如何在Tensorflow上的Cifar-10教程中测试自己的图像？

我训练了Tensorflow Cifar10模型，我想用自己的单张图像（32 * 32，jpg / png）喂它 .

我希望看到每个标签的标签和概率作为输出，但我对此有些麻烦..

搜索堆栈溢出后，我发现了一些this的帖子，我修改了cifar10_eval.py .

但它根本不起作用 .

错误信息是：

InvalidArgumentErrorTraceback（最近一次调用最后一次）in（）----> 1 evaluate（）in evaluate（）86＃从检查点87恢复打印（“ckpt.model_checkpoint_path”，ckpt.model_checkpoint_path）---> 88 saver.restore （sess，ckpt.model_checkpoint_path）89＃假设model_checkpoint_path看起来像：90＃/ my-favorite-path/cifar10_train/model.ckpt-0,/home/huray/anaconda2/envs/tensorflow/lib/python2.7/site -packages / tensorflow / python / training / saver.pyc in restore（self，sess，save_path）1127引发ValueError（“使用无效的保存路径％s调用恢复”％save_path）1128 sess.run（self.saver_def.restore_op_name， - > 1129 {self.saver_def.filename_tensor_name：save_path}）1130 1131 @staticmethod /home/huray/anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in run（self ，fetches，feed_dict，options，run_metadata）380 try：381 result = self._run（None，fetches，feed_dict，options_ptr， - > 382 run_metadata_ptr）383 if run_metadata：384 proto_data = tf_se _run中的ssion.TF_GetBuffer（run_metadata_ptr）/home/huray/anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/client/session.pyc（self，handle，fetches，feed_dict，options，run_metadata ）653 movers = self._update_with_movers（feed_dict_string，feed_map）654 results = self._do_run（handle，target_list，unique_fetches， - > 655 feed_dict_string，options，run_metadata）656 657＃用户可能多次获取相同的张量，但我们/home/huray/anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in_do_run（self，handle，target_list，fetch_list，feed_dict，options，run_metadata）721 if handle是无：722返回self.do_call（ run_fn，self__session，feed_dict，fetch_list， - > 723 target_list，options，run_metadata）724 else：725 return self._do_call（_prun_fn，self._session，handle，feed_dict，/ home /huray/anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _do_call（self，fn，* args）741 exce pt KeyError：742 pass - > 743 raise type（e）（node_def，op，message）744 745 def _extend_graph（self）：InvalidArgumentError：Assign需要两个张量的形状匹配 . lhs shape = [18,384] rhs shape = [2304,384] [[节点：save / Assign_5 =赋值[T = DT_FLOAT，_class = [“loc：@ local3 / weights”]，use_locking = true，validate_shape = true，_device =“/ job：localhost / replica：0 / task：0 / cpu：0”]（local3 / weights，save / restore_slice_5）]]

任何帮助Cifar10的帮助将不胜感激 .

到目前为止，这是编译问题的实现代码：

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from datetime import datetime
import math
import time

import numpy as np
import tensorflow as tf
import cifar10

FLAGS = tf.app.flags.FLAGS

tf.app.flags.DEFINE_string('eval_dir', '/tmp/cifar10_eval',
                           """Directory where to write event logs.""")
tf.app.flags.DEFINE_string('eval_data', 'test',
                           """Either 'test' or 'train_eval'.""")
tf.app.flags.DEFINE_string('checkpoint_dir', '/tmp/cifar10_train',
                           """Directory where to read model checkpoints.""")
tf.app.flags.DEFINE_integer('eval_interval_secs', 5,
                            """How often to run the eval.""")
tf.app.flags.DEFINE_integer('num_examples', 1,
                            """Number of examples to run.""")
tf.app.flags.DEFINE_boolean('run_once', False,
                         """Whether to run eval only once.""")

def eval_once(saver, summary_writer, top_k_op, summary_op):
  """Run Eval once.

  Args:
    saver: Saver.
    summary_writer: Summary writer.
    top_k_op: Top K op.
    summary_op: Summary op.
  """
  with tf.Session() as sess:
    ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
    if ckpt and ckpt.model_checkpoint_path:
      # Restores from checkpoint
      saver.restore(sess, ckpt.model_checkpoint_path)
      # Assuming model_checkpoint_path looks something like:
      #   /my-favorite-path/cifar10_train/model.ckpt-0,
      # extract global_step from it.
      global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
    else:
      print('No checkpoint file found')
      return
    print("Check point : %s" % ckpt.model_checkpoint_path)

    # Start the queue runners.
    coord = tf.train.Coordinator()
    try:
      threads = []
      for qr in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS):
        threads.extend(qr.create_threads(sess, coord=coord, daemon=True,
                                         start=True))

      num_iter = int(math.ceil(FLAGS.num_examples / FLAGS.batch_size))
      true_count = 0  # Counts the number of correct predictions.
      total_sample_count = num_iter * FLAGS.batch_size
      step = 0
      while step < num_iter and not coord.should_stop():
        predictions = sess.run([top_k_op])
        true_count += np.sum(predictions)
        step += 1

      # Compute precision @ 1.
      precision = true_count / total_sample_count
      print('%s: precision @ 1 = %.3f' % (datetime.now(), precision))

      summary = tf.Summary()
      summary.ParseFromString(sess.run(summary_op))
      summary.value.add(tag='Precision @ 1', simple_value=precision)
      summary_writer.add_summary(summary, global_step)
    except Exception as e:  # pylint: disable=broad-except
      coord.request_stop(e)

    coord.request_stop()
    coord.join(threads, stop_grace_period_secs=10)


def evaluate():
  """Eval CIFAR-10 for a number of steps."""
  with tf.Graph().as_default() as g:
    # Get images and labels for CIFAR-10.
    eval_data = FLAGS.eval_data == 'test'
#     images, labels = cifar10.inputs(eval_data=eval_data)

    # TEST CODE
    img_path = "/TEST_IMAGEPATH/image.png"
    input_img = tf.image.decode_png(tf.read_file(img_path), channels=3)
    casted_image = tf.cast(input_img, tf.float32)

    reshaped_image = tf.image.resize_image_with_crop_or_pad(casted_image, 24, 24)
    float_image = tf.image.per_image_withening(reshaped_image)
    images = tf.expand_dims(reshaped_image, 0) 

    logits = cifar10.inference(images)
    _, top_k_pred = tf.nn.top_k(logits, k=1)


    with tf.Session() as sess:
        saver = tf.train.Saver()
        ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
        if ckpt and ckpt.model_checkpoint_path:
          print("ckpt.model_checkpoint_path ", ckpt.model_checkpoint_path)
          saver.restore(sess, ckpt.model_checkpoint_path)
          global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
        else:
          print('No checkpoint file found')
          return

        print("Check point : %s" % ckpt.model_checkpoint_path)
        top_indices = sess.run([top_k_pred])
        print ("Predicted ", top_indices[0], " for your input image.")

evaluate()