This directory builds a convolutional neural net to classify the MNIST dataset using the tf.data, tf.estimator.Estimator, and tf.layers APIs.Tensorflow 1.15.0 is used in this example.TF-2.0.0 is not supported.
To build application using standalone S2I and then run the resulting image with Docker execute:
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
.
You can export the model into Tensorflow SavedModel 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
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]]
Eager execution
(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
TensorFlow Authors