'Cannot interpret feed_dict key as Tensor: ' + e.args[0])

Question

I have retrained the model using this code. Then followed instruction from this repo after cloning. Replaced newly generated labels.txt and graph.pb file. When posting an image to classify using the following code ,

MAX_K = 10

TF_GRAPH = "{base_path}/inception_model/graph.pb".format(
    base_path=os.path.abspath(os.path.dirname(__file__)))
TF_LABELS = "{base_path}/inception_model/labels.txt".format(
    base_path=os.path.abspath(os.path.dirname(__file__)))


def load_graph():
    sess = tf.Session()
    with tf.gfile.FastGFile(TF_GRAPH, 'rb') as tf_graph:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(tf_graph.read())
        tf.import_graph_def(graph_def, name='')
    label_lines = [line.rstrip() for line in tf.gfile.GFile(TF_LABELS)]
    softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
    return sess, softmax_tensor, label_lines


SESS, GRAPH_TENSOR, LABELS = load_graph()


@csrf_exempt
def classify_api(request):
    data = {"success": False}

    if request.method == "POST":
        tmp_f = NamedTemporaryFile()

    if request.FILES.get("image", None) is not None:
        image_request = request.FILES["image"]
        image_bytes = image_request.read()
        image = Image.open(io.BytesIO(image_bytes))
        image.save(tmp_f, image.format)
    elif request.POST.get("image64", None) is not None:
        base64_data = request.POST.get("image64", None).split(',', 1)[1]
        plain_data = b64decode(base64_data)
        tmp_f.write(plain_data)

    classify_result = tf_classify(tmp_f, int(request.POST.get('k', MAX_K)))
    tmp_f.close()

    if classify_result:
        data["success"] = True
        data["confidence"] = {}
        for res in classify_result:
            data["confidence"][res[0]] = float(res[1])

return JsonResponse(data)


def tf_classify(image_file, k=MAX_K):
    result = list()

    image_data = tf.gfile.FastGFile(image_file.name, 'rb').read()

    predictions = SESS.run(GRAPH_TENSOR, {'DecodeJpeg/contents:0': image_data})
    predictions = predictions[0][:len(LABELS)]
    top_k = predictions.argsort()[-k:][::-1]
    for node_id in top_k:
        label_string = LABELS[node_id]
        score = predictions[node_id]
        result.append([label_string, score])

    return result

then it shows the following error.

TypeError: Cannot interpret feed_dict key as Tensor: The name 'DecodeJpeg/contents:0' refers to a Tensor which does not exist. The operation, 'DecodeJpeg/contents', does not exist in the graph.

score 0 · Answer 1 · answered May 04 '18 at 18:09

0

Your problem is on this line:

predictions = SESS.run(GRAPH_TENSOR, {'DecodeJpeg/contents:0': image_data})

The key in the feed_dict dictionary should be a tensor, not a string. You can look up the tensor by name first:

data_tensor = tf.get_default_graph().get_tensor_by_name('DecodeJpeg/contents:0')
predictions = SESS.run(GRAPH_TENSOR, {data_tensor: image_data})

answered May 04 '18 at 18:09

David Parks

30,789
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Check the name of all tensors, make sure it's here, here's a guide: https://stackoverflow.com/questions/36883949/in-tensorflow-get-the-names-of-all-the-tensors-in-a-graph – David Parks May 05 '18 at 19:09

score 0 · Answer 2 · edited Feb 07 '20 at 15:21

0

If you are using tensorflow.keras, then you can do the following, but if you are using Keras(but not tensorflow.keras), then try the method for Keras like everyone mentioned above. but the same procedure applies thought.

from tensorflow.keras import backend as K
K.clear_session()

put in the place every time you re-use the model.

edited Feb 07 '20 at 15:21

HK boy

1,398
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answered Feb 07 '20 at 14:52

Simin Yu

1

'Cannot interpret feed_dict key as Tensor: ' + e.args[0])

2 Answers2