tf.contrib.learn Quickstart
TensorFlow的机器学习高级API(tf.contrib.learn)使配置、训练、评估不同的学习模型变得更加容易。在这个教程里,你将使用tf.contrib.learn在Iris data set上构建一个神经网络分类器。代码有一下5个步骤:
Complete Neural Network Source Code
这里是神经网络的源代码:
from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import urllib import numpy as np import tensorflow as tf # Data sets IRIS_TRAINING = "iris_training.csv" IRIS_TRAINING_URL = "http://download.tensorflow.org/data/iris_training.csv" IRIS_TEST = "iris_test.csv" IRIS_TEST_URL = "http://download.tensorflow.org/data/iris_test.csv" def main(): # If the training and test sets aren't stored locally, download them. if not os.path.exists(IRIS_TRAINING): raw = urllib.urlopen(IRIS_TRAINING_URL).read() with open(IRIS_TRAINING, "w") as f: f.write(raw) if not os.path.exists(IRIS_TEST): raw = urllib.urlopen(IRIS_TEST_URL).read() with open(IRIS_TEST, "w") as f: f.write(raw) # Load datasets. training_set = tf.contrib.learn.datasets.base.load_csv_with_header( filename=IRIS_TRAINING, target_dtype=np.int, features_dtype=np.float32) test_set = tf.contrib.learn.datasets.base.load_csv_with_header( filename=IRIS_TEST, target_dtype=np.int, features_dtype=np.float32) # Specify that all features have real-value data feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)] # Build 3 layer DNN with 10, 20, 10 units respectively. classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3, model_dir="/tmp/iris_model") # Define the training inputs def get_train_inputs(): x = tf.constant(training_set.data) y = tf.constant(training_set.target) return x, y # Fit model. classifier.fit(input_fn=get_train_inputs, steps=2000) # Define the test inputs def get_test_inputs(): x = tf.constant(test_set.data) y = tf.constant(test_set.target) return x, y # Evaluate accuracy. accuracy_score = classifier.evaluate(input_fn=get_test_inputs, steps=1)["accuracy"] print("\nTest Accuracy: {0:f}\n".format(accuracy_score)) # Classify two new flower samples. def new_samples():
return np.array( [[6.4, 3.2, 4.5, 1.5], [5.8, 3.1, 5.0, 1.7]], dtype=np.float32) predictions = list(classifier.predict(input_fn=new_samples)) print( "New Samples, Class Predictions: {}\n" .format(predictions) )if __name__ == "__main__": main()
Load the Iris CSV data to TensorFlow
Iris data set包含了150行数据,3个种类:Iris setosa, Iris virginica, and Iris versicolor.
每一行包括了以下的数据:花萼的宽度,长度,花瓣的宽度,花的种类。花的种类有整数表示,0表示Iris setosa, 1表示Iris virginica, 2表示Iris versicolor.
Sepal Length | Sepal Width | Petal Length | Petal Width | Species |
---|---|---|---|---|
5.1 | 3.5 | 1.4 | 0.2 | 0 |
4.9 | 3.0 | 1.4 | 0.2 | 0 |
4.7 | 3.2 | 1.3 | 0.2 | 0 |
… | … | … | … | … |
7.0 | 3.2 | 4.7 | 1.4 | 1 |
6.4 | 3.2 | 4.5 | 1.5 | 1 |
6.9 | 3.1 | 4.9 | 1.5 | 1 |
… | … | … | … | … |
6.5 | 3.0 | 5.2 | 2.0 | 2 |
6.2 | 3.4 | 5.4 | 2.3 | 2 |
5.9 | 3.0 | 5.1 | 1.8 | 2 |
这里,Iris数据随机分割成了两组不同的CSV文件:
开始时,首先引进所有必要的模块,然后定义下载存储数据集的路径:
from __future__ import absolute_ import from __future__ import division from __future__ import print_function import os import urllib import tensorflow as tf import numpy as np IRIS_TRAINING = "iris_training.csv" IRIS_TRAINING_URL = "http://download.tensorflow.org/data/iris_training.csv" IRIS_TEST = "iris_test.csv" IRIS_TEST_URL = "http://download.tensorflow.org/data/iris_test.csv"
