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| author |  A. Unique TensorFlower <gardener@tensorflow.org> | 2017-01-07 09:19:27 -0800 |
|---|---|---|
| committer | TensorFlower Gardener <gardener@tensorflow.org> | 2017-06-01 09:23:03 -0700 |
| commit | ce32228c49e595f966485acee947131e4ab04905 (patch) | |
| tree | 986ab7f9561ef1b2c7084f483910d11762b04667 /RELEASE.md | |
| parent | a23255bc079bb94006aa0bfdc5000eed0d97098a (diff) | |
Add release notes for Intel MKL integration.
PiperOrigin-RevId: 157722003
Diffstat (limited to 'RELEASE.md')
| -rw-r--r-- | RELEASE.md | 9 |
1 files changed, 9 insertions, 0 deletions
diff --git a/RELEASE.md b/RELEASE.md index ec24d6fd80..1590aabfef 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -39,6 +39,15 @@ be replaced by calling `embedding_lookup` or `layers.dense` as pre- or post- processing of the rnn. For RNN decoding, this functionality has been replaced with an alternative API in `tf.contrib.seq2seq`. +* Intel MKL Integration (https://software.intel.com/en-us/articles/tensorflow-optimizations-on-modern-intel-architecture). Intel developed a number of + optimized deep learning primitives: In addition to matrix multiplication and + convolution, these building blocks include: + Direct batched convolution + Pooling: maximum, minimum, average + Normalization: LRN, batch normalization + Activation: rectified linear unit (ReLU) + Data manipulation: multi-dimensional transposition (conversion), split, + concat, sum and scale. ## Bug Fixes and Other Changes * In python, `Operation.get_attr` on type attributes returns the Python DType |