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+from sklearn import datasets, metrics%0A%0Airis = datasets.load_iris()%0A%0Adef my_model(X, y):%0A    %22%22%22This is DNN with 10, 20, 10 hidden layers, and dropout of 0.5 probability.%22%22%22%0A    layers = learn.ops.dnn(X, %5B10, 20, 10%5D, dropout=0.5)%0A    return learn.models.logistic_regression(layers, y)%0A%0Aclassifier = learn.TensorFlowEstimator(model_fn=my_model, n_classes=3)%0Aclassifier.fit(iris.data, iris.target)%0Ascore = metrics.accuracy_score(iris.target, classifier.predict(iris.data))%0Aprint(%22Accuracy: %25f%22 %25 score)%0A

Create custom-model.py