mnist-master.zip

# mnist Some samples of the MNIST classifier which are corresponding to the tutorials of the jikexueyuan(http://wiki.jikexueyuan.com/project/tensorflow-zh/). **构建模块：** - `input_data.py`: MNIST数据集下载与解压 - `mnist.py`: Implements the inference/loss/training pattern for model building **代码测试：** - `mnist_softmax.py`: MNIST机器学习入门 - `mnist_deep.py`: 深入MNIST - `fully_connected_feed.py`: TensorFlow运作方式入门 - `mnist_with_summaries.py`: Tensorboard训练过程可视化 将MNIST数据集，下载后拷贝到文件夹`Mnist_data`中，如果已经配置好`tensorflow`环境，主要的四个测试代码文件，都可以直接编译运行： - `mnist_softmax.py`: MNIST机器学习入门 - `mnist_deep.py`: 深入MNIST - `fully_connected_feed.py`: TensorFlow运作方式入门 - `mnist_with_summaries.py`: Tensorboard训练过程可视化 **`mnist_softmax.py`**运行结果比较简单，就不列举。 **`mnist_deep.py`**迭代运行较为耗时，结果已显示在博客： [深入MNIST code测试](http://blog.csdn.net/yhl_leo/article/details/50624471) 。 **`fully_connected_feed.py`**的运行结果如下（本人电脑为2 CPU，没有使用GPU）： ``` Extracting Mnist_data/train-images-idx3-ubyte.gz Extracting Mnist_data/train-labels-idx1-ubyte.gz Extracting Mnist_data/t10k-images-idx3-ubyte.gz Extracting Mnist_data/t10k-labels-idx1-ubyte.gz I tensorflow/core/common_runtime/local_device.cc:25] Local device intra op parallelism threads: 2 I tensorflow/core/common_runtime/local_session.cc:45] Local session inter op parallelism threads: 2 Step 0: loss = 2.33 (0.023 sec) Step 100: loss = 2.09 (0.007 sec) Step 200: loss = 1.76 (0.009 sec) Step 300: loss = 1.36 (0.007 sec) Step 400: loss = 1.12 (0.007 sec) Step 500: loss = 0.74 (0.008 sec) Step 600: loss = 0.78 (0.006 sec) Step 700: loss = 0.69 (0.007 sec) Step 800: loss = 0.67 (0.007 sec) Step 900: loss = 0.52 (0.010 sec) Training Data Eval: Num examples: 55000 Num correct: 47532 Precision @ 1: 0.8642 Validation Data Eval: Num examples: 5000 Num correct: 4360 Precision @ 1: 0.8720 Test Data Eval: Num examples: 10000 Num correct: 8705 Precision @ 1: 0.8705 Step 1000: loss = 0.56 (0.013 sec) Step 1100: loss = 0.50 (0.145 sec) Step 1200: loss = 0.33 (0.007 sec) Step 1300: loss = 0.44 (0.006 sec) Step 1400: loss = 0.39 (0.006 sec) Step 1500: loss = 0.33 (0.009 sec) Step 1600: loss = 0.56 (0.008 sec) Step 1700: loss = 0.50 (0.007 sec) Step 1800: loss = 0.42 (0.006 sec) Step 1900: loss = 0.41 (0.006 sec) Training Data Eval: Num examples: 55000 Num correct: 49220 Precision @ 1: 0.8949 Validation Data Eval: Num examples: 5000 Num correct: 4520 Precision @ 1: 0.9040 Test Data Eval: Num examples: 10000 Num correct: 9014 Precision @ 1: 0.9014 [Finished in 22.8s] ``` **`mnist_with_summaries.py`**主要提供了一种在Tensorboard可视化方法，首先，编译运行代码：  运行完毕后，打开终端`Terminal`，输入`tensorboard --logdir=/tmp/mnist_logs`，就会运行出：`Starting TensorBoard on port 6006 (You can navigate to http://localhost:6006)` 然后，打开浏览器，输入链接`http://localhost:6006`：  其中，有一些选项，例如菜单栏里包括`EVENTS, IMAGES, GRAPH, HISTOGRAMS`，都可以一一点开查看~ 另外，此时如果不关闭该终端，是无法在其他终端中重新生成可视化结果的，会出现端口占用的错误，更多详细信息可以查看英文原文：[TensorBoard: Visualizing Learning](https://www.tensorflow.org/versions/master/how_tos/summaries_and_tensorboard/index.html)。 源自博客： [Tensorflow MNIST 数据集测试代码入门](http://http://blog.csdn.net/YhL_Leo/article/details/50614444) 如有纰漏，欢迎指正！