Tensorflow: CNN on MINST with examples

• Tensorflow coding reference document

Tested environments

• Tensorflow version: 1.8

Prerequisite

• Must make MNIST tfrecords with 2_create_mnist_tfrecords.py for some examples

Examples

Dataset API example

• 0_dataset_api_simple.py
  • example of from_tensor_slices(), which iterate through raw files
  • run several times to get to know about shuffling the data
  • seems like we should shuffle on file names with shuffle_buffer size greater_or_equal to length of files
• 1_dataset_api.py
  • example of *.tfrecords files
  • take a look at 'case-*.txt' files with test_tfrecords()
  • note: there is file name shuffling as well as dataset element shuffling which may confusing at first
• 2_create_mnist_tfrecords.py
  • download mnist train & test data and converts to *.tfrecord files

Low level API example

• 3_low_level_api.py
  • use tf.get_variable(), tf.nn.*
  • tf.Session() and tf.placeholder()

high level API example

• 4_1_high_level_api.py
  • tf.estimator and tf.layers.*
  • prepare trained model for tensorflow serving
• 4_2_export_trained_estimator.py
  • Prerequisite: 4_1_high_level_api.py
  • prepare existing estimator's model_fn() for serving to use in another estimator
• 4_3_estimator_within_estimator.py
  • Prerequisite: 4_1_high_level_api.py, 4_2_export_trained_estimator.py
  • use trained custum estimator inside another estimator

eager execution mode example

• 5_eager_execution.py
  • saving eager model with tfe.Saver
  • load model and evaluate
  • includes use case of converting eager trained model to servable model

multi-gpu example

• 6_multi_gpu.py and 6_multi_gpu_run.sh
  • muti-tower fashion(using same network on each GPU - data parallelism)

References

• MNIST-tutorial
• Tensorflow dataset api
• Tensorflow estimator
• Tensorflow eager execution