文章主要介绍Tensorflow编程环境，并通过鸢尾花分类问题简单介绍如何使用Tensorflow高级API解决实际问题。
首先，可以通过下图了解Tensorflow编程环境，Tensorflow提供一个包含有多个API层的编程环境：
tensorflow_programming_environment
0 使用高级API Estimator
Estimator是Tensorflow对完整模型的高级表示，所有的Estimator类都是继承自Tensorflow的tf.estimator.Estimator类。Tensorflow提供了一组预创建好的Estimator(例如DNNClassifier、DNNRegressor和LinearClassifier等)，除了这些预定义的Estimator，还可以自定义Estimator(具体方法后续记录)。本文主要记录如何使用预定义的Estimator。根据预定义的Estimator编写Tensorflow程序，必须按照如下步骤来进行：
- 创建一个或多个输入函数
- 定义模型的特征列
- 实例化Estimator，同时指定特征列和各种超参数
- 在Estimator对象上调用一个或多个方法，传递适当的输入函数并未数据的来源。
接下来按照如上步骤来完成鸢尾花的分类问题，源码在最后给出。
1. 创建输入函数
在对模型进行训练、评估和预测的时候需要一个输入函数作为数据的来源。
输入函数返回tf.data.Dataset对象，该对象会输出下列含有两个元素的元组：
-
featurepython字典- key为特征的名称
- value为所有样本在当前特征下的取值的数组
-
label包含有所有样本的标签值的数组
可以使用如下简单方式来实现输入函数：
def input_evaluation_set():
features = {'SepalLength': np.array([6.4, 5.0]),
'SepalWidth': np.array([2.8, 2.3]),
'PetalLength': np.array([5.6, 3.3]),
'PetalWidth': np.array([2.2, 1.0])}
labels = np.array([2, 1])
return features, labels
上述方式虽然可以作为模型的输入函数，但是这里强烈建议使用Tensorflow中的Dataset API来实现，如下所示：
def train_input_fn(features, labels, batch_size):
"""An input function for training"""
# Convert the inputs to a Dataset.
dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
# Shuffle, repeat, and batch the examples.
return dataset.shuffle(1000).repeat().batch(batch_size)
为了简化数据处理过程，一般情况函数参数features和labels都是Pandas数据。
2. 定义feature columns
feature columns用于说明模型应该如何使用特征字典中的原始输入数据。在构建Estimator时，需要向其传递一个feature columns的列表，其中包含有模型使用的所有特征。feature columns列表中每一个特征都是一个tf.feature_column对象。对于鸢尾花分类问题，4个特征都是数值型的，按照如下构建即可：
my_feature_columns = []
for key in train_x.keys():
my_feature_columns.append(tf.feature_column.numerici_column(key=key))
3. 实例化Estimator
鸢尾花分类是典型的分类问题。Tensorflow提供了几个预创建的分类器Esitimator，如下所示：
-
tf.estimator.DNNClassifier：适用于执行多类别分类的深度神经网络模型 -
tf.estimator.DNNLinearCombinedClassifier： wide and deep分类模型 -
tf.estimator.LinearClassifier：基于线性模型的分类器
这里直接使用tf.estimator.DNNClassifier即可，代码如下所示：
classifier = tf.estimator.DNNClassifier(
feature_columns=my_feature_columns,
# Two hidden layers of 10 nodes each.
hidden_units=[10, 10],
# The model must choose between 3 classes.
n_classes=3)
4. 训练、评估和预测
4.1 训练模型
通过调用Estimator的train方法即可以对模型进行训练，如下所示：
classifier.train(
input_fn=lambda:iris_data.train_input_fn(train_x, train_y, args.batch_size),
steps=args.train_steps)
4.2 评估训练的模型
模型经过训练之后，我们可以对模型的效果进行评估统计，可以通过如下代码对模型的准确率进行计算：
eval_result = classifier.evaluate(
input_fn=lambda:iris_data.eval_input_fn(test_x, test_y, args.batch_size))
print "\nTest set accuracy: {accuracy:0.3f}\n".format(**eval_result)
执行之后产出如下输出：
Test set accuracy: 0.967
4.3 预测
训练好模型之后，可以使用模型对无标签的样本进行预测，代码如下所示：
expected = ['Setosa', 'Versicolor', 'Virginica']
predict_x = {
'SepalLength': [5.1, 5.9, 6.9],
'SepalWidth': [3.3, 3.0, 3.1],
'PetalLength': [1.7, 4.2, 5.4],
'PetalWidth': [0.5, 1.5, 2.1],
}
predictions = classifier.predict(
input_fn=lambda:iris_data.eval_input_fn(
predict_x,
batch_size=args.batch_size
)
)
predict方法返回一个python可以迭代的对象，为每个样本生成一个预测结果字典，可以通过如下代码输出预测结果以及对应的概率:
for pred_dict, expec in zip(predictions, expected):
template = ('\nPrediction is "{}" ({:.1f}%), expected "{}"')
class_id = pred_dict['class_ids'][0]
probability = pred_dict['probabilities'][class_id]
print(template.format(iris_data.SPECIES[class_id],
100 * probability, expec))
结果输出如下：
Prediction is "Setosa" (99.8%), expected "Setosa"
Prediction is "Versicolor" (99.7%), expected "Versicolor"
Prediction is "Virginica" (96.9%), expected "Virginica"
5 源码
#iris_data.py文件
import pandas as pd
import tensorflow as tf
TRAIN_URL = "http://download.tensorflow.org/data/iris_training.csv"
TEST_URL = "http://download.tensorflow.org/data/iris_test.csv"
CSV_COLUMN_NAMES = ['SepalLength', 'SepalWidth',
'PetalLength', 'PetalWidth', 'Species']
SPECIES = ['Setosa', 'Versicolor', 'Virginica']
def maybe_download():
train_path = tf.keras.utils.get_file(TRAIN_URL.split('/')[-1], TRAIN_URL)
test_path = tf.keras.utils.get_file(TEST_URL.split('/')[-1], TEST_URL)
return train_path, test_path
def load_data(y_name='Species'):
"""Returns the iris dataset as (train_x, train_y), (test_x, test_y)."""
