File tree

• .gitignore
• Deep_Learning_with_TensorFlow
  • 1.4.0
    • Chapter03
      • .ipynb_checkpoints
        • 1. 图，张量及会话-checkpoint.ipynb
        • 2. 三层简单神经网络的前向传播算法-checkpoint.ipynb
        • 3. 完整神经网络样例程序-checkpoint.ipynb
      • 1. 图，张量及会话.ipynb
      • 2. 三层简单神经网络的前向传播算法.ipynb
      • 3. 完整神经网络样例程序.ipynb
    • Chapter04
      • .ipynb_checkpoints
        • 1. 自定义损失函数-checkpoint.ipynb
        • 2. 学习率的设置-checkpoint.ipynb
        • 3. 正则化-checkpoint.ipynb
        • 4. 滑动平均模型-checkpoint.ipynb
      • 1. 自定义损失函数.ipynb
      • 2. 学习率的设置.ipynb
      • 3. 正则化.ipynb
      • 4. 滑动平均模型.ipynb
    • Chapter05
      • .ipynb_checkpoints
        • 1. MNIST读取数据-checkpoint.ipynb
        • 2. TensorFlow训练神经网络-checkpoint.ipynb
        • 3. 变量管理-checkpoint.ipynb
        • 4.1. ckpt文件保存方法-checkpoint.ipynb
        • 4.2.1 滑动平均类的保存-checkpoint.ipynb
        • 4.2.2 variables_to_restore函数的使用样例-checkpoint.ipynb
        • 4.3. pb文件保存方法-checkpoint.ipynb
      • 1. MNIST读取数据.ipynb
      • 2. TensorFlow训练神经网络
        • .ipynb_checkpoints
          • 1. 全模型-checkpoint.ipynb
          • 2. 不使用正则化-checkpoint.ipynb
          • 3. 不使用指数衰减的学习率-checkpoint.ipynb
          • 4. 不使用激活函数-checkpoint.ipynb
          • 5. 不使用隐藏层-checkpoint.ipynb
          • 6. 不使用滑动平均-checkpoint.ipynb
        • 1. 全模型.ipynb
        • 2. 不使用正则化.ipynb
        • 3. 不使用指数衰减的学习率.ipynb
        • 4. 不使用激活函数.ipynb
        • 5. 不使用隐藏层.ipynb
        • 6. 不使用滑动平均.ipynb
      • 3. 变量管理.ipynb
      • 4.1. ckpt文件保存方法.ipynb
      • 4.2.1 滑动平均类的保存.ipynb
      • 4.2.2 variables_to_restore函数的使用样例.ipynb
      • 4.3. pb文件保存方法.ipynb
      • 5. MNIST最佳实践
        • ..\..\Data_sets\MNIST_data
          • train-images-idx3-ubyte.gz
          • train-labels-idx1-ubyte.gz
        • .ipynb_checkpoints
          • mnist_eval-checkpoint.ipynb
          • mnist_inference-checkpoint.ipynb
          • mnist_train-checkpoint.ipynb
        • mnist_eval.ipynb
        • mnist_inference.ipynb
        • mnist_inference.py
        • mnist_train.ipynb
        • mnist_train.py
    • Chapter06
      • .ipynb_checkpoints
        • 1. 卷积层、池化层样例-checkpoint.ipynb
        • 2. 迁移学习-checkpoint.ipynb
        • 2.1 data_process-checkpoint.ipynb
        • 2.2 fine_tuning-checkpoint.ipynb
        • 3.1 data_process-checkpoint.ipynb
        • 3.2 fine_tuning-checkpoint.ipynb
        • Untitled-checkpoint.ipynb
        • data_process-checkpoint.ipynb
        • fine_tuning-checkpoint.ipynb
        • load_data-checkpoint.ipynb
      • 1. 卷积层、池化层样例.ipynb
      • 2.1 data_process.ipynb
      • 2.2 fine_tuning.ipynb
      • LeNet-5
        • .ipynb_checkpoints
          • LeNet5_infernece-checkpoint.ipynb
          • LeNet5_train-checkpoint.ipynb
        • LeNet5_infernece.ipynb
        • LeNet5_infernece.py
        • LeNet5_train.ipynb
    • Chapter07
      • .ipynb_checkpoints
        • 1. TFRecord样例程序-checkpoint.ipynb
        • 2.1. TensorFlow图像处理函数-checkpoint.ipynb
        • 2.2. 图像预处理完整样例-checkpoint.ipynb
        • 3. 队列操作-checkpoint.ipynb
        • 4. 多线程队列操作-checkpoint.ipynb
        • 5. 输入文件队列-checkpoint.ipynb
        • 6. 输入数据处理框架-checkpoint.ipynb
      • 1. TFRecord样例程序.ipynb
      • 2.1. TensorFlow图像处理函数.ipynb
      • 2.2. 图像预处理完整样例.ipynb
      • 3. 队列操作.ipynb
      • 4. 多线程队列操作.ipynb
      • 5. 输入文件队列.ipynb
      • 6. 输入数据处理框架.ipynb
    • Chapter08
      • .ipynb_checkpoints
        • 1. 循环神经网络前向传播-checkpoint.ipynb
        • 2. PTB数据集介绍-checkpoint.ipynb
        • 3. 使用循环神经网络实现语言模型-checkpoint.ipynb
        • 4. SKlearn封装例子-checkpoint.ipynb
        • 5. 预测正弦函数-checkpoint.ipynb
      • 1. 循环神经网络前向传播.ipynb
    • Chapter10
      • .ipynb_checkpoints
        • 1. TensorFlow-Slim-checkpoint.ipynb
        • 10. Estimator-Dataset-checkpoint.ipynb
        • 2. TFLearn-checkpoint.ipynb
        • 3. Keras-CNN-checkpoint.ipynb
        • 4. Keras-RNN-checkpoint.ipynb
        • 5. Keras-返回值-checkpoint.ipynb
        • 6. Keras-多输入输出-checkpoint.ipynb
        • 7. Keras-TensorFlow API-checkpoint.ipynb
        • 8. Estimator-DNNClassifier-checkpoint.ipynb
        • 9. Estimator-自定义模型-checkpoint.ipynb
      • 1. TensorFlow-Slim.ipynb
      • 10. Estimator-Dataset.ipynb
      • 2. TFLearn.ipynb
      • 3. Keras-CNN.ipynb
      • 4. Keras-RNN.ipynb
      • 5. Keras-返回值.ipynb
      • 6. Keras-多输入输出.ipynb
      • 7. Keras-TensorFlow API.ipynb
      • 8. Estimator-DNNClassifier.ipynb
      • 9. Estimator-自定义模型.ipynb
    • Chapter11
      • .ipynb_checkpoints
        • 1. 命名空间-checkpoint.ipynb
        • 2. 改造后的mnist_train-checkpoint.ipynb
        • 3. 监控指标可视化-checkpoint.ipynb
        • MNIST_projector-checkpoint.ipynb
        • data_prepare-checkpoint.ipynb
      • 1. 命名空间.ipynb
      • 2. 改造后的mnist_train.ipynb
      • 3. 监控指标可视化.ipynb
      • 4.1. projector_data_prepare.ipynb
      • 4.2. projector_MNIST.ipynb
      • mnist_inference.py
    • Chapter12
      • 1. GPU基本操作.py
      • 2. 多GPU并行.py
      • 3. 分布式TensorFlow.py
      • 4. 异步更新模式样例程序.py
      • 5. 同步更新模式样例程序.py
      • mnist_inference.py
  • datasets
    • iris_test.csv
    • iris_training.csv