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The high-level TensorFlow APIs, via MNIST & 'fashion MNIST'

This directory contains a series of labs/examples that introduce the the high-level TensorFlow APIs in tf.estimator and via the 'MNIST' (and 'Fashion MNIST') data sets.

  • mnist_estimator.ipynb: Introducing the high-level TensorFlow APIs in tf.estimator and to easily build a LinearClassifier, as well a DNNClassifier with hidden layers. Introducing TensorBoard.

    Run this example as a colab notebook.

  • mnist_eager_keras-debug.ipynb and mnist_eager_keras.ipynb: This pair of notebooks show an example of using Keras with TensorFlow eager mode, on the 'Fashion MNIST' dataset. These notebooks require TensorFlow >=1.7.

    Run these examples as colab notebooks: mnist_eager_keras-debug.ipynb or mnist_eager_keras.ipynb. (We'll start with the "debug" version in the workshop).

  • Building Custom CNN Estimators: Where 'canned' Estimators aren't available for your use case, you can build a custom one, to get all the advantages of using an Estimator, including support for distributed training. You can use Keras layers to do this. Examples show how to do this for variants of CNNs, with both Keras and TensorFlow layers.
    Click through to the README for links to run these examples as colab notebooks.


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