train_path, test_path = maybe_download()
train = pd.read_csv(train_path, names=CSV_COLUMN_NAMES, header=0)
train_x, train_y = train, train.pop(y_name)
test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0)
test_x, test_y = test, test.pop(y_name)
return (train_x, train_y), (test_x, test_y)
def train_input_fn(features, labels, batch_size):
"""An input function for training"""
# Convert the inputs to a Dataset.
dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
# Shuffle, repeat, and batch the examples.
dataset = dataset.shuffle(1000).repeat().batch(batch_size)
# Return the dataset.
return dataset
def eval_input_fn(features, labels, batch_size):
"""An input function for evaluation or prediction"""
features=dict(features)
if labels is None:
# No labels, use only features.
inputs = features
else:
inputs = (features, labels)
# Convert the inputs to a Dataset.
dataset = tf.data.Dataset.from_tensor_slices(inputs)
# Batch the examples
assert batch_size is not None, "batch_size must not be None"
dataset = dataset.batch(batch_size)
# Return the dataset.
return dataset
# The remainder of this file contains a simple example of a csv parser,
# implemented using the `Dataset` class.
# `tf.parse_csv` sets the types of the outputs to match the examples given in
# the `record_defaults` argument.
CSV_TYPES = [[0.0], [0.0], [0.0], [0.0], [0]]
def _parse_line(line):
# Decode the line into its fields
fields = tf.decode_csv(line, record_defaults=CSV_TYPES)
# Pack the result into a dictionary
features = dict(zip(CSV_COLUMN_NAMES, fields))
# Separate the label from the features
label = features.pop('Species')
return features, label
def csv_input_fn(csv_path, batch_size):
# Create a dataset containing the text lines.
dataset = tf.data.TextLineDataset(csv_path).skip(1)
# Parse each line.
dataset = dataset.map(_parse_line)
# Shuffle, repeat, and batch the examples.
dataset = dataset.shuffle(1000).repeat().batch(batch_size)
# Return the dataset.
return dataset
#premade_estimator.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import tensorflow as tf
import iris_data
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', default=100, type=int, help='batch size')
parser.add_argument('--train_steps', default=1000, type=int,
help='number of training steps')
def main(argv):
args = parser.parse_args(argv[1:])
# Fetch the data
(train_x, train_y), (test_x, test_y) = iris_data.load_data()
# Feature columns describe how to use the input.
my_feature_columns = []
for key in train_x.keys():
my_feature_columns.append(tf.feature_column.numeric_column(key=key))
# Build 2 hidden layer DNN with 10, 10 units respectively.
classifier = tf.estimator.DNNClassifier(
feature_columns=my_feature_columns,
# Two hidden layers of 10 nodes each.
hidden_units=[10, 10],
# The model must choose between 3 classes.
n_classes=3)
# Train the Model.
classifier.train(
input_fn=lambda:iris_data.train_input_fn(train_x, train_y,
args.batch_size),
steps=args.train_steps)
# Evaluate the model.
eval_result = classifier.evaluate(
input_fn=lambda:iris_data.eval_input_fn(test_x, test_y,
args.batch_size))
print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result))
# Generate predictions from the model
expected = ['Setosa', 'Versicolor', 'Virginica']
predict_x = {
'SepalLength': [5.1, 5.9, 6.9],
'SepalWidth': [3.3, 3.0, 3.1],
'PetalLength': [1.7, 4.2, 5.4],
'PetalWidth': [0.5, 1.5, 2.1],
}
predictions = classifier.predict(
input_fn=lambda:iris_data.eval_input_fn(predict_x,
labels=None,
batch_size=args.batch_size))
template = ('\nPrediction is "{}" ({:.1f}%), expected "{}"')
for pred_dict, expec in zip(predictions, expected):
class_id = pred_dict['class_ids'][0]
probability = pred_dict['probabilities'][class_id]
print(template.format(iris_data.SPECIES[class_id],
100 * probability, expec))
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.INFO)
tf.app.run(main)
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