TensorFlow入门 - 使用TensorFlow给鸢尾花分类(线性模型)
TensorFlow入门 - 使用TensorFlow给鸢尾花分类(线性模型)
第一个例子将使用TensorFlow封装的Estimator来实现一个简单的Classifier，该Classifier能够区分3种比较难分辨的鸢尾花，分别是Iris Setosa(山鸢尾)、Iris Versicolour(变色鸢尾)和Iris Virginica(维吉尼亚鸢尾)。
不同种类植物有不同的性状，我们对不同的鸢尾花的区分是根据它们的某些性状来进行的。具体地说，我们通过4个特征值来对鸢尾花进行区分，这四个特征值(单位cm)包括sepal length(萼片长度)、sepal width(萼片宽度)、petal length(花瓣长度)、petal width(花瓣宽度)。
环境搭建
本例使用Jupyter Notebook进行具体实现，使用它需要安装Anaconda，这个部分参考博主之前的博文。
Ubuntu16.04使用Anaconda5搭建TensorFlow使用环境 图文详细教程
Jupyter Notebook即此前的Ipython Notebook，是一个web应用程序，可以以文档形式保存所有输入和输出。
鸢尾花数据集
鸢尾花数据集是一个经典的机器学习数据集，非常适合用来入门。它包括5列数据：前4列代表4个特征值即sepal length(萼片长度)、sepal width(萼片宽度)、petal length(花瓣长度)、petal width(花瓣宽度)；最后一列为Species，即鸢尾花的种类，是我们训练目标，在机器学习中称作label。这种数据也被称作标记数据(labeled data)。
机器学习中，为了保证测试结果的准确性，一般会从数据集中抽取一部分数据专门留作测试，其余数据用于训练。本例使用了两个CSV格式的数据文件，一个是iris_training.csv即训练文件，另一个是iris_test.csv，即测试文件。
具体实现
在tensorflow虚拟环境中启动jupyter notebook
steve@steve-Lenovo-V2000:~$ source activate tensorflow
(tensorflow) steve@steve-Lenovo-V2000:~$ jupyter notebookIn[1]
import tensorflow as tf
import numpy as np
print(tf.__version__)
1.3.0In[2]
from tensorflow.contrib.learn.python.learn.datasets import base
#所用的数据集文件
IRIS_TRAINING = "iris_training.csv"
IRIS_TEST = "iris_test.csv"
#加载数据集
training_set = base.load_csv_with_header(filename = IRIS_TRAINING,
features_dtype = np.float32,
target_dtype = np.int)
test_set = base.load_csv_with_header(filename = IRIS_TEST,
features_dtype = np.float32,
target_dtype = np.int)
print(training_set.data)
print(training_set.target)
[[ 6.4000001 2.79999995 5.5999999 2.20000005]
[ 5. 2.29999995 3.29999995 1. ]
[ 4.9000001 2.5 4.5 1.70000005]
[ 4.9000001 3.0999999 1.5 0.1 ]
[ 5.69999981 3.79999995 1.70000005 0.30000001]
[ 4.4000001 3.20000005 1.29999995 0.2 ]
[ 5.4000001 3.4000001 1.5 0.40000001]
[ 6.9000001 3.0999999 5.0999999 2.29999995]
[ 6.69999981 3.0999999 4.4000001 1.39999998]
[ 5.0999999 3.70000005 1.5 0.40000001]
[ 5.19999981 2.70000005 3.9000001 1.39999998]
[ 6.9000001 3.0999999 4.9000001 1.5 ]
[ 5.80000019 4. 1.20000005 0.2 ]
[ 5.4000001 3.9000001 1.70000005 0.40000001]
[ 7.69999981 3.79999995 6.69999981 2.20000005]
[ 6.30000019 3.29999995 4.69999981 1.60000002]
[ 6.80000019 3.20000005 5.9000001 2.29999995]
[ 7.5999999 3. 6.5999999 2.0999999 ]
[ 6.4000001 3.20000005 5.30000019 2.29999995]
[ 5.69999981 4.4000001 1.5 0.40000001]
[ 6.69999981 3.29999995 5.69999981 2.0999999 ]
[ 6.4000001 2.79999995 5.5999999 2.0999999 ]
[ 5.4000001 3.9000001 1.29999995 0.40000001]
