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4e5741a Temporarily remove eager/graph annotation until the class level one is released. PiperOrigin-RevId: 201509633 17 July 2018, 15:55:38 UTC
6926503 Add a bidirectional recurrent core to sonnet. Based off encoder component from: https://arxiv.org/pdf/1409.0473.pdf I avoided tf.while_loop as it complicates the implementation and we want explicit access to states and outputs from all steps of the sequence. PiperOrigin-RevId: 200346579 17 July 2018, 15:55:32 UTC
4f34f4f Support for tf.bfloat16 in Sonnet. PiperOrigin-RevId: 200045112 17 July 2018, 15:55:26 UTC
e61955b Add eager mode tests for sonnet nets. PiperOrigin-RevId: 199800734 17 July 2018, 15:55:20 UTC
ef9146d Fix dependencies in examples/BUILD - SciPy was missing. PiperOrigin-RevId: 199638219 17 July 2018, 15:55:01 UTC
1eac5c6 Make dataset_nth_farthest python3 compatible. PiperOrigin-RevId: 199635116 17 July 2018, 15:54:55 UTC
5332938 Make rmc_nth_farthest python3 compatible PiperOrigin-RevId: 199633253 17 July 2018, 15:54:49 UTC
220832e Fix rmc_nth_farthest.ipynb PiperOrigin-RevId: 199629407 17 July 2018, 15:54:43 UTC
9fe0a35 Adds demo for Relational Memory Core for "nth farthest" task from paper. PiperOrigin-RevId: 199622123 17 July 2018, 15:54:30 UTC
31bc79c Sonnet version update produced on Tuesday, 05. June 2018 PiperOrigin-RevId: 199312224 05 June 2018, 16:53:02 UTC
0c9ed73 Updated changelog for version 1.23 PiperOrigin-RevId: 199311026 05 June 2018, 16:45:51 UTC
05e2109 Fix error message in DeepRNN. Because we enumerate through core_sizes[1:] we need to add 1 to i when printing out which core we are referring to in the case of an error. Also fixed an issue where one of the shapes printed had the first dimension removed, and the other one didn't. PiperOrigin-RevId: 199127178 05 June 2018, 16:45:43 UTC
3dd3d33 Make relational_memory python3 compatible. PiperOrigin-RevId: 199124668 05 June 2018, 16:45:35 UTC
3ab2bea Change test assertion function. PiperOrigin-RevId: 199121362 05 June 2018, 16:45:26 UTC
5b314d6 Add relational memory module to sonnet. Adding relational memory implementation from "Relational Recurrent Neural Networks", Santoro et al., 2018. PiperOrigin-RevId: 199116117 05 June 2018, 16:45:17 UTC
b66dadc Sonnet version update produced on Friday, 01. June 2018 PiperOrigin-RevId: 198867845 01 June 2018, 14:20:59 UTC
71c16c1 Switch to using private member variables when possible. There can be subtle differences to the public and private versions of member variables in the conv module (eg, Convolution's `stride` is the conv size + 2 but `_stride` is the conv size). The current module mixes and matches these. To reduce cognitive load a bit, we switch to using the private member variable when possible. PiperOrigin-RevId: 198857690 01 June 2018, 14:20:52 UTC
3ec0d90 Additional changes to VQVAE notebook to make it python3 compatible. PiperOrigin-RevId: 198735118 01 June 2018, 10:48:37 UTC
ce869c1 Make brnn_ptb_test write checkpoints to temp directory PiperOrigin-RevId: 198729764 01 June 2018, 10:48:31 UTC
f6a930b MergeDims: also handle dimensions of size zero. PiperOrigin-RevId: 198699387 01 June 2018, 10:48:25 UTC
6307466 Use six to load cPickle in VQVAE notebook PiperOrigin-RevId: 198694898 01 June 2018, 10:48:18 UTC
35778ef Updated changelog for Sonnet version 1.21 PiperOrigin-RevId: 198559887 30 May 2018, 14:59:18 UTC
d6a02aa internal change PiperOrigin-RevId: 198559691 30 May 2018, 14:59:11 UTC
45d0f76 Jupyter Notebook demonstrating VQ-VAE training on CIFAR-10. PiperOrigin-RevId: 198556907 30 May 2018, 14:59:05 UTC
