machine-learning
  • 機器學習:使用Python
    • 簡介Scikit-learn 機器學習
  • 分類法 Classification
    • Ex 1: Recognizing hand-written digits
    • EX 2: Normal and Shrinkage Linear Discriminant Analysis for classification
    • EX 3: Plot classification probability
    • EX 4: Classifier Comparison
    • EX 5: Linear and Quadratic Discriminant Analysis with confidence ellipsoid
  • 特徵選擇 Feature Selection
    • Ex 1: Pipeline Anova SVM
    • Ex 2: Recursive Feature Elimination
    • Ex 3: Recursive Feature Elimination with Cross-Validation
    • Ex 4: Feature Selection using SelectFromModel
    • Ex 5: Test with permutations the significance of a classification score
    • Ex 6: Univariate Feature Selection
    • Ex 7: Comparison of F-test and mutual information
  • 互分解 Cross Decomposition
  • 通用範例 General Examples
    • Ex 1: Plotting Cross-Validated Predictions
    • Ex 2: Concatenating multiple feature extraction methods
    • Ex 3: Isotonic Regression
    • Ex 4: Imputing missing values before building an estimator
    • Ex 5: ROC Curve with Visualization API
    • Ex 7: Face completion with a multi-output estimators
  • 群聚法 Clustering
    • EX 1: Feature_agglomeration.md
    • EX 2: Mean-shift 群聚法.md
    • EX 6: 以群聚法切割錢幣影像.md
    • EX 10:_K-means群聚法
    • EX 12: Spectral clustering for image segmentation
    • Plot Hierarchical Clustering Dendrogram
  • 支持向量機
    • EX 1:Non_linear_SVM.md
    • [EX 4: SVM_with _custom _kernel.md](SVM/EX4_SVM_with _custom _kernel.md)
  • 機器學習資料集 Datasets
    • Ex 1: The digits 手寫數字辨識
    • Ex 2: Plot randomly generated classification dataset 分類數據集
    • Ex 3: The iris 鳶尾花資料集
    • Ex 4: Plot randomly generated multilabel dataset 多標籤數據集
  • 應用範例 Application
    • 用特徵臉及SVM進行人臉辨識實例
    • 維基百科主要的特徵向量
    • 波士頓房地產雲端評估(一)
    • 波士頓房地產雲端評估(二)
  • 類神經網路 Neural_Networks
    • Ex 1: Visualization of MLP weights on MNIST
    • Ex 2: Restricted Boltzmann Machine features for digit classification
    • Ex 3: Compare Stochastic learning strategies for MLPClassifier
    • Ex 4: Varying regularization in Multi-layer Perceptron
  • 決策樹 Decision_trees
    • Ex 1: Decision Tree Regression
    • Ex 2: Multi-output Decision Tree Regression
    • Ex 3: Plot the decision surface of a decision tree on the iris dataset
    • Ex 4: Understanding the decision tree structure
  • 機器學習:使用 NVIDIA JetsonTX2
    • 從零開始
    • 讓 TX2 動起來
    • 安裝OpenCV
    • 安裝TensorFlow
  • 廣義線性模型 Generalized Linear Models
    • Ex 3: SGD: Maximum margin separating hyperplane
  • 模型選擇 Model Selection
    • Ex 3: Plotting Validation Curves
    • Ex 4: Underfitting vs. Overfitting
  • 半監督式分類法 Semi-Supervised Classification
    • Ex 3: Label Propagation digits: Demonstrating performance
    • Ex 4: Label Propagation digits active learning
    • Decision boundary of label propagation versus SVM on the Iris dataset
  • Ensemble_methods
    • IsolationForest example
  • Miscellaneous_examples
    • Multilabel classification
  • Nearest_Neighbors
    • Nearest Neighbors Classification
Powered by GitBook
On this page
  1. 機器學習:使用 NVIDIA JetsonTX2

安裝TensorFlow

  1. 安裝依賴套件

     $ sudo apt-get install default-jdk libcupti-dev
     $ export JAVA_HOME='/usr/lib/jvm/java-8-openjdk-arm64/'
  2. 取得 TensorFlow 編譯腳本

     $ git clone git://github.com/jetsonhacks/installTensorFlowTX2
     $ cd installTensorFlowTX2
  3. 執行編譯腳本

     $ ./installPrerequisitesPy3.sh
     $ ./cloneTensorFlow.sh
     $ ./setTensorFlowEVPy3.sh
     $ ./buildTensorFlow.sh
     $ ./packageTensorFlow.sh
  4. 安裝 TensorFlow

    1. 建立虛擬開發環境

       $ virtualenv TensorFlow
    2. 進入虛擬開發環境

       $ cd TensorFlow
       $ source bin/active
    3. 安裝 TensorFlow 到虛擬環境

       pip3 install $HOME/<TensorFlow 的 .whl 安裝封包>
    4. 測試 TensorFlow

      1. Hello World

         import tensorflow as tf
         hello = tf.constant('Hello, TensorFlow on NVIDIA Jetson TX2!')
         sess = tf.Session()
         print(sess.run(hello))

        輸出

         Hello, TensorFlow on NVIDIA Jetson TX2!
      2. 運算單元

         import tensorflow as tf
         # Creates a graph.
         a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
         b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
         c = tf.matmul(a, b)
         # Creates a session with log_device_placement set to True.
         sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
         # Runs the op.
         print(sess.run(c))

        輸出

         name: NVIDIA Tegra X2
         major: 6 minor: 2 memoryClockRate (GHz) 1.3005
         MatMul: (MatMul): /job:localhost/replica:0/task:0/gpu:0
         b: (Const): /job:localhost/replica:0/task:0/gpu:0
         a: (Const): /job:localhost/replica:0/task:0/gpu:0
         [[ 22.  28.]
         [ 49.  64.]]
Previous安裝OpenCVNext廣義線性模型 Generalized Linear Models

Last updated 6 years ago