[ 6.0999999 2.5999999 5.5999999 1.39999998]
[ 7.19999981 3. 5.80000019 1.60000002]
[ 5.19999981 3.5 1.5 0.2 ]
[ 5.80000019 2.5999999 4. 1.20000005]
[ 5.9000001 3. 5.0999999 1.79999995]
[ 5.4000001 3. 4.5 1.5 ]
[ 6.69999981 3. 5. 1.70000005]
[ 6.30000019 2.29999995 4.4000001 1.29999995]
[ 5.0999999 2.5 3. 1.10000002]
[ 6.4000001 3.20000005 4.5 1.5 ]
[ 6.80000019 3. 5.5 2.0999999 ]
[ 6.19999981 2.79999995 4.80000019 1.79999995]
[ 6.9000001 3.20000005 5.69999981 2.29999995]
[ 6.5 3.20000005 5.0999999 2. ]
[ 5.80000019 2.79999995 5.0999999 2.4000001 ]
[ 5.0999999 3.79999995 1.5 0.30000001]
[ 4.80000019 3. 1.39999998 0.30000001]
[ 7.9000001 3.79999995 6.4000001 2. ]
[ 5.80000019 2.70000005 5.0999999 1.89999998]
[ 6.69999981 3. 5.19999981 2.29999995]
[ 5.0999999 3.79999995 1.89999998 0.40000001]
[ 4.69999981 3.20000005 1.60000002 0.2 ]
[ 6. 2.20000005 5. 1.5 ]
[ 4.80000019 3.4000001 1.60000002 0.2 ]
[ 7.69999981 2.5999999 6.9000001 2.29999995]
[ 4.5999999 3.5999999 1. 0.2 ]
[ 7.19999981 3.20000005 6. 1.79999995]
[ 5. 3.29999995 1.39999998 0.2 ]
[ 6.5999999 3. 4.4000001 1.39999998]
[ 6.0999999 2.79999995 4. 1.29999995]
[ 5. 3.20000005 1.20000005 0.2 ]
[ 7. 3.20000005 4.69999981 1.39999998]
[ 6. 3. 4.80000019 1.79999995]
[ 7.4000001 2.79999995 6.0999999 1.89999998]
[ 5.80000019 2.70000005 5.0999999 1.89999998]
[ 6.19999981 3.4000001 5.4000001 2.29999995]
[ 5. 2. 3.5 1. ]
[ 5.5999999 2.5 3.9000001 1.10000002]
[ 6.69999981 3.0999999 5.5999999 2.4000001 ]
[ 6.30000019 2.5 5. 1.89999998]
[ 6.4000001 3.0999999 5.5 1.79999995]
[ 6.19999981 2.20000005 4.5 1.5 ]
[ 7.30000019 2.9000001 6.30000019 1.79999995]
[ 4.4000001 3. 1.29999995 0.2 ]
[ 7.19999981 3.5999999 6.0999999 2.5 ]
[ 6.5 3. 5.5 1.79999995]
[ 5. 3.4000001 1.5 0.2 ]
[ 4.69999981 3.20000005 1.29999995 0.2 ]
[ 6.5999999 2.9000001 4.5999999 1.29999995]
[ 5.5 3.5 1.29999995 0.2 ]
[ 7.69999981 3. 6.0999999 2.29999995]
[ 6.0999999 3. 4.9000001 1.79999995]
[ 4.9000001 3.0999999 1.5 0.1 ]
[ 5.5 2.4000001 3.79999995 1.10000002]
[ 5.69999981 2.9000001 4.19999981 1.29999995]
[ 6. 2.9000001 4.5 1.5 ]
[ 6.4000001 2.70000005 5.30000019 1.89999998]
[ 5.4000001 3.70000005 1.5 0.2 ]
[ 6.0999999 2.9000001 4.69999981 1.39999998]
[ 6.5 2.79999995 4.5999999 1.5 ]
[ 5.5999999 2.70000005 4.19999981 1.29999995]
[ 6.30000019 3.4000001 5.5999999 2.4000001 ]
[ 4.9000001 3.0999999 1.5 0.1 ]
[ 6.80000019 2.79999995 4.80000019 1.39999998]
[ 5.69999981 2.79999995 4.5 1.29999995]
[ 6. 2.70000005 5.0999999 1.60000002]
[ 5. 3.5 1.29999995 0.30000001]
[ 6.5 3. 5.19999981 2. ]
