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diff --git a/RELEASE.md b/RELEASE.md index 0cd4eef5d6..33fe88cd54 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -65,37 +65,6 @@ integration into apps. See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/android/README.md for more details. -* RNNCells' variable names have been renamed for consistency with Keras layers. - Specifically, the previous variable names "weights" and "biases" have - been changed to "kernel" and "bias", respectively. - This may cause backward incompatibility with regard to your old - checkpoints containing such RNN cells, in which case you can use the tool - [checkpoint_convert script](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/rnn/python/tools/checkpoint_convert.py) - to convert the variable names in your old checkpoints. -* Many of the RNN functions and classes that were in the `tf.nn` namespace - before the 1.0 release and which were moved to `tf.contrib.rnn` have now - been moved back to the core namespace. This includes - `RNNCell`, `LSTMCell`, `GRUCell`, and a number of other cells. These - now reside in `tf.nn.rnn_cell` (with aliases in `tf.contrib.rnn` for backwards - compatibility). The original `tf.nn.rnn` function is now `tf.nn.static_rnn`, - and the bidirectional static and state saving static rnn functions are also - now back in the `tf.nn` namespace. - - Notable exceptions are the `EmbeddingWrapper`, `InputProjectionWrapper` and - `OutputProjectionWrapper`, which will slowly be moved to deprecation - in `tf.contrib.rnn`. These are inefficient wrappers that should often - 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. ## Deprecations |