1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
|
# Copyright 2016 The TensorFlow 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.
"""Example of DNNClassifier for Iris plant dataset, with pipeline."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from sklearn import cross_validation
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
import tensorflow as tf
learn = tf.contrib.learn
def main(unused_argv):
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.
classifier = learn.DNNClassifier(
feature_columns=learn.infer_real_valued_columns_from_input(x_train),
hidden_units=[10, 20, 10],
n_classes=3)
pipeline = Pipeline([('scaler', scaler), ('DNNclassifier', classifier)])
pipeline.fit(x_train, y_train, DNNclassifier__steps=200)
score = accuracy_score(y_test, list(pipeline.predict(x_test)))
print('Accuracy: {0:f}'.format(score))
if __name__ == '__main__':
tf.app.run()
|
