我和我的朋友正在为一个黑客马拉松制作一个用于图像识别的深度学习模型，我们不断遇到这个问题。

基本上，当我运行run.py来分析图像时，它会返回SSTable（坏魔法数）错误。

我们不知道为什么会发生这种情况，也不知道该怎么办。

这是run.py的代码：

    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)

在调试时，错误显示在load_model这一行。

我们不知道如何修复它，任何帮助都将受到欢迎。

回答：

好的，我已经弄清楚了为什么会发生这种情况。看来我必须在我的电脑上运行模型，以便生成正确的变量和模型文件。