Support for transform=None

CHANGES.rst

@@ -18,7 +18,7 @@ Release history
    - Deprecated
    - Removed
 
-3.0.1 (unreleased)
+3.1.0 (unreleased)
 ------------------
 
 **Added**
@@ -41,6 +41,10 @@ Release history
   ``nengo_dl.configure_settings(dtype=...)`` config option. Note that this will
   override the default precision set in ``nengo.rc``. (`#119`_)
 - Minimum Numpy version is now 1.16.0 (required by TensorFlow). (`#119`_)
+- Added support for the new ``transform=None`` default in Nengo connections
+  (see `Nengo#1591`_). Note that this may change the number of trainable
+  parameters in a network as the scalar default ``transform=1`` weights on
+  non-Ensemble connections will no longer be present. (`#128`_)
 
 **Fixed**
 
@@ -55,7 +59,9 @@ Release history
 - Fixed compatibility with ``progressbar2`` version 3.50.0. (`#136`_)
 
 .. _#119: https://github.com/nengo/nengo-dl/pull/119
+.. _#128: https://github.com/nengo/nengo-dl/pull/128
 .. _#136: https://github.com/nengo/nengo-dl/pull/136
+.. _Nengo#1591: https://github.com/nengo/nengo/pull/1591
 
 3.0.0 (December 17, 2019)
 -------------------------

nengo_dl/callbacks.py

@@ -17,7 +17,7 @@
 import tensorflow as tf
 from tensorflow.python.eager import context
 
-from nengo_dl import utils
+from nengo_dl import compat, utils
 
 
 class NengoSummaries(tf.keras.callbacks.Callback):
@@ -61,6 +61,11 @@ def __init__(self, log_dir, sim, objects):
                     param = "bias"
                     name = "Ensemble.neurons_%s" % obj.ensemble.label
                 elif isinstance(obj, nengo.Connection):
+                    if not compat.conn_has_weights(obj):
+                        raise ValidationError(
+                            "Connection '%s' does not have any weights to log" % obj,
+                            "objects",
+                        )
                     param = "weights"
                     name = "Connection_%s" % obj.label
 

nengo_dl/compat.py

@@ -5,6 +5,9 @@
 dependencies.
 """
 
+from distutils.version import LooseVersion
+
+import nengo
 from nengo._vendor.scipy.sparse import linalg_interface, linalg_onenormest
 import tensorflow as tf
 
@@ -75,3 +78,18 @@ def filter(self, record):
 
 # monkeypatch fix for https://github.com/nengo/nengo/pull/1587
 linalg_onenormest.aslinearoperator = linalg_interface.aslinearoperator
+
+if LooseVersion(nengo.__version__) < "3.1.0":
+    default_transform = 1
+
+    def conn_has_weights(conn):
+        """All connections have weights."""
+        return True
+
+
+else:
+    default_transform = None
+
+    def conn_has_weights(conn):
+        """Equivalent to conn.has_weights."""
+        return conn.has_weights

nengo_dl/losses.py

@@ -41,7 +41,7 @@ class Regularize(tf.losses.Loss):
         with nengo.Network() as net:
             a = nengo.Node([0])
             b = nengo.Node(size_in=1)
-            c = nengo.Connection(a, b)
+            c = nengo.Connection(a, b, transform=1)
             p = nengo.Probe(c, "weights")
             ...
 

nengo_dl/simulator.py

@@ -30,7 +30,7 @@
 import tensorflow as tf
 from tensorflow.python.keras import backend
 
-from nengo_dl import utils, config, callbacks
+from nengo_dl import callbacks, compat, config, utils
 from nengo_dl.builder import NengoBuilder, NengoModel
 from nengo_dl.tensor_graph import TensorGraph
 
