#测试读取后的Model print(clf2.predict(X[0:1]))# [0] # 方法二:joblib. joblib是sklearn的外部模块。 from sklearn.externals import joblib #jbolib模块 #保存Model(注:save文件夹要预先建立,否则会报错) joblib.dump(clf, 'save_model/clf.pkl') #读取Model clf3 = joblib.load('save_model/clf.p...
train(params_xgb, trainset, evals=[(trainset, 'train'),(valset, 'eval')], num_boost_round=params_xgb["num_boost_round"], early_stopping_rounds=params_xgb["early_stopping_rounds"], verbose_eval=1000) model.save_model("../models/xgb_%d.json" % fold) model_xgb.append(model) xgb_...
from sklearn.externals import joblib #jbolib模块 #保存Model(注:save文件夹要预先建立,否则会报错) joblib.dump(clf, 'save/clf.pkl') #读取Model clf3 = joblib.load('save/clf.pkl') #测试读取后的Model print(clf3.predict(X[0:1])) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 最后可以知道jobl...
使用joblib 保存 joblib是sklearn的外部模块。 from sklearn.externals import joblib #jbolib模块 #保存Model(注:save文件夹要预先建立,否则会报错) joblib.dump(clf, 'save/clf.pkl') #读取Model clf3 = joblib.load('save/clf.pkl') #测试读取后的Model print(clf3.predict(X[0:1])) 最后可以知道job...
# saveModel(path) # 加载模型 global vectorizer clf, vectorizer = useModel("model_rg_train_20171230_1000008.txt","feature_rg_train_20171230_1000008.pkl") source = "我很开心" source = source.replace("\r", "") source = source.replace("\n", "") ...
>>> os.chdir("D://model_save") >>> from sklearn import svm >>> X = [[0, 0], [1, 1]] >>> y = [0, 1] >>> clf = svm.SVC() >>> clf.fit(X, y) >>> clf.fit(train_X,train_y) >>> joblib.dump(clf, "train_model.m") ...
现在让我们创建一个新的模型版本并导出一个 SavedModel,这次导出到my_mnist_model/0002目录: model = [...] # build and train a new MNIST model versionmodel_version = "0002"model_path = Path(model_name) / model_versionmodel.save(model_path, save_format="tf") ...
model.save("my_keras_model", save_format="tf") 当您设置save_format="tf"时,Keras 会使用 TensorFlow 的SavedModel格式保存模型:这是一个目录(带有给定名称),包含多个文件和子目录。特别是,saved_model.pb文件包含模型的架构和逻辑,以序列化的计算图形式,因此您不需要部署模型的源代码才能在生产中使用它;Sav...
print("training and save model...") joblib.dump((clf,training_names,stdSlr,k,voc),"bof.pkl",compress=3) 在训练图像上的运行输出: "C:\Program Files\Python\Python36\python.exe"D:/python/image_classification/feature_detection.p...
在下面的几行代码中,我们会把上面得到的模型保存到pickle_model.pkl文件中,然后将其载入。最后,使用载入的模型基于测试数据计算 Accuracy,并输出预测结果。 代码语言:javascript 复制 importpickle # # Create your modelhere(sameasabove)# # Save to fileinthe current working directory ...