I have installed tensorflow-lattice using pip 9.0.1 in Python 3.5.2 on Ubuntu 16.04 LTS. Tensorflow version is 1.3.1. For testing purpose I tried to execute example program

import tensorflow as tf
import tensorflow_lattice as tfl

x = tf.placeholder(tf.float32, shape=(None, 2))
(y, _, _, _) = tfl.lattice_layer(x, lattice_sizes=(2, 2))

with tf.Session() as sess:
  sess.run(tf.global_variables_initializer())
  print(sess.run(y, feed_dict={x: [[0.0, 0.0]]}))

which resulted in error. Here is stack trace from Jupyter notebook

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-7-444d1bededea> in <module>()
----> 1 (y, _, _, _) = tfl.lattice_layer(x, lattice_sizes=(2, 2))

/usr/local/lib/python3.5/dist-packages/tensorflow_lattice/python/lib/lattice_layers.py in lattice_layer(input_tensor, lattice_sizes, is_monotone, output_dim, interpolation_type, lattice_initializer, l1_reg, l2_reg, l1_torsion_reg, l2_torsion_reg, l1_laplacian_reg, l2_laplacian_reg)
    193   parameter_tensor = variable_scope.get_variable(
    194       interpolation_type + '_lattice_parameters',
--> 195       initializer=lattice_initializer)
    196 
    197   output_tensor = lattice_ops.lattice(

/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py in get_variable(name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter)
   1063       collections=collections, caching_device=caching_device,
   1064       partitioner=partitioner, validate_shape=validate_shape,
-> 1065       use_resource=use_resource, custom_getter=custom_getter)
   1066 get_variable_or_local_docstring = (
   1067     """%s

/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py in get_variable(self, var_store, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter)
    960           collections=collections, caching_device=caching_device,
    961           partitioner=partitioner, validate_shape=validate_shape,
--> 962           use_resource=use_resource, custom_getter=custom_getter)
    963 
    964   def _get_partitioned_variable(self,

/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py in get_variable(self, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter)
    365           reuse=reuse, trainable=trainable, collections=collections,
    366           caching_device=caching_device, partitioner=partitioner,
--> 367           validate_shape=validate_shape, use_resource=use_resource)
    368 
    369   def _get_partitioned_variable(

/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py in _true_getter(name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource)
    350           trainable=trainable, collections=collections,
    351           caching_device=caching_device, validate_shape=validate_shape,
--> 352           use_resource=use_resource)
    353 
    354     if custom_getter is not None:

/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py in _get_single_variable(self, name, shape, dtype, initializer, regularizer, partition_info, reuse, trainable, collections, caching_device, validate_shape, use_resource)
    662                          " Did you mean to set reuse=True in VarScope? "
    663                          "Originally defined at:\n\n%s" % (
--> 664                              name, "".join(traceback.format_list(tb))))
    665       found_var = self._vars[name]
    666       if not shape.is_compatible_with(found_var.get_shape()):

ValueError: Variable hypercube_lattice_parameters already exists, disallowed. Did you mean to set reuse=True in VarScope? Originally defined at:

  File "/usr/local/lib/python3.5/dist-packages/tensorflow_lattice/python/lib/lattice_layers.py", line 195, in lattice_layer
    initializer=lattice_initializer)
  File "<ipython-input-1-e860b057ec64>", line 5, in <module>
    (y, _, _, _) = tfl.lattice_layer(x, lattice_sizes=(2, 2))
  File "/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py", line 2847, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)

I just installed tensorflow-lattice on a MacOS but got the following importing error. Do you know what's happening here?

Python 3.6.8 |Anaconda, Inc.| (default, Dec 29 2018, 19:04:46) [GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)] on darwin Type "help", "copyright", "credits" or "license" for more information.

