mnist
Some samples of the MNIST classifier which are corresponding to the tutorials of the jikexueyuan(https://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MNISTfully_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MNISTfully_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 。
**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 https://localhost:6006)
然后，打开浏览器，输入链接https://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。
源自博客： Tensorflow MNIST 数据集测试代码入门
如有纰漏，欢迎指正！