H2O学习笔记（四）——Deep Learning 例子

这里写图片描述##MNIST Digit Classification

MNIST一个比较出名的数据集，包括60000张训练图片和10000张测试图片，每张图片是一个手写数字，包括28²像素值的手写识别数据,扫描的手写数字如下图所示：

Example in python

import h2o from h2o.estimators.deeplearning import H2ODeepLearningEstimator # Start H2O cluster with all available cores (default) h2o.init() # Get a brief summary of the data train.describe() test.describe() train = h2o.import_file("./train.csv.gz") test = h2o.import_file("./test.csv.gz") x=train.names[0:784] y="C785" # Encode the response column as categorical for multinomial classification train[y] = train[y].asfactor() test[y] = test[y].asfactor() model = H2ODeepLearningEstimator(distribution='multinomial', activation='RectifierWithDropout', hidden=[32,32,32], input_dropout_ratio=0.2, sparse=True, l1=1e-5, epochs=10, nfolds=5) model.train(x=x,y=y,training_frame=train,validation_frame=test) print model.params print model.model_performance(train=True) print model.model_performance(test=True) print model.auc(train=True) print model.predict(test_data=test) # Get a brief summary of the data # train.describe() # test.describe() 1234567891011121314151617181920212223242526272829303132333435363738 1234567891011121314151617181920212223242526272829303132333435363738

Cartesian Grid Search

对超参数hidden和l1两个参数做网格搜索

from h2o.estimators import H2ODeepLearningEstimator from h2o.grid.grid_search import H2OGridSearch import h2o h2o.init() train = h2o.import_file("./train.csv.gz") test = h2o.import_file("./test.csv.gz") # Get a brief summary of the data train.describe() test.describe() # Specify the response and predictor columns x=train.names[0:784] y="C785" # Encode the response column as categorical for multinomial classification train[y] = train[y].asfactor() test[y] = test[y].asfactor() hidden_opt = [[32,32],[32,16,8],[100]] l1_opt = [1e-4,1e-3] hyper_parameters = {"hidden":hidden_opt, "l1":l1_opt} model_grid = H2OGridSearch(H2ODeepLearningEstimator, hyper_params=hyper_parameters) model_grid.train(x=x, y=y, distribution="multinomial", epochs=1000, training_frame=train, validation_frame=test, score_interval=2, stopping_rounds=3, stopping_tolerance=0.05, stopping_metric="misclassification") #print model grid search results for model in model_grid: print model.model_id + " mse: " + str(model.mse()) 12345678910111213141516171819202122232425262728293031323334353637383940 12345678910111213141516171819202122232425262728293031323334353637383940

搜索结果：

Random Grid Search

如果参数的搜索空间太大，可以使用随机网格搜索，只需要指定最大的模型个数和seed,使用search_criteria来设置

hidden_opt =[[17,32],[8,19],[32,16,8],[100],[10,10,10,10]] l1_opt = [s/1e6 for s in range(1,1001)] hyper_parameters = {"hidden":hidden_opt, "l1":l1_opt} search_criteria = {"strategy":"RandomDiscrete", "max_models":10, "max_runtime_secs":100, "seed":123456} model_grid = H2OGridSearch(H2ODeepLearningEstimator, hyper_params=hyper_parameters, search_criteria = search_criteria) model_grid.train(x=x, y=y, distribution="multinomial", epochs=1000, training_frame=train, validation_frame=test, score_interval=2, stopping_rounds=3, stopping_tolerance=0.05, stopping_metric="misclassification") #print model grid search results for model in model_grid: print model.model_id + " mse: " + str(model.mse()) 123456789101112131415161718192021222324 123456789101112131415161718192021222324

搜索结果：