我和我的朋友正在為黑客馬拉松制作圖像識(shí)別的深度學(xué)習(xí)模型，我們不斷遇到這個(gè)問(wèn)題?；旧?，當(dāng)我運(yùn)行 run.py 進(jìn)行分析和成像時(shí)，它會(huì)返回 sstable（壞幻數(shù)）錯(cuò)誤。我們不知道為什么會(huì)這樣，也不知道該怎么辦。這是 run.py：    import os, gcfrom skimage import ioimport globimport pandas as pdimport globimport tensorflow as tffrom tensorflow import kerasfrom keras.preprocessing import imagefrom tensorflow.keras.models import Sequential, save_model, load_modelimport matplotlib.pyplot as pltimport numpy as npfrom skimage import transformfrom keras.optimizers import Adamfrom keras.applications import mobilenet_v2from PIL import Imagepath = []for file in os.listdir("./media_cdn"):    path.append(file)print(path)filepath = './saved_model'model = load_model(filepath, custom_objects= None, compile = False)loss = 'CategoricalCrossentropy'optimizer = Adam(lr=1e-5)metrics = ['binary_accuracy']model.compile(optimizer=optimizer, loss=loss, metrics=metrics)def load(filename):np_image = Image.open("./media_cdn/" + filename)np_image = np.array(np_image).astype('float32')/255np_image = transform.resize(np_image, (244, 244, 3))np_image = np.expand_dims(np_image, axis=0)return np_imagenew_image = load(path[0])print(new_image.shape)new_model = keras.Sequential([model])new_model.load_weights('./model_weights')prediction = new_model.predict_classes(new_image)classes = np.argmax(prediction, axis = -1)print(classes)print('This is the Diagnosis:')if classes == 0:    print('MELANOMA')if classes == 1:    print('Melanocytic Nevus')if classes == 2:    print('Basal Cell Carcinoma')if classes == 3:    print('Arctinic Keratosis')if classes == 4:    print('Benign Keratosis')if classes == 5:    print('Dermatofibroma')if classes == 6:    print('Vascular Lesion')if classes == 7:    print('Squamous Cell Carcinoma')if classes == 8:    print(['Unknown', 'BCC', 'AK', 'BKL', 'DF', 'VASC', 'SCC', 'UNK'])classes = np.argmax(prediction, axis = 1)print(classes)調(diào)試時(shí)，錯(cuò)誤顯示在行中l(wèi)oad_model。我們不知道如何修復(fù)它，歡迎任何幫助。