024f263 Add control dependencie to VectorQuantizerEMA to avoid potential non-deterministic results. PiperOrigin-RevId: 197884089 30 May 2018, 14:43:34 UTC
f72c4a5 Improve Sonnet's MergeDim behaviour on partially defined shapes. PiperOrigin-RevId: 197863174 30 May 2018, 14:43:27 UTC
c3d3bc2 Add support for custom getters in ConvNet2D PiperOrigin-RevId: 197546042 30 May 2018, 14:43:20 UTC
8a7ca0a Add SeparableConv1D class to sonnet. PiperOrigin-RevId: 197408513 30 May 2018, 14:43:14 UTC
85492de skip_connection deprecation warning is printed only once PiperOrigin-RevId: 197370137 30 May 2018, 14:43:07 UTC
0277e53 Sonnet version update produced on Tuesday, 08. May 2018 PiperOrigin-RevId: 195825275 30 May 2018, 14:42:59 UTC
c4ac2b1 Add copyright header PiperOrigin-RevId: 195399161 30 May 2018, 14:42:46 UTC
c595084 Add VQ-VAE plus EMA variant to Sonnet. PiperOrigin-RevId: 195398448 30 May 2018, 14:42:36 UTC
f34755c Add snt.summarize_variables to Sonnet. This prints a summary of #variables, #scalars and memory usage for each datatype. PiperOrigin-RevId: 194779974 08 May 2018, 15:49:30 UTC
df245c0 Make an error more explicit when the Sonnet module name is not a string. PiperOrigin-RevId: 194254264 08 May 2018, 15:49:14 UTC
c838ebf Sonnet version update produced on Tuesday, 24. April 2018 PiperOrigin-RevId: 194098244 24 April 2018, 16:28:00 UTC
6db202c Make brnn_ptb and ptb_reader python3 compatible. Fixes https://github.com/deepmind/sonnet/issues/79 PiperOrigin-RevId: 194091717 24 April 2018, 15:51:37 UTC
50b41df Update installation instructions PiperOrigin-RevId: 194085445 24 April 2018, 15:51:30 UTC
a30da96 Add a unit test for brnn_ptb. Runs a small model with fake data for 1 epoch. PiperOrigin-RevId: 194079346 24 April 2018, 15:51:20 UTC
5c7e355 Merge SeparableConv2D into _ConvND Merge the final convolution class into _ConvND. We also add a property field for `channel_multiplier` in `DepthwiseConv2D` as well as `SeparableConv2D`. PiperOrigin-RevId: 193007472 24 April 2018, 15:51:11 UTC
1a313f6 Sonnet version update produced on Tuesday, 10. April 2018 PiperOrigin-RevId: 192293232 10 April 2018, 16:01:18 UTC
a86f044 Remove skip_connnection option from ConvLSTM. PiperOrigin-RevId: 192131304 10 April 2018, 14:10:07 UTC
600268f Update test PiperOrigin-RevId: 192129236 10 April 2018, 14:10:01 UTC
5980cf9 Move CausalConv related functionality into subclass. There are a few bits of functionality that are only related to CausalConv1D and don't need to be in _ConvND. We're moving them into the subclass and calling _ConvND machinery for the rest. PiperOrigin-RevId: 192117666 10 April 2018, 14:09:56 UTC
1ca6866 Refactor DepthwiseConv2D to use _ConvND. Rewrite DepthwiseConv2D so that it shares as much functionality with _ConvND as possible. PiperOrigin-RevId: 192116657 10 April 2018, 14:09:50 UTC
23cb8d9 Refactor InPlaneConv2D to use _ConvND This refactor removes almost all of the code in InPlaneConv2D and uses _ConvND instead. We also fix some tests that were failing when run on the GPU in conv_gpu_test due to numpy & tensorflow version upgrades. PiperOrigin-RevId: 192109587 10 April 2018, 14:09:45 UTC
9802714 Add dilation rate tests for ConvNet2D. Adding testing for the `rates` argument in ConvNet2D. PiperOrigin-RevId: 191893167 10 April 2018, 14:09:39 UTC
159adc5 Remove spurious spaces in doc. PiperOrigin-RevId: 190449915 10 April 2018, 14:09:34 UTC
e660931 Use tensorflow nest for nest operations. PiperOrigin-RevId: 188871826 10 April 2018, 14:09:28 UTC
