一.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
二.在tf.contrib.learn中创建input函数（输入预处理函数）
格式：
def my_input_fn():
# Preprocess your data here...
# ...then return 1) a mapping of feature columns to Tensors with
# the corresponding feature data, and 2) a Tensor containing labels
return feature_cols, labels
完整代码:
1 # Copyright 2016 The TensorFlow Authors. All Rights Reserved. 2 # 3 # Licensed under the Apache License, Version 2.0 (the "License"); 4 # you may not use this file except in compliance with the License. 5 # You may obtain a copy of the License at 6 # 7 # http://www.apache.org/licenses/LICENSE-2.0 8 # 9 # Unless required by applicable law or agreed to in writing, software 10 # distributed under the License is distributed on an "AS IS" BASIS, 11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 # See the License for the specific language governing permissions and 13 # limitations under the License. 14 """DNNRegressor with custom input_fn for Housing dataset.""" 15 16 from __future__ import absolute_import 17 from __future__ import division 18 from __future__ import print_function 19 20 import itertools 21 22 import pandas as pd 23 import tensorflow as tf 24 25 tf.logging.set_verbosity(tf.logging.INFO) 26 27 COLUMNS = ["crim", "zn", "indus", "nox", "rm", "age", 28 "dis", "tax", "ptratio", "medv"] 29 FEATURES = ["crim", "zn", "indus", "nox", "rm", 30 "age", "dis", "tax", "ptratio"] 31 LABEL = "medv" 32 33 34 def input_fn(data_set): 35 feature_cols = {k: tf.constant(data_set[k].values) for k in FEATURES} 36 labels = tf.constant(data_set[LABEL].values) 37 return feature_cols, labels 38 39 40 def main(unused_argv): 41 # Load datasets 42 training_set = pd.read_csv("boston_train.csv", skipinitialspace=True, 43 skiprows=1, names=COLUMNS) 44 test_set = pd.read_csv("boston_test.csv", skipinitialspace=True, 45 skiprows=1, names=COLUMNS) 46 47 # Set of 6 examples for which to predict median house values 48 prediction_set = pd.read_csv("boston_predict.csv", skipinitialspace=True, 49 skiprows=1, names=COLUMNS) 50 51 # Feature cols 52 feature_cols = [tf.contrib.layers.real_valued_column(k) 53 for k in FEATURES] 54 55 # Build 2 layer fully connected DNN with 10, 10 units respectively. 56 regressor = tf.contrib.learn.DNNRegressor(feature_columns=feature_cols, 57 hidden_units=[10, 10], 58 model_dir="/tmp/boston_model") 59 60 # Fit 61 regressor.fit(input_fn=lambda: input_fn(training_set), steps=5000) 62 63 # Score accuracy 64 ev = regressor.evaluate(input_fn=lambda: input_fn(test_set), steps=1) 65 loss_score = ev["loss"] 66 print("Loss: {0:f}".format(loss_score)) 67 68 # Print out predictions 69 y = regressor.predict(input_fn=lambda: input_fn(prediction_set)) 70 # .predict() returns an iterator; convert to a list and print predictions 71 predictions = list(itertools.islice(y, 6)) 72 print("Predictions: {}".format(str(predictions))) 73 74 if __name__ == "__main__": 75 tf.app.run()