[ 6.0999999 2.79999995 4.69999981 1.20000005]
[ 5.0999999 3.5 1.39999998 0.30000001]
[ 4.5999999 3.0999999 1.5 0.2 ]
[ 6.5 3. 5.80000019 2.20000005]
[ 4.5999999 3.4000001 1.39999998 0.30000001]
[ 4.5999999 3.20000005 1.39999998 0.2 ]
[ 7.69999981 2.79999995 6.69999981 2. ]
[ 5.9000001 3.20000005 4.80000019 1.79999995]
[ 5.0999999 3.79999995 1.60000002 0.2 ]
[ 4.9000001 3. 1.39999998 0.2 ]
[ 4.9000001 2.4000001 3.29999995 1. ]
[ 4.5 2.29999995 1.29999995 0.30000001]
[ 5.80000019 2.70000005 4.0999999 1. ]
[ 5. 3.4000001 1.60000002 0.40000001]
[ 5.19999981 3.4000001 1.39999998 0.2 ]
[ 5.30000019 3.70000005 1.5 0.2 ]
[ 5. 3.5999999 1.39999998 0.2 ]
[ 5.5999999 2.9000001 3.5999999 1.29999995]
[ 4.80000019 3.0999999 1.60000002 0.2 ]
[ 6.30000019 2.70000005 4.9000001 1.79999995]
[ 5.69999981 2.79999995 4.0999999 1.29999995]
[ 5. 3. 1.60000002 0.2 ]
[ 6.30000019 3.29999995 6. 2.5 ]
[ 5. 3.5 1.60000002 0.60000002]
[ 5.5 2.5999999 4.4000001 1.20000005]
[ 5.69999981 3. 4.19999981 1.20000005]
[ 4.4000001 2.9000001 1.39999998 0.2 ]
[ 4.80000019 3. 1.39999998 0.1 ]
[ 5.5 2.4000001 3.70000005 1. ]]
[2 1 2 0 0 0 0 2 1 0 1 1 0 0 2 1 2 2 2 0 2 2 0 2 2 0 1 2 1 1 1 1 1 2 2 2 2
2 0 0 2 2 2 0 0 2 0 2 0 2 0 1 1 0 1 2 2 2 2 1 1 2 2 2 1 2 0 2 2 0 0 1 0 2 2 0 1 1 1 2 0 1 1 1 2 0 1 1 1 0 2 1 0 0 2 0 0 2 1 0 0 1 0 1 0 0 0 0 1 0 2 1 0 2 0 1 1 0 0 1]
(第一个list是4个特征值，第二个list是目标结果，即鸢尾的种类，用int的0、1、2表示Iris Setosa(山鸢尾)、Iris Versicolour(变色鸢尾)和Iris Virginica(维吉尼亚鸢尾)。)In[3]
#构建模型
#假定所有的特征都有一个实数值作为数据
feature_name = "flower_features"
feature_columns = [tf.feature_column.numeric_column(feature_name, shape = [4])]
classifier = tf.estimator.LinearClassifier(
feature_columns = feature_columns,
n_classes = 3,
model_dir = "/tmp/iris_model")
INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {
'_model_dir': '/tmp/iris_model', '_tf_random_seed': 1, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_save_checkpoints_steps': None, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100}
In[4]
# define input function 定义一个输入函数，用于为模型产生数据
def input_fn(dataset):
def _fn():
features = {feature_name: tf.constant(dataset.data)}
label = tf.constant(dataset.target)
return features, label
return _fn
print(input_fn(training_set)())
({
'flower_features': 'Const:0' shape=(120, 4) dtype=float32>}, 'Const_1:0' shape=(120,) dtype=int64>) In[5]
# 数据流向
# raw data -> input_fn -> feature columns -> model
# fit model 训练模型
classifier.train(input_fn = input_fn(training_set), steps = 1000)
print('fit already done.')