1bee593 Use nest from pytflib to get rid of deprecation messages. PiperOrigin-RevId: 188862864 10 April 2018, 14:09:21 UTC
4596fd0 Sonnet version update produced on Monday, 12. March 2018 PiperOrigin-RevId: 188861040 13 March 2018, 12:50:56 UTC
80d4784 Fix typo in DeepRNN docs. PiperOrigin-RevId: 188474053 12 March 2018, 14:10:13 UTC
e331172 Support the root scope (an empty string) in get_variable_scope. PiperOrigin-RevId: 188341067 12 March 2018, 14:10:08 UTC
7d65b8a Improve the way reuse_variables handles name scopes This fixes a bug where outer name scopes were ignored by reuse_variables. PiperOrigin-RevId: 188163153 12 March 2018, 14:10:03 UTC
b1191ad Docstring clarification PiperOrigin-RevId: 187880593 12 March 2018, 14:09:58 UTC
e62f660 Add mention to get_all_variables in snt.Sequential warning. PiperOrigin-RevId: 187456723 12 March 2018, 14:09:53 UTC
553159b Added optional dilation rates argument for ConvNet2D. PiperOrigin-RevId: 186779967 12 March 2018, 14:09:48 UTC
c855672 Sort variables returned by get_all_variables() by name. The variables returned by get_all_variables() are stored in a set, so they must be sorted to determine an ordering over them. This change sorts the variables by name before returning them. PiperOrigin-RevId: 186752578 12 March 2018, 14:09:42 UTC
a1d1a37 Add missing argument to example code. PiperOrigin-RevId: 186613534 12 March 2018, 14:09:36 UTC
108fce7 Bump version to 1.17 PiperOrigin-RevId: 186463459 21 February 2018, 17:32:51 UTC
c522ae6 Updated README - TensorFlow v1.5 required GIT_ORIGIN_REV_ID=264bc35ad75f1dad605407295e857846237917be PiperOrigin-RevId: 185666441 21 February 2018, 16:10:30 UTC
62f4399 Implementation of get_all_variables() for sonnet modules. We introduce a module call stack, which tracks the order in which modules are called. When a module enters __call__ (or _enter_variable_scope) it adds itself to the top of the stack. Variables created inside of the custom_getter are added to a collection specific to the module on the top of the stack. Before exiting __call__ (or _enter_variable_scope) the module moves all variables added to this graph collection into `_all_variables`, removes itself from the top of the stack, and adds all of the variables from `self._all_variables` to collection for the module that is currently at the top of the module stack. PiperOrigin-RevId: 185664981 21 February 2018, 16:10:23 UTC
06b8e4f Replace keep_dims with keepdims in call to tf.reduce_prod() PiperOrigin-RevId: 185524194 21 February 2018, 16:10:17 UTC
f9e3859 Fix an error message which was incorrect. PiperOrigin-RevId: 185509698 21 February 2018, 16:10:10 UTC
c75bca4 Fix comment. PiperOrigin-RevId: 185501790 21 February 2018, 16:10:03 UTC
0737482 Internal change. PiperOrigin-RevId: 185389700 21 February 2018, 16:09:55 UTC
551d652 Fix Python3 incompatibilities in new tests and methods. PiperOrigin-RevId: 183831798 30 January 2018, 16:10:36 UTC
b23489a Update changelog PiperOrigin-RevId: 183680938 29 January 2018, 17:59:14 UTC
2136d31 Add backwards compatibility with tests for ones and zeros name scopes. PiperOrigin-RevId: 183680464 29 January 2018, 17:59:00 UTC
1d2d99e Remove fixed seed dependency in gated_rnn_test.LSTMTest.testRecurrentDropout PiperOrigin-RevId: 183644341 29 January 2018, 17:03:51 UTC
132924d Refactor out bias construction and application. Refactor out the construction of the bias variable and its application to the output of all Convolution modules. PiperOrigin-RevId: 183249262 29 January 2018, 17:03:46 UTC
