- 31 Jan, 2018 1 commit
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Neal Wu authored
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- 26 Jan, 2018 1 commit
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Mark Daoust authored
The `sparse` version is more efficient anyway. I'm returning the labels shape [1] instead of [] because tf.accuracy fails otherwise.
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- 18 Jan, 2018 1 commit
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Asim Shankar authored
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- 10 Jan, 2018 1 commit
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Asim Shankar authored
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- 06 Jan, 2018 1 commit
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Asim Shankar authored
This is a step towards merging the example in https://github.com/tensorflow/tpu-demos/tree/master/cloud_tpu/models/mnist with this repository, so we have a single model definition for training across CPU/GPU/eager execution/TPU. The change to dataset.py is so that the raw data can be read from cloud storage systems (like GCS and S3).
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