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Saving checkpoints for 1 into /tmp/iris_model/model.ckpt.
INFO:tensorflow:loss = 131.833, step = 1
INFO:tensorflow:global_step/sec: 1396.3
INFO:tensorflow:loss = 37.1391, step = 101 (0.072 sec)
INFO:tensorflow:global_step/sec: 1279.85
INFO:tensorflow:loss = 27.8594, step = 201 (0.078 sec)
INFO:tensorflow:global_step/sec: 1400.15
INFO:tensorflow:loss = 23.0449, step = 301 (0.071 sec)
INFO:tensorflow:global_step/sec: 1293.92
INFO:tensorflow:loss = 20.058, step = 401 (0.077 sec)
INFO:tensorflow:global_step/sec: 1610.43
INFO:tensorflow:loss = 18.0083, step = 501 (0.062 sec)
INFO:tensorflow:global_step/sec: 1617.19
INFO:tensorflow:loss = 16.505, step = 601 (0.062 sec)
INFO:tensorflow:global_step/sec: 1602.84
INFO:tensorflow:loss = 15.3496, step = 701 (0.062 sec)
INFO:tensorflow:global_step/sec: 1799.5
INFO:tensorflow:loss = 14.43, step = 801 (0.056 sec)
INFO:tensorflow:global_step/sec: 1577.18
INFO:tensorflow:loss = 13.6782, step = 901 (0.063 sec)
INFO:tensorflow:Saving checkpoints for 1000 into /tmp/iris_model/model.ckpt.
INFO:tensorflow:Loss for final step: 13.0562.
fit already done.In[6]
# Evaluate accuracy 评估模型的准确度
accuracy_score = classifier.evaluate(input_fn = input_fn(test_set),
steps = 100)["accuracy"]
print('\nAccuracy: {0:f}'.format(accuracy_score))
INFO:tensorflow:Starting evaluation at 2018-03-03-12:07:04
INFO:tensorflow:Restoring parameters from /tmp/iris_model/model.ckpt-1000
INFO:tensorflow:Evaluation [1/100]
INFO:tensorflow:Evaluation [2/100]
INFO:tensorflow:Evaluation [3/100]
INFO:tensorflow:Evaluation [4/100]
INFO:tensorflow:Evaluation [5/100]
INFO:tensorflow:Evaluation [6/100]
INFO:tensorflow:Evaluation [7/100]
INFO:tensorflow:Evaluation [8/100]
……
INFO:tensorflow:Evaluation [98/100]
INFO:tensorflow:Evaluation [99/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2018-03-03-12:07:05
INFO:tensorflow:Saving dict for global step 1000: accuracy = 0.966667, average_loss = 0.120964, global_step = 1000, loss = 3.62893
Accuracy: 0.966667总结与说明
本例主要使用了TensorFlow封装的高级API，即Estimator。Estimator已经对训练过程进行了封装，因此我们只需要配置就可以进行使用。
classifier = tf.estimator.LinearClassifier(
feature_columns = feature_columns,
n_classes = 3,
model_dir = "/tmp/iris_model")这是构建模型所使用的代码，它定义了一个简单的线性模型，并配置了三个参数：feature_columns即特征值，已在前面定义；n_class即分类的总数，本例为3；model_dir即模型的存储路径。
本例所搭建的线性模型的最终准确度达到了96.66667%。这是一个不错的数值，因为这意味着从统计方面来说该模型能从100朵鸢尾中正确区分96朵鸢尾的品种。事实上，如果让一个真实的人来对100朵鸢尾做出品种的区分，他也有可能区分错其中4朵甚至更多。当然这并不意味着我们对此感到满足，因为这是一个示例的简单模型，我们应当追求实际应用模型的准确率超过99%！
以上过程也给出了我们机器学习模型搭建的基本步骤，即:
本例参考自Plain and Simple Estimators - YouTube，中文字幕以及详细解释参考机器学习 | 更进一步，用评估器给花卉分类，本文着重于其具体实现部分，给代码加了比较详细的注释。