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Tensorflow 框架检索

data文件夹保存了一些基本数据

1 基本操作 basictensor_handson文件夹

1.1 basic_operations.py

  • 包含一些基本的tensorflow操作

1.2 logistic_regression_handon.py

  • 一个简单的tensorflow代码

1.3 logistic_fancy.py

  • 在基础上加上了额外的features
  • 提供了一个随机batch的方法
  • 加上了name_scope
  • 加上了tensorboard
  • 加上了check_point

1.4 search_parameters.py

  • 提供了一个自动随机搜索参数的方法

2 wordembeddings_handson文件夹

2.1 jieba(to do !!!!!!)

  • train.conll 是文本预处理后

2.2 word2vec_handson

  • process_data.py 预处理数据,后续移动到结巴目录,并将结巴目录改为数据预处理目录(to do !!!!!!!!!)
  • build_word2vec.py

2.3 glove_handson

  • build_glove.py 明磊贡献的glove生成方法(to do !!!!!!!!!!)

3 如何训练一个小型的神经网络small_net_work_handson文件夹

3.1 dnn_plain_handson.py

  • 一个基础的多层神经网络

3.2 dnn_fancy.py

  • 在基础上加入name_scope
  • tensorbaord
  • check_point
  • restore
  • 保存最优模型的方法
  • early_stop
  • 中断重启机制

4 一个深度神经网络 deep_beural_nets_handson文件夹

4.1 basic_operation.py

  • 几个激活函数的图
  • batch_normlization
  • 梯度剪裁的方法

4.2 resuing_pretrained_layers.py

  • 使用已经训练好的模型

4.3 resuing_other_frameworks.py

  • 使用其它框架生成模型的参数

4.4 learning_rate_decay_handson.py

  • 学习率衰减,注意AdaGrad RMSProp Adam 不需要learning_rate_decay

4.5 regularization_and_dropout.py

  • 如何进行正则化和drop_out

4.6 max_norm_regularization_handson.py

  • 如何将权重的范数限定在一定范围内

4.7 transfer_learning文件夹

  • origin_learning_handson.py
    • 一个简单的6层神经网络,为了输出一个保存好的模型
  • transfer_learning_handson.py
    • 使用之前训练好的layers,加上新定义的层,去做transfer_learning
  • transfer_learning_and_freeze.py
    • 提供了多种方法去transfer_learning,可以选择是否freeze掉已训练的隐层
  • catch_frozen_layers.py
    • 提供一个方法,可以将之前隐层的结果缓存下来,提高速度

4.8 exercise文件夹

deep_learning文件夹

  • exercise8.1

transfer_learning_exercise文件夹

  • 重用8.1
  • freeze掉前面几层
  • 缓存freeze的层,速度确实快了

pretraining_on_auxiliary_task文件夹

basemodel.py

  • 训练一个模型,有两个dnn,并输入两个图片,比较两个图片是否为一个手写体

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