然后,如果训练和测试集没有在本地存储,下载:
if not os.path.exists(IRIS_TRAINING): raw = urllib.urlopen(IRIS_TRAINING_URL).read() with open(IRIS_TRAINING,'w') as f: f.write(raw) if not os.path.exists(IRIS_TEST): raw = urllib.urlopen(IRIS_TEST_URL).read() with open(IRIS_TEST,'w') as f: f.write(raw)
然后,用learn.datasets.base的load_csv_with_header()方法加载训练集和测试集成Dataset S,load_csv_with_header()包涵一下三个参数:
这里,目标是花的种类,是0-2的整数,所以数据类型是np.int:
# Load datasets. training_set = tf.contrib.learn.datasets.base.load_csv_with_header( filename=IRIS_TRAINING, target_dtype=np.int, features_dtype=np.float32) test_set = tf.contrib.learn.datasets.base.load_csv_with_header( filename=IRIS_TEST, target_dtype=np.int, features_dtype=np.float32)
tf.contrib.learn中的Dataset S是tuple,你可以通过data,target来访问特征值和目标值,比如,training_set.data,training_set.target
Construct a Deep Neural Network Classifier
tf.contrib.learn提供了多种预定义的模型,称为 Estimator S,你可以用“黑盒子”在你的数据上来训练和评估节点。这里,你讲配置深度神经网络分类器来拟合Iris数据,你可以用tf.contrib.learn.DNNClassifier作为示例:
# Specify that all features have real-value data feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)] # Build 3 layer DNN with 10, 20, 10 units respectively. classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3, model_dir="/tmp/iris_model")
首先定义特征所在的列,有4个特征,所以dimension设定为4.
然后,构建了DNNClassifier,包含以下参数:
Describe the training input pipeline
tf.contrib.learn API使用输入函数,创建TensorFlow节点来生成模型数据。这里,数据比较小,可以放在tf.constant。
# Define the test inputs def get_train_inputs():
x = tf.constant(training_set.data) y = tf.constant(training_set.target) return x, y
Fit the DNNClassifier to the Iris Training Data
配置了DNN分类器,你可以用fit方法来拟合数据,传递get_train_inputs到input_fn参数中,循环训练2000次:
# Fit model. classifier.fit(input_fn=get_train_inputs, steps=2000)
等效于:
classifier.fit(x=training_set.data, y=training_set.target, steps=1000) classifier.fit(x=training_set.data, y=training_set.target, steps=1000)
如果你想追踪训练模型,你可以用TensorFlow monitor来执行节点的日志。
“Logging and Monitoring Basics with tf.contrib.learn”
Evaluate Model Accuracy
你已经用训练数据拟合了模型,现在,你可以用evaluate方法在测试集上评估准确性。像fit一样,evaluate也需要一个输入函数来构建输入的通道,并返回评估结果的字典。
# Define the test inputs def get_test_inputs(): x = tf.constant(test_set.data) y = tf.constant(test_set.target) return x, y # Evaluate accuracy. accuracy_score = classifier.evaluate(input_fn=get_test_inputs, steps=1)["accuracy"] print("\nTest Accuracy: {0:f}\n".format(accuracy_score))
运行整个脚本,打印:
Test Accuracy: 0.966667
Classify New Samples
用predict()方法来分类新的样本,比如,你有下面的两个新样本:
Sepal Length | Sepal Width | Petal Length | Petal Width |
---|---|---|---|
6.4 | 3.2 | 4.5 | 1.5 |
5.8 | 3.1 | 5.0 | 1.7 |
predict方法返回一个generator,可以转换成list
# Classify two new flower samples.
def new_samples():
return np.array(
[[6.4, 3.2, 4.5, 1.5],
[5.8, 3.1, 5.0, 1.7]],dtype=np.float32)
predictions = list(classifier.predict(input_fn=new_samples))
print(
"New Samples, Class Predictions:
{}\n" .format(predictions))
结果大致如下:
New Samples, Class Predictions: [1 2]