Gluon.probability

python/mxnet/gluon/__init__.py

@@ -40,3 +40,5 @@
 from . import model_zoo
 
 from . import contrib
+
+from . import probability

python/mxnet/gluon/probability/__init__.py

@@ -0,0 +1,26 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+
+# coding: utf-8
+# pylint: disable=wildcard-import
+"""Probability module"""
+
+from .block import *
+
+from .distributions import *
+
+from .transformation import *

python/mxnet/gluon/probability/block/__init__.py

@@ -0,0 +1,22 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+
+# coding: utf-8
+# pylint: disable=wildcard-import
+"""Stochastic block."""
+
+from .stochastic_block import *

python/mxnet/gluon/probability/block/stochastic_block.py

@@ -0,0 +1,127 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+
+# coding: utf-8
+# pylint: disable=abstract-method
+"""Stochastic block class."""
+__all__ = ['StochasticBlock', 'StochasticSequential']
+
+from functools import wraps
+from ...block import HybridBlock
+from ...utils import _indent
+
+
+class StochasticBlock(HybridBlock):
+    """`StochasticBlock` extends `HybridBlock` to support accumulating loss
+    in the forward phase, which is extremely useful in building Bayesian Neural Network,
+    where the loss function is composed of a classification loss and a KL loss.
+
+    """
+
+    def __init__(self, **kwargs):
+        super(StochasticBlock, self).__init__(**kwargs)
+        self._losses = []
+        self._losscache = []
+
+    def add_loss(self, loss):
+        self._losscache.append(loss)
+
+    @staticmethod
+    def collectLoss(func):
+        """To accumulate loss during the forward phase, one could first decorate
+        hybrid_forward with `StochasticBlock.collectLoss,
+        and then collect the loss tensor `x` by calling self.add_loss(x).
+        For example, in the following forward function,
+        we generate samples from a Gaussian parameterized by `loc` and `scale` and
+        accumulate the KL-divergence between it and its prior into the block's loss storage.:
+        @StochasticBlock.collectLoss
+        def hybrid_forward(self, F, loc, scale):
+            qz = mgp.Normal(loc, scale)
+            # prior
+            pz = mgp.Normal(F.np.zeros_like(loc), F.np.ones_like(scale))
+            self.add_loss(mgp.kl_divergence(qz, pz))
+            return qz.sample()
+        """
+        @wraps(func)
+        def inner(self, *args, **kwargs):
+            # Loss from hybrid_forward
+            func_out = func(self, *args, **kwargs)
+            collected_loss = self._losscache
+            self._losscache = []
+            return (func_out, collected_loss)
+
+        return inner
+
+    def __call__(self, *args, **kwargs):
+		# pylint: disable=arguments-differ
+        out = super().__call__(*args, **kwargs)
+        self._losses.extend(out[1])
+        return out[0]
+
+    @property
+    def losses(self):
+        return self._losses
+
+
+class StochasticSequential(StochasticBlock):
+    """Stack StochasticBlock sequentially.
+    """
+
+    def __init__(self, **kwargs):
+        super(StochasticSequential, self).__init__(**kwargs)
+        self._layers = []
+
+    def add(self, *blocks):
+        """Adds block on top of the stack."""
+        for block in blocks:
+            self._layers.append(block)
+            self.register_child(block)
+
+    @StochasticBlock.collectLoss
+    def forward(self, x, *args):
+        # pylint: disable=arguments-differ
+        for block in self._children.values():
+            x = block()(x, *args)
+            args = []
+            if isinstance(x, (tuple, list)):
+                args = x[1:]
+                x = x[0]
+        if args:
+            x = tuple([x] + list(args))
+        for block in self._layers:
+            if hasattr(block, '_losses'):
+                self.add_loss(block._losses)
+        return x
+
+    def __repr__(self):
+        s = '{name}(\n{modstr}\n)'
+        modstr = '\n'.join(['  ({key}): {block}'.format(key=key,
+                                                        block=_indent(block().__repr__(), 2))
+                            for key, block in self._children.items()])
+        return s.format(name=self.__class__.__name__, modstr=modstr)
+
+    def __getitem__(self, key):
+        layers = list(self._children.values())[key]
+        if isinstance(layers, list):
+            net = type(self)()
+            net.add(*(l() for l in layers))
+            return net
+        else:
+            return layers()
+
+    def __len__(self):
+        return len(self._children)

python/mxnet/gluon/probability/distributions/__init__.py

@@ -0,0 +1,86 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+
+# coding: utf-8
+# pylint: disable=wildcard-import
+"""Distribution classes."""
+
+from .distribution import *
+
+from .exp_family import *
+
+from .exponential import *
+
+from .weibull import *
+
+from .pareto import *
+
+from .uniform import *
+
+from .normal import *
+
+from .laplace import *
+
+from .cauchy import *
+
+from .half_cauchy import *
+
+from .poisson import *
+
+from .geometric import *
+
+from .negative_binomial import *
+
+from .gamma import *
+
+from .dirichlet import *
+
+from .beta import *
+
+from .chi2 import *
+
+from .fishersnedecor import *
+
+from .studentT import *
+
+from .half_normal import *
+
+from .independent import *
+
+from .bernoulli import *
+
+from .binomial import *
+
+from .relaxed_bernoulli import *
+
+from .gumbel import *
+
+from .categorical import *
+
+from .one_hot_categorical import *
+
+from .relaxed_one_hot_categorical import *
+
+from .multinomial import *
+
+from .multivariate_normal import *
+
+from .transformed_distribution import *
+
+from .divergence import *
+
+from .utils import *