import tensorflow_lattice /anaconda3/envs/tf_lattice/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_qint8 = np.dtype([("qint8", np.int8, 1)]) /anaconda3/envs/tf_lattice/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_quint8 = np.dtype([("quint8", np.uint8, 1)]) /anaconda3/envs/tf_lattice/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_qint16 = np.dtype([("qint16", np.int16, 1)]) /anaconda3/envs/tf_lattice/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_quint16 = np.dtype([("quint16", np.uint16, 1)]) /anaconda3/envs/tf_lattice/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_qint32 = np.dtype([("qint32", np.int32, 1)]) /anaconda3/envs/tf_lattice/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. np_resource = np.dtype([("resource", np.ubyte, 1)]) /anaconda3/envs/tf_lattice/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_qint8 = np.dtype([("qint8", np.int8, 1)]) /anaconda3/envs/tf_lattice/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_quint8 = np.dtype([("quint8", np.uint8, 1)]) /anaconda3/envs/tf_lattice/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_qint16 = np.dtype([("qint16", np.int16, 1)]) /anaconda3/envs/tf_lattice/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_quint16 = np.dtype([("quint16", np.uint16, 1)]) /anaconda3/envs/tf_lattice/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_qint32 = np.dtype([("qint32", np.int32, 1)]) /anaconda3/envs/tf_lattice/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. np_resource = np.dtype([("resource", np.ubyte, 1)]) Traceback (most recent call last): File "", line 1, in File "/anaconda3/envs/tf_lattice/lib/python3.6/site-packages/tensorflow_lattice/init.py", line 33, in from tensorflow_lattice.python.estimators.calibrated import input_calibration_layer_from_hparams File "/anaconda3/envs/tf_lattice/lib/python3.6/site-packages/tensorflow_lattice/python/estimators/calibrated.py", line 28, in from tensorflow_lattice.python.lib import pwl_calibration_layers File "/anaconda3/envs/tf_lattice/lib/python3.6/site-packages/tensorflow_lattice/python/lib/pwl_calibration_layers.py", line 36, in from tensorflow_lattice.python.ops import pwl_calibration_ops File "/anaconda3/envs/tf_lattice/lib/python3.6/site-packages/tensorflow_lattice/python/ops/pwl_calibration_ops.py", line 45, in '../../cc/ops/_pwl_calibration_ops.so')) File "/anaconda3/envs/tf_lattice/lib/python3.6/site-packages/tensorflow/python/framework/load_library.py", line 61, in load_op_library lib_handle = py_tf.TF_LoadLibrary(library_filename) tensorflow.python.framework.errors_impl.NotFoundError: dlopen(/anaconda3/envs/tf_lattice/lib/python3.6/site-packages/tensorflow_lattice/python/ops/../../cc/ops/_pwl_calibration_ops.so, 6): image not found

Saving the model having keras tfl layers creates the following problem.

`/usr/local/lib/python3.6/dist-packages/h5py/_hl/group.py in setitem(self, name, obj) 371 372 if isinstance(obj, HLObject): --> 373 h5o.link(obj.id, self.id, name, lcpl=lcpl, lapl=self._lapl) 374 375 elif isinstance(obj, SoftLink):

h5py/_objects.pyx in h5py._objects.with_phil.wrapper()

h5py/_objects.pyx in h5py._objects.with_phil.wrapper()

h5py/h5o.pyx in h5py.h5o.link()

RuntimeError: Unable to create link (name already exists)`

The error is reproduced in the colab example here. https://colab.research.google.com/drive/1tknejj9CtM27bHGktsZSTnvLvH3eCgG8

Is there any plan for tensorflow 2.0 release? I heard that the eager mode will be default in TF 2.0, but not sure TF lattice is ready for it.

Thank you!

The package name is scikit-learn while the import is sklearn.

See https://pypi.org/project/sklearn/ for the official recommendation.

Requiring sklearn can lead to subtle problems as explained in https://github.com/scikit-learn/scikit-learn/issues/8215 .

SGD or Adagrad with a square root decay learning rate schedule can help training. Since TensorFlow lattice estimators accepts arbitrary callable as an optimizer, so we can use learning rate scheduling, but it is not easy to configure for a beginner. This pull requests include some helper functions and tests to illustrate how to use a custom learning rate schedule.

Also this pull requests include modify keep_dims argument to keepdims in linear model construction since tensorflow is deprecating a former argument.

I installed tensorflow lattice with TF 2.3.0 but when trying to run I receive a conversion error and an attribute error "gast has no attribute 'Index'"

I tried to install three versions of tensorflow lattice (0.9.9, 2.0, and 2.0.8) but recieved the same error and I could not install an older version of gast (currently running 0.4.0) due to the dependencies of my current setup.

Please let me know if you have additional recommendations. Thank you!

I was running the uci_census.py file, with the create_calibrated_lattice function. When parameter lattice_size is set to 2, the program can run successfully. However, when the parameter is set to 3 (also 4 or other values, which I have not tested yet), the program will crash with the following error:

