0.14 (#172)

* Better sampler (#160)

* working verison ish

* faster sampler

* Remove quickdraw notebooks.

Co-authored-by: Owen Vallis <ovallis@google.com>

* * Add get_slice to SingleShotMemorySampler
* Make get_examples() -> _get_examples()
* Update doc strings to refer to classes_per_batch and examples_per_class_per_batch
* Update notebook to also use examples_per_class
* Update Return types of _get_examples() and generate_batch() to use FloatTensor and IntTensor

* Set version to 0.14

* Run workflow on push for master only and on pull_request for all branches.

* Add create_index method to enable setting an index outside of the compile call. (#164)

fix #163

* * Add sampler_io_cookbook notebook
* Fix get_slice in tfds sampler
* other small sampler fixes

* Remove randomflip from mnist augmentation in sampler cookbook.

* Add link to sampler notebook in examples readme.

* * Remove tf.gather from memory_samplers._get_examples(). Replace with faster for loop over lists and convert to np.array
* Improve docstring coverage
* Basic formatting using yamf
* Reorder top level imports in __init__

* Update batch info when initializing sampler to better reflect the initial batch size and number of augmenters.

* kaggle first

* Raise errors if users try to evaluate over an empty index or pass an … (#169)

* Raise errors if users try to evaluate over an empty index or pass an empty set of lookupd to the evaluators.

* Add hints to index Exceptions.

* Make batch size msg clearer.

* MultiSampler now samples without replacement unless the class size is <
num_examples_per_class. In that case, we warn the user and then
sample with replacement. The warning will only be printed once per
class.

Also fixed a mypy error in the img_augments.py module.

* Update SimilarityModel.compile() arg order to be consistent with the
Keras Model.compile().

* Enforce that class_mapping is map by calling .get() and move the class_label code into it's own function.

* Update hello world to remove class_mapping from viz_neighbors_imgs as we can just use the class ids.

* Ensure class mapping in nn_viz requires a dict (#170)

* Enforce that class_mapping is map by calling .get() and move the class_label code into it's own function.

* Update hello world to remove class_mapping from viz_neighbors_imgs as we can just use the class ids.

* Add support for passing distance thresholds and matcher in callbacks.

* Update new for 0.14 in README

Co-authored-by: Elie Bursztein <github@elie.net>

Author: owenvallis

README.md

@@ -20,10 +20,8 @@ To learn more about the benefits of using similarity training, you can check out
 
 ## What's new
 
-- [Aug 21]: Interactive embedding `projector()` added. See this [notebook](examples/supervised_visualization.ipynb)
-- [Aug 21]: [`CircleLoss()`](api/TFSimilarity/losses/CircleLoss.md) added
-- [Aug 21]: [`PNLoss()`](api/TFSimilarity/losses/PNLoss.md) added.
-- [Aug 21]: [`MultiSimilarityLoss()`](api/TFSimilarity/losses/MultiSimilarityLoss.md) added.
+- [Oct 8]: Added `Samplers.*` IO [notebook](examples/sampler_io_cookbook.ipynb) detailing how to efficently sample your data for succesful training.
+- [Oct 8]: 0.14 is out which includes various speed improvements and post initial release bug fixes.
 
 For previous changes - see [the release changelog](./releases.md)
 

examples/README.md

@@ -5,3 +5,4 @@
 | ------ | :-----: | ---------- |
 | [Hello World](./supervised_hello_world.ipynb) | Supervised | Train and use an image similarity model to find similar looking MNIST digits |
 | [visualization](./supervised_visualization.ipynb) | Supervised | Train an image similarity model on the Stanford Dogs dataset using Evaluation Callbacks and the interactive visualizer |
+| [Sampler IO Cookbook](./sampler_io_cookbook.ipynb) | Utils | Examples demonstrating how to use the various in memory batch samplers.

examples/kaggle.ipynb

@@ -11,24 +11,25 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-__version__ = '0.13.45'
+__version__ = '0.14'
 
+
+from . import algebra  # noqa
 from . import architectures  # noqa
+from . import callbacks  # noqa
 from . import classification_metrics  # noqa
+from . import distances  # noqa
 from . import evaluators  # noqa
+from . import layers  # noqa
 from . import losses  # noqa
+from . import indexer  # noqa
 from . import matchers  # noqa
 from . import models  # noqa
 from . import retrieval_metrics  # noqa
 from . import samplers  # noqa
 from . import search  # noqa
 from . import stores  # noqa
 from . import training_metrics  # noqa
-from . import visualization  # noqa
-from . import algebra  # noqa
-from . import callbacks  # noqa
-from . import distances  # noqa
-from . import indexer  # noqa
-from . import layers  # noqa
 from . import types  # noqa
 from . import utils  # noqa
+from . import visualization  # noqa