84bab04 Enable custom_getter for TrainableVariable. PiperOrigin-RevId: 183243768 29 January 2018, 17:03:40 UTC
438b320 Refactor out inputs verification. Remove duplicate code that is performing the same checks on the `inputs` tensor in every Convolution class. PiperOrigin-RevId: 183230711 29 January 2018, 17:03:35 UTC
2a8e6e9 Fix typo. PiperOrigin-RevId: 183071198 29 January 2018, 17:03:29 UTC
de769df 1. Start supporting TensorShapes for the output_shape input for Conv*DTranspose classes. 2. Support the use of tf.Dimension objects instead of integers for dimensions. 3. Add tests to make sure output_shape inference works correctly. PiperOrigin-RevId: 182768071 29 January 2018, 17:03:24 UTC
3aead32 Correct typo in warning. PiperOrigin-RevId: 182755077 29 January 2018, 17:03:18 UTC
109d606 Make masked Conv2D usable with ResourceVariables by avoiding use of *=. PiperOrigin-RevId: 182516172 29 January 2018, 17:03:11 UTC
7393cfb Explicitly enter the scope of the connected Graph in AbstractModule.get_variables() This fixeds the following inconsistency: ``` with tf.Graph().as_default() as graph: input = tf.constant(np.random.randn(16, 784)) lin = snt.Linear(output_size=256) output = lin(input) print(lin.get_variables()) # prints tuple of 2 variables print(lin.get_variables()) # prints empty tuple, as technically we are in a different Graph. ``` Also added a .graph readonly property to AbstractModule. PiperOrigin-RevId: 182044744 29 January 2018, 16:56:19 UTC
7bdd40c Provide better examples for snt.reuse_variables. PiperOrigin-RevId: 181984575 29 January 2018, 16:56:13 UTC
3943452 Push BBB library and Bayesian RNN example to open source sonnet. PiperOrigin-RevId: 181743627 29 January 2018, 16:56:08 UTC
315719e Select the correct input channel and stride values within Conv{1,2,3}DTranspose.transpose() when the data_format is NC*. Add more tests for Conv*Transpose transpose functionality. PiperOrigin-RevId: 181724912 29 January 2018, 16:56:02 UTC
7865f86 Improve an error message. This is supposing `data_format` is a string, but if one is incorrectly passing something else, the error message will fail e.g. with ValueError: Unknown format code 's' for object of type 'int' With this change the error becomes: ValueError: Invalid data_format 256. Allowed formats set(['NCHW', 'NHWC']) PiperOrigin-RevId: 181604070 29 January 2018, 16:55:57 UTC
3a21b8d Remove dependency on fixed seed for testZoneout. This means it won't be possible to test for expected state values. PiperOrigin-RevId: 181587515 29 January 2018, 16:55:48 UTC
1d2171d Sonnet version update produced on Monday, 8. January 2018 PiperOrigin-RevId: 181464426 10 January 2018, 17:19:34 UTC
f11b5af Add Con[1-3]DTranspose classes to conv_gpu_tests. Expanding testing coverage of the transpose classes. PiperOrigin-RevId: 181293937 10 January 2018, 16:53:39 UTC
3c18209 Add Causal1DConv testing to conv_gpu_test. PiperOrigin-RevId: 180955048 08 January 2018, 12:15:39 UTC
a1b1f34 Stop using constant initializers in conv_gpu_test.py. Unit tests were using constant initializers to check for correctness in data_format permutation operations. This is problematic; you want the weights to be very random so that they don't inadvertently mask an issue in their functionality. PiperOrigin-RevId: 180941577 08 January 2018, 12:15:33 UTC
fe91080 Rename SUPPORTED_DATA_FORMATS to SUPPORTED_2D_DATA_FORMATS. As we support far more than just 2D conv now, this variable needs to change. PiperOrigin-RevId: 180915317 08 January 2018, 12:15:28 UTC
