.3；C:0.1；D:0.1

---

图2-A:0.3；B:0.2；C:0:4；我想我自己找到了答案

概率可以这样获得：

probs = tf.nn.softmax(final_tensor)
probabilities = sess.run(probs, feed_dict={bottleneck_input: test_bottlenecks,
    ground_truth_input: test_ground_truth})

for i, test_filename in enumerate(test_filenames):        
    tf.logging.info('%70s  %f %f %f %f' %
                      (test_filename,
                       probabilities[i][0], probabilities[i][1], probabilities[i][2], probabilities[i][3]))

然后，可以像这样访问它们：

probs = tf.nn.softmax(final_tensor)
probabilities = sess.run(probs, feed_dict={bottleneck_input: test_bottlenecks,
    ground_truth_input: test_ground_truth})

for i, test_filename in enumerate(test_filenames):        
    tf.logging.info('%70s  %f %f %f %f' %
                      (test_filename,
                       probabilities[i][0], probabilities[i][1], probabilities[i][2], probabilities[i][3]))