title: ValueError: Cannot feed value of shape (1, 233, 472, 4) for Tensor 'image_tensor:0', which has shape '(?, ?, ?, 3)'
body: <p>I am new to TensorFlow, I am getting this valuing error for some images while on other images works fine. Is there anything wrong with the code?</p>
  <pre><code>import numpy as np
  import os
  import six.moves.urllib as urllib
  import sys
  import tarfile
  import tensorflow.compat.v1 as tf
  tf.disable_v2_behavior()
  import zipfile
  from collections import defaultdict
  from io import StringIO
  from matplotlib import pyplot as plt
  from PIL import Image
  
  from object_detection.utils import label_map_util
  
  from object_detection.utils import visualization_utils as vis_util
  
  # This is needed since the notebook is stored in the object_detection folder.
  sys.path.append(&quot;..&quot;)
  
  MODEL_NAME = 'new_graph'  # change to whatever folder has the new graph
  # MODEL_FILE = MODEL_NAME + '.tar.gz'   # these lines not needed as we are using our own model
  # DOWNLOAD_BASE = 'http://download.tensorflow.org/models/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')  # our labels are in training/object-detection.pbkt
  
  NUM_CLASSES = 3
  
  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, 4)).astype(np.uint8)
  
  
  PATH_TO_TEST_IMAGES_DIR = 'test_images'
  TEST_IMAGE_PATHS = [os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.png'.format(i)) for i in range(1, 8)]
  # adjust range for # of images in folder
  
  # Size, in inches, of the output images.
  IMAGE_SIZE = (12, 8)
  
  
  with detection_graph.as_default():
      with tf.Session(graph=detection_graph) as sess:
          i = 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.reshape(image, ())
              # print(image)
              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)
              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.
              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.
              scores = detection_graph.get_tensor_by_name('detection_scores:0')
              classes = detection_graph.get_tensor_by_name('detection_classes:0')
              num_detections = detection_graph.get_tensor_by_name('num_detections:0')
              # Actual detection.
              (boxes, scores, classes, num_detections) = sess.run(
                  [boxes, scores, 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.int32),
                  np.squeeze(scores),
                  category_index,
                  use_normalized_coordinates=True,
                  line_thickness=8)
  
              plt.figure(figsize=IMAGE_SIZE)
              plt.imshow(image_np)    # matplotlib is configured for command line only so we save the outputs instead
              plt.savefig(&quot;outputs/detection_output{}.png&quot;.format(i))  # create an outputs folder for the images to be saved
              i = i+1  # this was a quick fix for iteration, create a pull request if you'd like
  </code></pre>
  <p>Error :</p>
  <p>Traceback (most recent call last):
  File &quot;custom_model_images.py&quot;, line 82, in 
  feed_dict={image_tensor: image_np_expanded})</p>
  <p>File &quot;C:\Python36\lib\site-packages\tensorflow_core\python\client\session.py&quot;, line 956, in run
  run_metadata_ptr)</p>
  <p>File &quot;C:\Python36\lib\site-packages\tensorflow_core\python\client\session.py&quot;, line 1156, in _run
  (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))</p>
  <p>ValueError: Cannot feed value of shape (1, 233, 472, 4) for Tensor 'image_tensor:0', which has shape '(?, ?, ?, 3)'</p>