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 h2ofrom h2o.estimators.deeplearning import H2ODeepLearningEstimator# Start H2O cluster with all available cores (default)h2o.init()# Get a brief summary of the datatrain.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 classificationtrain[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.paramsprint 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()
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Cartesian Grid Search

对超参数hidden和l1两个参数做网格搜索

from h2o.estimators import H2ODeepLearningEstimatorfrom h2o.grid.grid_search import H2OGridSearchimport h2oh2o.init()train = h2o.import_file("./train.csv.gz")test = h2o.import_file("./test.csv.gz")# Get a brief summary of the datatrain.describe()test.describe()# Specify the response and predictor columnsx=train.names[0:784]y="C785"# Encode the response column as categorical for multinomial classificationtrain[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 resultsfor model in model_grid:    print model.model_id + " mse: " + str(model.mse())
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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 resultsfor model in model_grid:    print model.model_id + " mse: " + str(model.mse())
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搜索结果：