Code
所属分类:其他
开发工具:Python
文件大小:23490KB
下载次数:1
上传日期:2020-06-14 11:16:54
上 传 者:
lakii
说明: tensorflow学习代码,包括mnist数据集的训练与识别,验证码的生成与训练识别,适用于初学者
(tensorflow learning code)
文件列表:
Code (0, 2020-05-04)
Code\week01-src (0, 2020-05-04)
Code\week01-src\01-Tensorflow简介,Anaconda安装,Tensorflow的CPU版本安装.md (3488, 2019-03-10)
Code\week02-src (0, 2020-05-04)
Code\week02-src\02-Tensorflow的基础使用,包括对图(graphs),会话(session),张量(tensor),变量(Variable)的一些解释和操作.md (8203, 2019-03-10)
Code\week03-src (0, 2020-05-04)
Code\week03-src\3-1非线性回归.ipynb (33736, 2019-03-10)
Code\week03-src\3-1非线性回归.py (1379, 2019-03-10)
Code\week03-src\3-2MNIST数据集分类简单版本.ipynb (3904, 2019-03-10)
Code\week03-src\3-2MNIST数据集分类简单版本.py (1470, 2019-03-10)
Code\week03-src\MNIST_data (0, 2020-05-04)
Code\week03-src\MNIST_data\t10k-images-idx3-ubyte.gz (1648877, 2019-03-10)
Code\week03-src\MNIST_data\t10k-labels-idx1-ubyte.gz (4542, 2019-03-10)
Code\week03-src\MNIST_data\train-images-idx3-ubyte.gz (9912422, 2019-03-10)
Code\week03-src\MNIST_data\train-labels-idx1-ubyte.gz (28881, 2019-03-10)
Code\week04-src (0, 2020-05-04)
Code\week04-src\4-1交叉熵.ipynb (2517, 2019-03-10)
Code\week04-src\4-1交叉熵.py (1539, 2019-03-10)
Code\week04-src\4-2Dropout.ipynb (6230, 2019-03-10)
Code\week04-src\4-2Dropout.py (2470, 2019-03-10)
Code\week04-src\4-3优化器.ipynb (4073, 2019-03-10)
Code\week04-src\4-3优化器.py (1622, 2019-03-10)
Code\week05-src (0, 2020-05-04)
Code\week05-src\5-1第四周作业.ipynb (7357, 2019-03-10)
Code\week05-src\5-1第四周作业.py (2116, 2019-03-10)
Code\week05-src\5-2tensorboard网络结构.ipynb (3878, 2019-03-10)
Code\week05-src\5-2tensorboard网络结构.py (2173, 2019-03-10)
Code\week05-src\5-3tensorboard网络运行.ipynb (7373, 2019-03-10)
Code\week05-src\5-3tensorboard网络运行.py (3293, 2019-03-10)
Code\week05-src\5-4tensorboard可视化.ipynb (7082, 2019-03-10)
Code\week05-src\5-4tensorboard可视化.py (4389, 2019-03-10)
Code\week05-src\mnist_10k_sprite.png (3514847, 2019-03-10)
Code\week06-src (0, 2020-05-04)
Code\week06-src\6-1卷积神经网络应用于MNIST数据集分类.ipynb (5717, 2019-03-10)
Code\week06-src\6-1卷积神经网络应用于MNIST数据集分类.py (4198, 2019-03-10)
Code\week07-src (0, 2020-05-04)
Code\week07-src\7-1第六周作业.ipynb (10604, 2019-03-10)
Code\week07-src\7-1第六周作业.py (7138, 2019-03-10)
Code\week07-src\7-2递归神经网络RNN.ipynb (4314, 2019-03-10)
Code\week07-src\7-2递归神经网络RNN.py (2349, 2019-03-10)
... ...
**[This code belongs to the "Implementing a CNN for Text Classification in Tensorflow" blog post.](http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/)**
It is slightly simplified implementation of Kim's [Convolutional Neural Networks for Sentence Classification](http://arxiv.org/abs/1408.5882) paper in Tensorflow.
## Requirements
- Python 3
- Tensorflow > 0.12
- Numpy
## Training
Print parameters:
```bash
./train.py --help
```
```
optional arguments:
-h, --help show this help message and exit
--embedding_dim EMBEDDING_DIM
Dimensionality of character embedding (default: 128)
--filter_sizes FILTER_SIZES
Comma-separated filter sizes (default: '3,4,5')
--num_filters NUM_FILTERS
Number of filters per filter size (default: 128)
--l2_reg_lambda L2_REG_LAMBDA
L2 regularizaion lambda (default: 0.0)
--dropout_keep_prob DROPOUT_KEEP_PROB
Dropout keep probability (default: 0.5)
--batch_size BATCH_SIZE
Batch Size (default: ***)
--num_epochs NUM_EPOCHS
Number of training epochs (default: 100)
--evaluate_every EVALUATE_EVERY
Evaluate model on dev set after this many steps
(default: 100)
--checkpoint_every CHECKPOINT_EVERY
Save model after this many steps (default: 100)
--allow_soft_placement ALLOW_SOFT_PLACEMENT
Allow device soft device placement
--noallow_soft_placement
--log_device_placement LOG_DEVICE_PLACEMENT
Log placement of ops on devices
--nolog_device_placement
```
Train:
```bash
./train.py
```
## Evaluating
```bash
./eval.py --eval_train --checkpoint_dir="./runs/1459637919/checkpoints/"
```
Replace the checkpoint dir with the output from the training. To use your own data, change the `eval.py` script to load your data.
## References
- [Convolutional Neural Networks for Sentence Classification](http://arxiv.org/abs/1408.5882)
- [A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification](http://arxiv.org/abs/1510.03820)
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