2018-06-17 19:54:25.814852: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.2 AVX AVX2 FMA
Traceback (most recent call last):
  File "uci_census.py", line 616, in <module>
    run()
  File "uci_census.py", line 609, in run
    main(argv)
  File "uci_census.py", line 586, in main
    train(estimator)
  File "uci_census.py", line 550, in train
    batch_size=FLAGS.batch_size, num_epochs=epochs, shuffle=True))
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 314, in train
    loss = self._train_model(input_fn, hooks, saving_listeners)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 812, in _train_model
    log_step_count_steps=self._config.log_step_count_steps) as mon_sess:
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 380, in MonitoredTrainingSession
    stop_grace_period_secs=stop_grace_period_secs)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 787, in __init__
    stop_grace_period_secs=stop_grace_period_secs)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 511, in __init__
    self._sess = _RecoverableSession(self._coordinated_creator)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 972, in __init__
    _WrappedSession.__init__(self, self._create_session())
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 977, in _create_session
    return self._sess_creator.create_session()
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 668, in create_session
    self.tf_sess = self._session_creator.create_session()
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 440, in create_session
    init_fn=self._scaffold.init_fn)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/session_manager.py", line 273, in prepare_session
    config=config)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/session_manager.py", line 205, in _restore_checkpoint
    saver.restore(sess, ckpt.model_checkpoint_path)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1686, in restore
    {self.saver_def.filename_tensor_name: save_path})
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 895, in run
    run_metadata_ptr)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1128, in _run
    feed_dict_tensor, options, run_metadata)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1344, in _do_run
    options, run_metadata)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1363, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Assign requires shapes of both tensors to match. lhs shape= [1,1594323] rhs shape= [1,8192]
	 [[Node: save/Assign_3 = Assign[T=DT_FLOAT, _class=["loc:@calibrated_tf_lattice_model/lattice/hypercube_lattice_parameters"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](calibrated_tf_lattice_model/lattice/calibrated_tf_lattice_model/lattice/hypercube_lattice_parameters/Adam_1, save/RestoreV2_3)]]

Caused by op u'save/Assign_3', defined at:
  File "uci_census.py", line 616, in <module>
    run()
  File "uci_census.py", line 609, in run
    main(argv)
  File "uci_census.py", line 586, in main
    train(estimator)
  File "uci_census.py", line 550, in train
    batch_size=FLAGS.batch_size, num_epochs=epochs, shuffle=True))
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 314, in train
    loss = self._train_model(input_fn, hooks, saving_listeners)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 812, in _train_model
    log_step_count_steps=self._config.log_step_count_steps) as mon_sess:
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 380, in MonitoredTrainingSession
    stop_grace_period_secs=stop_grace_period_secs)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 787, in __init__
    stop_grace_period_secs=stop_grace_period_secs)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 511, in __init__
    self._sess = _RecoverableSession(self._coordinated_creator)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 972, in __init__
    _WrappedSession.__init__(self, self._create_session())
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 977, in _create_session
    return self._sess_creator.create_session()
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 668, in create_session
    self.tf_sess = self._session_creator.create_session()
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 431, in create_session
    self._scaffold.finalize()
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.py", line 212, in finalize
    self._saver.build()
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1248, in build
    self._build(self._filename, build_save=True, build_restore=True)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1284, in _build
    build_save=build_save, build_restore=build_restore)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 759, in _build_internal
    restore_sequentially, reshape)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 471, in _AddShardedRestoreOps
    name="restore_shard"))
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 440, in _AddRestoreOps
    assign_ops.append(saveable.restore(tensors, shapes))
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 160, in restore
    self.op.get_shape().is_fully_defined())
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/state_ops.py", line 276, in assign
    validate_shape=validate_shape)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/gen_state_ops.py", line 59, in assign
    use_locking=use_locking, name=name)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3160, in create_op
    op_def=op_def)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1625, in __init__
    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

InvalidArgumentError (see above for traceback): Assign requires shapes of both tensors to match. lhs shape= [1,1594323] rhs shape= [1,8192]
	 [[Node: save/Assign_3 = Assign[T=DT_FLOAT, _class=["loc:@calibrated_tf_lattice_model/lattice/hypercube_lattice_parameters"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](calibrated_tf_lattice_model/lattice/calibrated_tf_lattice_model/lattice/hypercube_lattice_parameters/Adam_1, save/RestoreV2_3)]]

IMO, the point should be this line: Assign requires shapes of both tensors to match. lhs shape= [1,1594323] rhs shape= [1,8192], in which 1594323 = 3^13 and 8192 = 2^13. Here 13 is the number of features used in this example, and 3 is the lattice_size we defined. Could anyone help me with this?

Currently when trying to serve a lattice model with tf serving, I run into an op that isn't supported in the serving kernel

... 2018-03-01 23:45:59.827196: I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:284] Loading SavedModel: fail. Took 429225 microseconds. 2018-03-01 23:45:59.828892: E tensorflow_serving/util/retrier.cc:38] Loading servable: {name: default version: 1519947547} failed: Not found: Op type not registered 'PwlIndexingCalibrator' in binary running on dsexperiment-prod-0fe24ce9bf2552633. Make sure the Op and Kernel are registered in the binary running in this process.

Both tf and tf-serving on the system are at version 1.5.0. Are lattice models not supported with serving yet ? If they are, could you point me to how to make it happen.

Hi,

I wonder if you have a functionality to specify that the target function should be convex by pieces, and/or monotonic by pieces.

Thanks for writing this amazing piece of software :) Matias

It is so great to see such a good package. However the speed is too slow.

