This directory builds a convolutional neural net to classify the MNIST dataset using the tf.data, tf.estimator.Estimator, and tf.layers APIs.
To begin, you'll simply need the latest version of TensorFlow installed. Then to train the model, run the following:
python mnist.py
The model will begin training and will automatically evaluate itself on the validation data.
Illustrative unit tests and benchmarks can be run with:
python mnist_test.py
python mnist_test.py --benchmarks=.
You can export the model into Tensorflow SavedModel format by using the argument --export_dir:
python mnist.py --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=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
mnist.py (and mnist_eager.py) demonstrate training a neural network to
classify digits on CPUs and GPUs. mnist_tpu.py can be used to train the
same model using TPUs for hardware acceleration. More information in
the tensorflow/tpu repository.