H2O的Deep Autoencoders

先前的部分介绍了监督Deep Learning内容，Deep Learning 也可以用作非监督特征学习，尤其是在非线性降维方面。

Deep Autoencoders

Deep Autoencoders的原理可以看UFLDL的教程

例子：Anomaly Detection

#Import ECG train and test data into the H2O cluster from h2o.estimators.deeplearning import H2OAutoEncoderEstimator import h2o h2o.init() train_ecg = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/anomaly/ecg_discord_train.csv") test_ecg = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/anomaly/ecg_discord_test.csv") train_ecg.describe() test_ecg.describe() #Train deep autoencoder learning model on "normal" # training data, y ignored anomaly_model = H2OAutoEncoderEstimator(activation="Tanh", hidden=[50,50,50], sparse=True, l1=1e-4, epochs=100) anomaly_model.train(x=train_ecg.names,training_frame=train_ecg) # Compute reconstruction error with the Anomaly # detection app (MSE between output and input layers) recon_error = anomaly_model.anomaly(test_ecg) print 'recon_error:',recon_error # Testing = Reconstructing the test dataset test_recon = anomaly_model.predict(test_ecg) print 'test_recon:',test_recon