I am using Crystal ensemble model config. tfl.estimators.CannedRegressor estimator. It seems only one CPU is using, though I have 48 CPUs on the machine.

I have set the dataset with multiple threads:

feature_analysis_input_fn = tf.compat.v1.estimator.inputs.pandas_input_fn(
    x=train_xs.loc[feature_analysis_index].copy(), 
    y=train_ys.loc[feature_analysis_index].copy(), 
    batch_size=128, 
    num_epochs=1, 
    shuffle=True, 
    queue_capacity=1000,
    num_threads=40)

prefitting_input_fn = tf.compat.v1.estimator.inputs.pandas_input_fn(
    x=train_xs.loc[prefitting_index].copy(), 
    y=train_ys.loc[prefitting_index].copy(), 
    batch_size=128, 
    num_epochs=1, 
    shuffle=True, 
    queue_capacity=1000,
    num_threads=40)

train_input_fn = tf.compat.v1.estimator.inputs.pandas_input_fn(
    x=train_xs.loc[train_index].copy(), 
    y=train_ys.loc[train_index].copy(), 
    batch_size=128, 
    num_epochs=100, 
    shuffle=True, 
    queue_capacity=1000,
    num_threads=40)

The usage of CPU is still only 1.25 CPU. Any suggestion?

Hi, I'm looking at the shape constraints tutorial, and the results for GBDT and DNN are listed in the tutorial as follows:

• GBT Validation AUC: 0.7248634099960327
• GBT Test AUC: 0.6980501413345337
• DNN Validation AUC: 0.7518489956855774
• DNN Testing AUC: 0.745200514793396

After the experiment results, the tutorial comments Note that even though the validation metric is better than the tree solution, the testing metric is much worse.

I don't understand where this comment comes from, since DNN outperforms GBT in both validation AUC and testing AUC.

Hi,

When trying to get predictions of Lattice Models on more than one batch of data at once, Errors are raised. This is a nice feature to efficiently get predictions, and is present in basic Neural Network Keras models; find some examples in this colab.

As far as I can tell from looking at API docs + source code, this should be related to the inputs admitted by PWC layers, but I wonder if there is an easy way around.

In particular, this piece of code captures what I would like to get (and retrieves an error when calling on batched_inputs):

class LatticeModel(tf.keras.Model):
    def __init__(self, nodes=[2,2], nkeypoints=100):
        super(LatticeModel,self).__init__()
        self.combined_calibrators = tfl.layers.ParallelCombination()
        for ind,i in enumerate(range(2)):
          calibration_layer = tfl.layers.PWLCalibration(input_keypoints=np.linspace(0,1,nkeypoints),output_min=0.0, output_max=nodes[ind])
          self.combined_calibrators.append(calibration_layer)
        self.lattice = tfl.layers.Lattice(lattice_sizes=nodes,interpolation="simplex")
        
    def call(self, x):
        rescaled = self.combined_calibrators(x)
        feat = self.lattice(rescaled)
        return feat
    
#we define some input data
x1 = np.random.randn(100,1).astype(np.float32)
x2 = np.random.randn(100,1).astype(np.float32)

inputs = tf.concat([x1,x2], axis=-1)

#we initialize out model, and feed it with a batch of size 100
model = LatticeModel()
model(inputs)

### now we would like to efficiently predict the output of the lattice model on many batches of data at once (in this case 2)
batched_inputs = np.random.randn(2,100,1)
model(batched_inputs)

Thanks a lot! Matías.

I've written custom parsers and emitters for everything from docstrings to classes and functions. However, I recently came across an issue with the TensorFlow codebase: inconsistent use of Args: and Arguments: in its docstrings. It is easy enough to extend my parsers to support both variants, however it looks like Arguments: is wrong anyway, as per:

• https://google.github.io/styleguide/pyguide.html#doc-function-args @ ddccc0f

• https://chromium.googlesource.com/chromiumos/docs/+/master/styleguide/python.md#describing-arguments-in-docstrings @ 9fc0fc0

• https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html @ c0ae8e3

Therefore, only Args: is valid. This PR replaces them throughout the codebase.

PS: For related PRs, see tensorflow/tensorflow/pull/45420

First off - Thank you so much for open sourcing Tensorflow lattice! It is great to make use of lattice interpolation to enforce predicate domain knowledge concerning monotonicity and convexity. Looking through the current documentation, I see it is possible to enforce an increasing and concave graph for diminishing returns, but what if I want to enforce an S-curve (i.e. an increasing convex curve with an inflection point that then turns concave)?

https://github.com/tensorflow/lattice/blob/master/docs/overview.md#tutorials-and-api-docs points to https://github.com/tensorflow/lattice/blob/master/docs/api_docs/python/tfl.ipynb