TensorFlow高层次机器学习API (tf.contrib.learn)

TensorFlow高层次机器学习API (tf.contrib.learn)

1.tf.contrib.learn.datasets.base.load_csv_with_header 加载csv格式数据

2.tf.contrib.learn.DNNClassifier 建立DNN模型(classifier)

3.classifer.fit 训练模型

4.classifier.evaluate 评价模型

5.classifier.predict 预测新样本

完整代码：

 1 from __future__ import absolute_import
 2 from __future__ import division
 3 from __future__ import print_function
 4
 5 import tensorflow as tf
 6 import numpy as np
 7
 8 # Data sets
 9 IRIS_TRAINING = "iris_training.csv"
10 IRIS_TEST = "iris_test.csv"
11
12 # Load datasets.
13 training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
14     filename=IRIS_TRAINING,
15     target_dtype=np.int,
16     features_dtype=np.float32)
17 test_set = tf.contrib.learn.datasets.base.load_csv_with_header(
18     filename=IRIS_TEST,
19     target_dtype=np.int,
20     features_dtype=np.float32)
21
22 # Specify that all features have real-value data
23 feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]
24
25 # Build 3 layer DNN with 10, 20, 10 units respectively.
26 classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
27                                             hidden_units=[10, 20, 10],
28                                             n_classes=3,
29                                             model_dir="/tmp/iris_model")
30
31 # Fit model.
32 classifier.fit(x=training_set.data,
33                y=training_set.target,
34                steps=2000)
35
36 # Evaluate accuracy.
37 accuracy_score = classifier.evaluate(x=test_set.data,
38                                      y=test_set.target)["accuracy"]
39 print('Accuracy: {0:f}'.format(accuracy_score))
40
41 # Classify two new flower samples.
42 new_samples = np.array(
43     [[6.4, 3.2, 4.5, 1.5], [5.8, 3.1, 5.0, 1.7]], dtype=float)
44 y = list(classifier.predict(new_samples, as_iterable=True))
45 print('Predictions: {}'.format(str(y)))

结果：

Accuracy：0.966667