tf.contrib.learn Quickstart

TensorFlow的機器學(xué)習(xí)高級API(tf.contrib.learn)使配置、訓(xùn)練、評估不同的學(xué)習(xí)模型變得更加容易。在這個教程里，你將使用tf.contrib.learn在Iris data set上構(gòu)建一個神經(jīng)網(wǎng)絡(luò)分類器。代碼有一下5個步驟：

在TensorFlow數(shù)據(jù)集上加載Iris
構(gòu)建神經(jīng)網(wǎng)絡(luò)
用訓(xùn)練數(shù)據(jù)擬合
評估模型的準(zhǔn)確性
在新樣本上分類

Complete Neural Network Source Code

這里是神經(jīng)網(wǎng)絡(luò)的源代碼：

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行數(shù)據(jù)，3個種類：Iris setosa, Iris virginica, and Iris versicolor.

每一行包括了以下的數(shù)據(jù)：花萼的寬度，長度，花瓣的寬度，花的種類?；ǖ姆N類有整數(shù)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

這里，Iris數(shù)據(jù)隨機分割成了兩組不同的CSV文件：

120個樣本的訓(xùn)練數(shù)據(jù)(iris_training.csv)
30個樣本的測試數(shù)據(jù)(iris_test.csv).

開始時，首先引進所有必要的模塊，然后定義下載存儲數(shù)據(jù)集的路徑：

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，如果訓(xùn)練和測試集沒有在本地存儲，下載：

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()方法加載訓(xùn)練集和測試集成Dataset S，load_csv_with_header()包涵一下三個參數(shù)：

filename，CSV文件的路徑
target_dtype,數(shù)據(jù)集目標(biāo)值的numpy數(shù)據(jù)類型
features_dtype,數(shù)據(jù)集特征值的numpy數(shù)據(jù)類型

這里，目標(biāo)是花的種類，是0-2的整數(shù)，所以數(shù)據(jù)類型是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,你可以通過data，target來訪問特征值和目標(biāo)值，比如，training_set.data,training_set.target

Construct a Deep Neural Network Classifier

tf.contrib.learn提供了多種預(yù)定義的模型，稱為 Estimator S,你可以用“黑盒子”在你的數(shù)據(jù)上來訓(xùn)練和評估節(jié)點。這里，你講配置深度神經(jīng)網(wǎng)絡(luò)分類器來擬合Iris數(shù)據(jù)，你可以用tf.contrib.learn.DNNClassifier作為示例：

# 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")

首先定義特征所在的列，有4個特征，所以dimension設(shè)定為4.

然后，構(gòu)建了DNNClassifier，包含以下參數(shù)：

feature_columns=feature_columns.上面定義的特征的列
hidden_units=[10, 20, 10]. 三個隱層，分別包含10,20,10個神經(jīng)元
n_classes=3.三個目標(biāo)
model_dir=/tmp/iris_model.訓(xùn)練模型時保存的斷點數(shù)據(jù)

Describe the training input pipeline

tf.contrib.learn API使用輸入函數(shù)，創(chuàng)建TensorFlow節(jié)點來生成模型數(shù)據(jù)。這里，數(shù)據(jù)比較小，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分類器，你可以用fit方法來擬合數(shù)據(jù)，傳遞get_train_inputs到input_fn參數(shù)中，循環(huán)訓(xùn)練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)

如果你想追蹤訓(xùn)練模型，你可以用TensorFlow monitor來執(zhí)行節(jié)點的日志。
“Logging and Monitoring Basics with tf.contrib.learn”

Evaluate Model Accuracy

你已經(jīng)用訓(xùn)練數(shù)據(jù)擬合了模型，現(xiàn)在，你可以用evaluate方法在測試集上評估準(zhǔn)確性。像fit一樣，evaluate也需要一個輸入函數(shù)來構(gòu)建輸入的通道，并返回評估結(jié)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))

運行整個腳本，打印：

Test Accuracy: 0.966667

Classify New Samples

用predict()方法來分類新的樣本，比如，你有下面的兩個新樣本：

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方法返回一個generator，可以轉(zhuǎn)換成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))

結(jié)果大致如下：

New Samples, Class Predictions:    [1 2]

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TensorFlow入門（四）高級API用法示例

TensorFlow入門（四）高級API用法示例

tf.contrib.learn Quickstart

Complete Neural Network Source Code

Load the Iris CSV data to TensorFlow

Construct a Deep Neural Network Classifier

Describe the training input pipeline

Fit the DNNClassifier to the Iris Training Data

Evaluate Model Accuracy

Classify New Samples

相關(guān)閱讀更多精彩內(nèi)容

友情鏈接更多精彩內(nèi)容

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

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

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TensorFlow入門（四）高級API用法示例

tf.contrib.learn Quickstart

Complete Neural Network Source Code

Load the Iris CSV data to TensorFlow

Construct a Deep Neural Network Classifier

Describe the training input pipeline

Fit the DNNClassifier to the Iris Training Data

Evaluate Model Accuracy

Classify New Samples

相關(guān)閱讀更多精彩內(nèi)容

友情鏈接更多精彩內(nèi)容

色偷偷精品伊人,欧洲久久精品,欧美综合婷婷骚逼,国产AV主播,国产最新探花在线,九色在线视频一区,伊人大交九欧美,1769亚洲,黄色成人av

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