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!