小编Ngu*_*uan的帖子

我是研究深度学习的新手。我用 tensorflow 加载了一个保存的模型，并用 Flask 制作了一个 API，但我收到错误“Container localhost 不存在”。当我预测时，请帮我修复它。谢谢你。

张量流版本 1.13.1

keras 版本 2.2.4

烧瓶版本 1.0.3

我通过命令'python app.py'运行它

这是我的代码：

from flask import Flask, request
from tensorflow.python.keras.models import load_model
import numpy as np
import tensorflow as tf
from tensorflow.python.keras.applications import imagenet_utils
from tensorflow.python.keras.preprocessing.image import img_to_array
from tensorflow.python.keras.preprocessing.image import load_img
from PIL import Image
import io

app = Flask(__name__)
model = None
labels = ['AchatinaFulice', 'Riptortus', 'SquashBug']


def load_model_insect():
    global model
    model = load_model('insect2.h5')
    global graph
    graph = tf.get_default_graph()

def predict(image):
    image = …

我的输出：

def load_data(self):
    """
    Load data from list of paths
    :return: 3D-array X and 2D-array y
    """
    X = None
    y = None
    df = pd.read_excel('data/Data.xlsx', header=None)
    for i in range(len(df.columns)):
        sentences_ = df[i].to_numpy().tolist()
        label_vec = [0.0 for _ in range(0, self.n_class)]
        label_vec[i] = 1.0
        labels_ = [label_vec for _ in range(0, len(sentences_))]
        if X is None:
            X = sentences_
            y = labels_
        else:
            X += sentences_
            y += labels_
    X, max_length = self.tokenize_sentences(X)
    X = self.word_embed_sentences(X, max_length=self.max_length)
    return np.array(X), …

我有一个代码 Volley Code

 val queue = Volley.newRequestQueue(context)
 val stringRequest = StringRequest(Request.Method.GET, linkTrang,
            Response.Listener<String> { response ->
                mTextView.text = "Response is: " + response.substring(0,500));
            },
            Response.ErrorListener {  })
    {

    }
    queue.add(stringRequest)

如何在此设置名为 Authorization 的标头？