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# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from sklearn.pipeline import Pipeline
from sklearn.datasets import load_iris
from sklearn import cross_validation
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score
from tensorflow.contrib import learn
iris = load_iris()
X_train, X_test, y_train, y_test = cross_validation.train_test_split(iris.data, iris.target,
test_size=0.2, random_state=42)
# It's useful to scale to ensure Stochastic Gradient Descent will do the right thing
scaler = StandardScaler()
# DNN classifier
DNNclassifier = learn.TensorFlowDNNClassifier(hidden_units=[10, 20, 10], n_classes=3, steps=200)
pipeline = Pipeline([('scaler', scaler), ('DNNclassifier', DNNclassifier)])
pipeline.fit(X_train, y_train)
score = accuracy_score(y_test, pipeline.predict(X_test))
print('Accuracy: {0:f}'.format(score))
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