使用TensorFlow我试图检测一个对象（png和灰度图像） . 我成功地训练并导出了一个model.ckpt . 现在我正在尝试恢复已保存的model.ckpt以进行预测 . 这是脚本：

import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile

from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image

if tf.__version__ != '1.4.0':
raise ImportError('Please upgrade your tensorflow installation to v1.4.0!')

# This is needed to display the images.
#matplotlib inline

# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")

from utils import label_map_util

from utils import visualization_utils as vis_util


MODEL_NAME = 'melon_graph'

# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('training', 'object_detection.pbtxt')

NUM_CLASSES = 1

detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')

label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True) 
category_index = label_map_util.create_category_index(categories)

def load_image_into_numpy_array(image):
    (im_width, im_height) = image.size
    return np.array(image.getdata()).reshape((im_height, im_width, 1)).astype(np.float64)

# For the sake of simplicity we will use only 2 images:
# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
PATH_TO_TEST_IMAGES_DIR = 'test_images'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'te_data{}.png'.format(i)) for i in range(1, 336) ]

# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)

with detection_graph.as_default():
    with tf.Session(graph=detection_graph) as sess:
      # Definite input and output Tensors for detection_graph
      image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
      # Each box represents a part of the image where a particular object was detected.
  detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
      # Each score represent how level of confidence for each of the objects.
      # Score is shown on the result image, together with the class label.
      detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
      detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
      num_detections = detection_graph.get_tensor_by_name('num_detections:0')
      for image_path in TEST_IMAGE_PATHS:
        image = Image.open(image_path)
        # the array based representation of the image will be used later in order to prepare the
        # result image with boxes and labels on it.
        image_np = load_image_into_numpy_array(image)
        # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
        image_np_expanded = np.expand_dims(image_np, axis=0)
        # Actual detection.
        (boxes, scores, classes, num) = sess.run([detection_boxes, detection_scores, detection_classes, num_detections], feed_dict={image_tensor: image_np_expanded})
        # Visualization of the results of a detection.
    vis_util.visualize_boxes_and_labels_on_image_array(image_np,np.squeeze(boxes),np.squeeze(classes).astype(np.float64), np.squeeze(scores), category_index, use_normalized_coordinates=True, line_thickness=5)
        plt.figure(figsize=IMAGE_SIZE)
        plt.imshow(image_np)

这是错误

回溯（最近一次调用最后一次）：文件“cochlear_detection.py”，第81行，（box，scores，classes，num）= sess.run（[detection_boxes，detection_scores，detection_classes，num_detections]，feed_dict = {image_tensor：image_np_expanded }）文件“/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py”，第889行，运行run_metadata_ptr）文件“/anaconda/lib/python3.6/site-packages/ tensorflow / python / client / session.py“，第1096行，在_run％（np_val.shape，subfeed_t.name，str（subfeed_t.get_shape（））））ValueError：无法提供形状值（1,2048,2048， 1）对于Tensor'image_tensor：0'，其形状为'（？，？，？，3）'

valueerror