我用这个code重新训练了模型。然后在克隆之后遵循来自这个repo的指令。替换新生成的标签.txt和图形.pb文件。当发布一张图片用下面的代码分类时
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
然后显示以下错误。在
TypeError:无法将feed_dict key解释为张量:名称'DecodeJpeg'/内容:0'是指不存在的张量。图形中不存在“DecodeJpeg/contents”操作。
你的问题就在这条线上:
^{pr2}$feed_dict
字典中的键应该是张量,而不是字符串。您可以先按名称查找张量:相关问题 更多 >
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