Removes unnecessary ViT-GP hyper-parameters.

Due to [pull #489](#489) to `edward2.jax.nn.RandomFeatureGaussianProcess`. Some of the special hyper-parameter configs are no longer needed. Therefore we remove them to simplify the model API.

PiperOrigin-RevId: 388484029

Author: jereliu

edward2/jax/nn/random_feature.py

@@ -27,6 +27,9 @@
 [3]: Ali Rahimi and Benjamin Recht. Random Features for Large-Scale Kernel
      Machines. In _Neural Information Processing Systems_, 2007.
      https://people.eecs.berkeley.edu/~brecht/papers/07.rah.rec.nips.pdf
+[4]: Zhiyun Lu, Eugene Ie, Fei Sha. Uncertainty Estimation with Infinitesimal
+     Jackknife.  _arXiv preprint arXiv:2006.07584_, 2020.
+     https://arxiv.org/abs/2006.07584
 """
 import dataclasses
 import functools

edward2/jax/nn/utils.py

@@ -15,11 +15,12 @@
 
 """JAX layer and utils."""
 
-from typing import Iterable, Callable
+from typing import Callable, Iterable, Optional
 
 from jax import random
 import jax.numpy as jnp
 
+Array = jnp.ndarray
 DType = type(jnp.float32)
 InitializeFn = Callable[[jnp.ndarray, Iterable[int], DType], jnp.ndarray]
 
@@ -48,3 +49,55 @@ def initializer(key, shape, dtype=jnp.float32):
       x = random.normal(key, shape, dtype) * (-random_sign_init) + 1.0
       return x.astype(dtype)
     return initializer
+
+
+def mean_field_logits(logits: Array,
+                      covmat: Optional[Array] = None,
+                      mean_field_factor: float = 1.,
+                      likelihood: str = 'logistic'):
+  """Adjust the model logits so its softmax approximates the posterior mean [4].
+
+  Arguments:
+    logits: A float ndarray of shape (batch_size, num_classes).
+    covmat: A float ndarray of shape (batch_size, ). If None then it is assumed
+      to be a vector of 1.'s.
+    mean_field_factor: The scale factor for mean-field approximation, used to
+      adjust the influence of posterior variance in posterior mean
+      approximation. If covmat=None then it is used as the scaling parameter for
+      temperature scaling.
+    likelihood: name of the likelihood for integration in Gaussian-approximated
+      latent posterior. Must be one of ('logistic', 'binary_logistic',
+      'poisson').
+
+  Returns:
+    A float ndarray of uncertainty-adjusted logits, shape
+    (batch_size, num_classes).
+
+  Raises:
+    (ValueError) If likelihood is not one of ('logistic', 'binary_logistic',
+    'poisson').
+  """
+  if likelihood not in ('logistic', 'binary_logistic', 'poisson'):
+    raise ValueError(
+        f'Likelihood" must be one of (\'logistic\', \'binary_logistic\', \'poisson\'), got {likelihood}.'
+    )
+
+  if mean_field_factor < 0:
+    return logits
+
+  # Defines predictive variance.
+  variances = 1. if covmat is None else covmat
+
+  # Computes scaling coefficient for mean-field approximation.
+  if likelihood == 'poisson':
+    logits_scale = jnp.exp(-variances * mean_field_factor / 2.)  # pylint:disable=invalid-unary-operand-type
+  else:
+    logits_scale = jnp.sqrt(1. + variances * mean_field_factor)
+
+  # Pads logits_scale to compatible dimension.
+  while logits_scale.ndim < logits.ndim:
+    logits_scale = jnp.expand_dims(logits_scale, axis=-1)
+
+  return logits / logits_scale
+
+

edward2/jax/nn/utils_test.py

@@ -0,0 +1,75 @@
+# coding=utf-8
+# Copyright 2021 The Edward2 Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+"""Tests for utils."""
+from absl.testing import absltest
+from absl.testing import parameterized
+
+import edward2.jax as ed
+
+import jax
+import jax.numpy as jnp
+
+import numpy as np
+import tensorflow as tf
+
+
+class MeanFieldLogitsTest(parameterized.TestCase, tf.test.TestCase):
+
+  def testMeanFieldLogitsLikelihood(self):
+    """Tests if scaling is correct under different likelihood."""
+    batch_size = 10
+    num_classes = 12
+    variance = 1.5
+    mean_field_factor = 2.
+
+    rng_key = jax.random.PRNGKey(0)
+    logits = jax.random.normal(rng_key, (batch_size, num_classes))
+    covmat = jnp.ones(batch_size) * variance
+
+    logits_logistic = ed.nn.utils.mean_field_logits(
+        logits, covmat, mean_field_factor=mean_field_factor)
+    logits_poisson = ed.nn.utils.mean_field_logits(
+        logits,
+        covmat,
+        mean_field_factor=mean_field_factor,
+        likelihood='poisson')
+
+    self.assertAllClose(logits_logistic, logits / 2., atol=1e-4)
+    self.assertAllClose(logits_poisson, logits * np.exp(1.5), atol=1e-4)
+
+  def testMeanFieldLogitsTemperatureScaling(self):
+    """Tests using mean_field_logits as temperature scaling method."""
+    batch_size = 10
+    num_classes = 12
+
+    rng_key = jax.random.PRNGKey(0)
+    logits = jax.random.normal(rng_key, (batch_size, num_classes))
+
+    # Test if there's no change to logits when mean_field_factor < 0.
+    logits_no_change = ed.nn.utils.mean_field_logits(
+        logits, covmat=None, mean_field_factor=-1)
+
+    # Test if mean_field_logits functions as a temperature scaling method when
+    # mean_field_factor > 0, with temperature = sqrt(1. + mean_field_factor).
+    logits_scale_by_two = ed.nn.utils.mean_field_logits(
+        logits, covmat=None, mean_field_factor=3.)
+
+    self.assertAllClose(logits_no_change, logits, atol=1e-4)
+    self.assertAllClose(logits_scale_by_two, logits / 2., atol=1e-4)
+
+
+if __name__ == '__main__':
+  absltest.main()