Deep Learning with H2O in Python

Step 1-  First of all , we need to install H2o package in Python.

on anaconda prompt
pip install h2o

Step 2-  Initialize and start the cluster -

h2o.init()
from h2o.estimators.deeplearning import H2ODeepLearningEstimator


Step 3-  load train and test data set-

train = h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")

Step 4-  Creating test and train data set using split-

splits = train.split_frame(ratios=[0.75], seed=1234)



Step 5-  Configuring the model-

model = H2ODeepLearningEstimator(distribution = "AUTO",activation = "RectifierWithDropout",hidden = [32,32],input_dropout_ratio = 0.2,l1 = 1e-5,epochs = 10)

 Step 6-  train(fit the model)-

model.train(x="sepal_len", y=["petal_len"], training_frame=splits[0])

 Step 7-  predicting using trained model and creating a new column in test data-

 (splits[1]['predicted_sepal_len'])=model.predict(splits[1])

reference-   complete book is present at-

http://docs.h2o.ai/h2o/latest-stable/h2o-docs/booklets/DeepLearningBooklet.pdf
Location: Bengaluru, Karnataka, India

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