@@ -1346,7 +1346,8 @@ def get_nengo_params(self, nengo_objs, as_dict=False):
         fetches = []
         for obj in nengo_objs:
             if isinstance(obj, Connection):
-                fetches.append((obj, "weights"))
+                if compat.conn_has_weights(obj):
+                    fetches.append((obj, "weights"))
             elif isinstance(obj, Ensemble):
                 if isinstance(obj.neuron_type, Direct):
                     # we cannot transfer direct ensemble parameters, because
@@ -1372,6 +1373,10 @@ def get_nengo_params(self, nengo_objs, as_dict=False):
         idx = 0
         for obj in nengo_objs:
             if isinstance(obj, Connection):
+                if not compat.conn_has_weights(obj):
+                    params.append({"transform": None})
+                    continue
+
                 weights = data[idx]
                 idx += 1
                 if isinstance(obj.pre_obj, Ensemble):
@@ -1381,7 +1386,7 @@ def get_nengo_params(self, nengo_objs, as_dict=False):
                             "function": lambda x, weights=weights: np.zeros(
                                 weights.shape[0]
                             ),
-                            "transform": 1,
+                            "transform": compat.default_transform,
                         }
                     )
                 elif isinstance(obj.transform, Convolution):
@@ -2104,7 +2109,11 @@ def __getitem__(self, obj):
             )
         elif isinstance(obj, Connection):
             # get the live simulation values
-            weights = self.get_params((obj, "weights"))[0]
+            weights = (
+                self.get_params((obj, "weights"))[0]
+                if compat.conn_has_weights(obj)
+                else None
+            )
 
             # impossible to recover transform
             transform = None

nengo_dl/tensor_graph.py

@@ -19,7 +19,15 @@
 from tensorflow.python.eager import context
 from tensorflow.python.training.tracking import base as trackable
 
-from nengo_dl import builder, graph_optimizer, signals, utils, tensor_node, config
+from nengo_dl import (
+    builder,
+    config,
+    compat,
+    graph_optimizer,
+    tensor_node,
+    signals,
+    utils,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -856,10 +864,11 @@ def mark_network(parent_configs, net):
             for conn in net.connections:
                 # note: this doesn't include probe connections, since they
                 # aren't added to the network
-                self.model.sig[conn]["weights"].trainable = get_trainable(
-                    parent_configs, conn
-                )
-                self.model.sig[conn]["weights"].minibatched = False
+                if compat.conn_has_weights(conn):
+                    self.model.sig[conn]["weights"].trainable = get_trainable(
+                        parent_configs, conn
+                    )
+                    self.model.sig[conn]["weights"].minibatched = False
 
             # parameters can't be modified by an online Nengo learning rule
             # and offline training at the same time. (it is possible in
@@ -909,8 +918,9 @@ def mark_network(parent_configs, net):
             probe_seeds = [self.model.seeds[p] for p in self.model.probes]
             for obj, seed in self.model.seeds.items():
                 if isinstance(obj, Connection) and seed in probe_seeds:
-                    self.model.sig[obj]["weights"].trainable = False
-                    self.model.sig[obj]["weights"].minibatched = False
+                    if compat.conn_has_weights(obj):
+                        self.model.sig[obj]["weights"].trainable = False
+                        self.model.sig[obj]["weights"].minibatched = False
 
         # time/step are not minibatched and not trainable
         self.model.step.trainable = False

nengo_dl/tensor_node.py

@@ -20,6 +20,7 @@
 import tensorflow as tf
 
 from nengo_dl.builder import Builder, OpBuilder, NengoBuilder
+from nengo_dl.compat import default_transform
 from nengo_dl.config import configure_settings
 
 
@@ -403,7 +404,7 @@ def __init__(self, layer_func):
     def __call__(
         self,
         input,
-        transform=1,
+        transform=default_transform,
         shape_in=None,
         synapse=None,
         return_conn=False,

nengo_dl/tests/dummies.py

@@ -157,7 +157,7 @@ def linear_net():
     with nengo.Network() as net:
         a = nengo.Node([1])
         b = nengo.Node(size_in=1)
-        nengo.Connection(a, b, synapse=None)
+        nengo.Connection(a, b, synapse=None, transform=1)
         p = nengo.Probe(b)
 
