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https://github.com/sub-mod/tf-mnist
MNIST digit identification with Tensorflow and s2i
https://github.com/sub-mod/tf-mnist
openshift s2i tekton-pipelines tensorflow
Last synced: 15 days ago
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MNIST digit identification with Tensorflow and s2i
- Host: GitHub
- URL: https://github.com/sub-mod/tf-mnist
- Owner: sub-mod
- Created: 2019-10-10T19:16:25.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2023-03-24T21:54:11.000Z (over 1 year ago)
- Last Synced: 2023-09-15T13:26:57.210Z (about 1 year ago)
- Topics: openshift, s2i, tekton-pipelines, tensorflow
- Language: Python
- Homepage:
- Size: 59.6 KB
- Stars: 0
- Watchers: 3
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# tf-mnist
## MNIST in TensorFlow
This directory builds a convolutional neural net to classify the [MNIST
dataset](http://yann.lecun.com/exdb/mnist/) using the
[tf.data](https://www.tensorflow.org/api_docs/python/tf/data),
[tf.estimator.Estimator](https://www.tensorflow.org/api_docs/python/tf/estimator/Estimator),
and
[tf.layers](https://www.tensorflow.org/api_docs/python/tf/layers)
APIs.Tensorflow 1.15.0 is used in this example.TF-2.0.0 is not supported.
## Setup
To build application using standalone S2I and then run the resulting image with Docker execute:
```shell
s2i build . centos/python-36-centos7 mnist-python
```
To train the model, run the following:
```
docker run -it mnist-python
```
This runs the app.sh script
```
python official/mnist/mnist.py --data_format 'channels_last' --stop_threshold '0.96'
```
The model will begin training and will automatically evaluate itself on the
validation data.The checkpoint folder is `/tmp/mnist_model/model.ckpt`.
## Exporting the model
You can export the model into Tensorflow [SavedModel](https://www.tensorflow.org/guide/saved_model) format by using the argument `--export_dir`:
```
python mnist.py --data_format 'channels_last' --stop_threshold '0.96' --export_dir /tmp/mnist_saved_model
```
The SavedModel will be saved in a timestamped directory under `/tmp/mnist_saved_model/` (e.g. `/tmp/mnist_saved_model/1513630966/`).
**Getting predictions with SavedModel**
Use [`saved_model_cli`](https://www.tensorflow.org/guide/saved_model#cli_to_inspect_and_execute_savedmodel) to inspect and execute the SavedModel.
```
saved_model_cli run --dir /tmp/mnist_saved_model/TIMESTAMP --tag_set serve --signature_def classify --inputs image=/opt/app-root/src/official/mnist/examples.npy
```
`examples.npy` contains the data from `example5.png` and `example3.png` in a numpy array, in that order. The array values are normalized to values between 0 and 1.
The output should look similar to below:
```
Result for output key classes:
[5 3]
Result for output key probabilities:
[[ 1.53558474e-07 1.95694142e-13 1.31193523e-09 5.47467265e-03
5.85711526e-22 9.94520664e-01 3.48423509e-06 2.65365645e-17
9.78631419e-07 3.15522470e-08]
[ 1.22413359e-04 5.87615965e-08 1.72251271e-06 9.39960718e-01
3.30306928e-11 2.87386645e-02 2.82353517e-02 8.21146413e-18
2.52568233e-03 4.15460236e-04]]
```
## Experimental: Eager Execution
[Eager execution](https://research.googleblog.com/2017/10/eager-execution-imperative-define-by.html)
(an preview feature in TensorFlow 1.5) is an imperative interface to TensorFlow.
The exact same model defined in `mnist.py` can be trained without creating a
TensorFlow graph using:
```
python mnist_eager.py
```
## Credits
TensorFlow Authors