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(#9398)\n\n* added _load_attributes_from_hdf5_group() and _save_attributes_to_hdf5_group() methods that can deal with the problem of saving models with a lot of layers (or with nested deep models) into HDF5 files\r\n\r\n* np.split --> np.array_split, making _save_attributes_to_hdf5_group() robust to unrealistically long layer names\r\n\r\n* adding unit tests for checking the validity of `layer_names` and `weights_names` HDF5 file attributes splitting\r\n\r\n* topology.py changes from review in #9398","distinct":true,"url":"https://api.github.com/repos/theSage21/keras/commits/4eab0556d29f11ff41758d80c15d6457263f6a93"},{"sha":"8193bae22a3aec7b9b2ad2962b62ab71ee5a6f2e","author":{"name":"Francois Chollet","email":"0bf6f10265bea1a2b8787c59d746d2ff00a8e031@gmail.com"},"message":"Minor style fixes","distinct":true,"url":"https://api.github.com/repos/theSage21/keras/commits/8193bae22a3aec7b9b2ad2962b62ab71ee5a6f2e"},{"sha":"2802c0820218e29d09382cbaf2235825a3ae703b","author":{"name":"Bohumír Zámečník","email":"316d142cff142118080ee6f3bcebb48b4b167ec1@gmail.com"},"message":"Allow weights trained with CuDNNGRU to be used without GPU (#9112)\n\n* Implement GRU that's compatible with CuDNNGRU and allow loading weights.\r\n\r\n- GRU in Keras follows 1406.1078v3, while CuDNNGRU is based on 1406.1078v1\r\n- implement GRUResetAfter based on 1406.1078v1\r\n  - reset gate after matrix multiplication\r\n   - separate biases for input kernel and recurrent kernel\r\n- add code for weights conversion similar to CuDNNLSTM (except for biases)\r\n- so far there are two classes for easier testing, ideally we'd like a single class\r\n  with configuration option\r\n\r\n* Make two variants of GRUCell available from GRU with a parameters instead of separate classes.\r\n\r\n- cells with different implementation: GRUCell, GRUResetAfterCell\r\n- remove GRUResetAfter class\r\n- GRU: add parameter variant: 'reset_before' (default), 'reset_after'\r\n\r\n* Merge GRUCell and GRUResetAfterCell.\r\n\r\n- put both implementations together, differentiate by argument \"variant\"\r\n- fix GRU class name in topology.py (weights conversion)\r\n\r\n* Merge tests of converting weights from CuDNN RNN layer.\r\n\r\nThere were two similar tests for LSTM and GRU. Now we have\r\na parametrized test.\r\n\r\n* Change the GRU parameter \"variant\" to \"reset_after\" based on code review.\r\n\r\nChange variant='reset_after'/'reset_before' to reset_after=True/False\r\nin GRU and GRUCell. It should be more intuitive and we use it already\r\ninternally.\r\n\r\nSince the paper will most probably will not evolve a boolean argument\r\nfor two conventions is enough.\r\n\r\n* Fix GRU with \"reset_after\" after code review.\r\n\r\n- fix default value for reset_after parameter in GRU\r\n  - by mistake I set it on, it would break compatibility\r\n- add reset_after to GRUCell config\r\n- fix a typo in comment\r\n\r\n* #9112 Add (failing) tests of compatibility weights of GRU variants.\r\n\r\n- GRU(reset_after=True) <-> CuDNNGRU - can be converted\r\n- loading weights to the same class should always succeed\r\n  - now fails for GRU(reset_after=True) since the code cannot distinguish\r\n    its weights from CuDNNGRU\r\n- other combinations are not compatible and should throw a ValueError\r\n  - they do not throw now\r\n- make clear that wrapping a layer with Sequential model is done\r\n  to initialize the weights\r\n\r\n* Improve converting GRU/CuDNNGRU weights.\r\n\r\n- split biases in GRU(reset_after=True) to two sets\r\n  - in order to distinguish weights of this variant and CuDNNGRU\r\n    based on the bias array shape\r\n- allow converting weights from GRU(reset_after=True) to CuDNNGRU\r\n- fail fast when converting incompatible weights\r\n- add tests\r\n\r\n* Refactor and simplify tests.\r\n\r\n- merge tests of converting between plain RNN and CuDNN RNN\r\n- make them parametric\r\n- add parametric ids\r\n\r\n* Rename bias variables in GRU to make them consistent with kernels.\r\n\r\n* Fix tests.\r\n\r\n- CuDNN layers can be instantiated only inside test with required backed,\r\n  not in the pytest decorator\r\n- fix PEP8 code formatting\r\n\r\n* Fix the CuDNN RNN tests.\r\n\r\n- due to silly mistakes in refactoring the LSTM conversion test was not working\r\n\r\n* Refactor common code in LSTM/GRU CuDNN weight conversion.\r\n\r\n* Allow converting LSTM weights to CuDNNLSTM.\r\n\r\n- in order to make it consistent with GRU\r\n- it's also possible and actually simpler\r\n- extract the code to a separate function\r\n- add a test\r\n\r\n* Test both implementations of the GRU/LSTM for CuDNN weight conversion.\r\n\r\n* Fix things based on code review.\r\n\r\n- fix string formatting for ValueError\r\n- inline some function and import\r\n- format docs more consistently\r\n\r\n* Split a RNN weight loading test, so that non-CuDNN stuff can run on Travis CI.\r\n\r\n- move back to test_topology.py where it should be\r\n- split into two parts - GRU/LSTM can run without GPU, CuDNN variant need it\r\n- adjust imports\r\n\r\n* Fix slicing biases on CNTK backend.\r\n\r\n`self.bias[0]` should be 1D, but CNTK returns 2D. We need to flatten.\r\nOn other backends flattening 1D array is idempotent.\r\n\r\nSee: https://github.com/Microsoft/CNTK/issues/2938\r\n  (Slicing of arrays is strange and not numpy compatible)\r\n\r\n* Write better docs for keras.engine.topology._convert_rnn_weights().","distinct":true,"url":"https://api.github.com/repos/theSage21/keras/commits/2802c0820218e29d09382cbaf2235825a3ae703b"}]}
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