865f4aa Refactor CausalConv1D to use _ConvND. Tests for clone functionality in all _ConvND subclasses. CausalConv1D can exist using the new _ConvND superclass. Add support within _ConvND to accommodate padded input. Refactored _ConvND a bit to get smaller methods as _build() was getting unwieldy. With CausalConv1D inheriting from _ConvND, it is now cloneable by default. I noticed that most of the classes lacked testing of their clone functionality, so I added those. Finally, in supporting multiple data_formats, I noticed there aren't enough tests that check to see an exception is thrown when an invalid data_format is used. So I added those. PiperOrigin-RevId: 180912740 08 January 2018, 12:15:22 UTC
699f8cb Refactor Conv{1,2,3}Transpose classes into one. Add tests for transpose functionality. Rearchitect the Transpose classes into one base class that is then instantiated for each dimension convolution we want. Add tests for Conv{1,3}Transpose; we make sure to test N*C and NC* data formats. PiperOrigin-RevId: 180907942 08 January 2018, 12:15:16 UTC
511f11b Add a Recurrent Highway Network cell. PiperOrigin-RevId: 180705745 08 January 2018, 12:15:06 UTC
5fe263b Improve snt.Embed performance in distributed training. Adds a fix to avoid excess computation on parameter servers. PiperOrigin-RevId: 180674688 08 January 2018, 12:15:01 UTC
6f9f46e Update AbstractModule documentation. PiperOrigin-RevId: 180531448 08 January 2018, 12:14:55 UTC
ca13720 Creation of a ConvND class. More flexible masks. Lots more tests. Creating a ConvND class that is then subclassed for Conv{1,2,3}D. This removes a lot of duplicated boilerplate code. As part of this we, are also adding a `mask` argument to the Conv{1,3}D classes. Finally, we remove rank restrictions on the mask argument. We add testing for new masking functionality. PiperOrigin-RevId: 179918026 08 January 2018, 12:14:49 UTC
d0f50a6 Extend batch_norm_v2 data_format default across dimensions. PiperOrigin-RevId: 179915706 08 January 2018, 12:14:44 UTC
89c2091 Improve snt.Embed performance in distributed training. Adds a fix to avoid excess computation on parameter servers. PiperOrigin-RevId: 179912285 08 January 2018, 12:14:38 UTC
9a547ed Implemented snt.BatchNormV2, which differs from snt.BatchNorm in the following ways: * Automatically computes updates to moving statistics by default (i.e. update_ops_collection=None). * Uses moving statistics by default when testing (i.e. test_local_stats=False). * Takes a data_format string (NC/NWC/NCW/NHWC/NCHW/NDHWC/NCDHW) rather than axes; reduces along all non-C axes. * Uses fused batch normalization by default. If the data_format isn't NHWC or NCHW, reshapes the batch internally. * Uses flat variables for the moving statistics, scale, and offset so that they can be shared between different data_formats. PiperOrigin-RevId: 179819339 08 January 2018, 12:14:32 UTC
b920570 tf.float16 support for batch_norm PiperOrigin-RevId: 179714930 08 January 2018, 12:14:26 UTC
e2ce831 Improve error message when partitioners/regularizers/initializers used wrong. PiperOrigin-RevId: 179566444 08 January 2018, 12:14:20 UTC
3b9f636 When Sequential contains no layers, simply act as identity. Previously, a sequential with no layers would return any input wrapped in a 1 element tuple, which is a strange inconsistency. PiperOrigin-RevId: 179566305 08 January 2018, 12:14:14 UTC
a7a557e Improve error message when partitioners/regularizers/initializers used wrong. PiperOrigin-RevId: 179561994 08 January 2018, 12:14:09 UTC
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