     return net, a, p

nengo_dl/tests/test_benchmarks.py

@@ -192,8 +192,8 @@ def test_lmu(Simulator, native_nengo, pytestconfig):
     [
         (benchmarks.cconv(128, 64, nengo.RectifiedLinear()), False, 64, 0.65, 0.8),
         (benchmarks.cconv(128, 64, nengo.LIF()), False, 64, 1.45, 1.65),
-        (benchmarks.integrator(128, 32, nengo.RectifiedLinear()), True, 64, 0.5, 0.75),
-        (benchmarks.integrator(128, 32, nengo.LIF()), True, 64, 0.9, 1.2),
+        (benchmarks.integrator(128, 32, nengo.RectifiedLinear()), True, 64, 0.6, 1.0),
+        (benchmarks.integrator(128, 32, nengo.LIF()), True, 64, 1.1, 1.4),
         (
             benchmarks.random_network(
                 64,
@@ -208,8 +208,7 @@ def test_lmu(Simulator, native_nengo, pytestconfig):
             0.35,
             0.55,
         ),
-        (benchmarks.lmu(1000, 1, native_nengo=True), True, 100, 0.75, 1.05),
-        # (benchmarks.spaun(1), False, None, 8.02, 9.52),
+        (benchmarks.lmu(1000, 1, native_nengo=True), True, 100, 0.85, 1.15),
     ],
 )
 def test_performance(net, train, minibatch_size, min, max):
@@ -218,8 +217,8 @@ def test_performance(net, train, minibatch_size, min, max):
     # GPU: GeForce GTX Titan X
     # Python version: 3.6.8
     # TensorFlow GPU version: 2.0.0
-    # Nengo version: 3.0.0
-    # NengoDL version: 3.0.0
+    # Nengo version: 3.1.0
+    # NengoDL version: 3.1.0
 
     time = benchmarks.run_profile(
         net,

nengo_dl/tests/test_keras.py

@@ -236,8 +236,8 @@ def test_fit(Simulator, seed):
         inp_a = nengo.Node([0])
         inp_b = nengo.Node([0])
         inp = nengo.Node(size_in=2)
-        nengo.Connection(inp_a, inp[0])
-        nengo.Connection(inp_b, inp[1])
+        nengo.Connection(inp_a, inp[0], transform=1)
+        nengo.Connection(inp_b, inp[1], transform=1)
 
         ens = nengo.Ensemble(
             n_hidden + 1, n_hidden, neuron_type=nengo.Sigmoid(tau_ref=1)

nengo_dl/tests/test_simulator.py

@@ -1,6 +1,7 @@
 # pylint: disable=missing-docstring
 
 from collections import OrderedDict
+from distutils.version import LooseVersion
 import logging
 import os
 import pickle
@@ -20,7 +21,7 @@
 from tensorflow.core.util import event_pb2
 
 from nengo_dl import Layer, TensorNode, callbacks, configure_settings, dists, utils
-from nengo_dl.compat import TFLogFilter
+from nengo_dl.compat import TFLogFilter, default_transform
 from nengo_dl.simulator import SimulationData
 from nengo_dl.tests import dummies
 
@@ -167,8 +168,8 @@ def test_train_ff(Simulator, neurons, use_loop, seed):
         inp_a = nengo.Node([0])
         inp_b = nengo.Node([0])
         inp = nengo.Node(size_in=2)
-        nengo.Connection(inp_a, inp[0])
-        nengo.Connection(inp_b, inp[1])
+        nengo.Connection(inp_a, inp[0], transform=1)
+        nengo.Connection(inp_b, inp[1], transform=1)
 
         ens = nengo.Ensemble(
             n_hidden + 1, n_hidden, neuron_type=nengo.Sigmoid(tau_ref=1)
@@ -300,11 +301,11 @@ def test_train_objective(Simulator, unroll, seed):
         inp = nengo.Node([1])
 
         ens = nengo.Ensemble(n_hidden, 1, neuron_type=nengo.RectifiedLinear())
-        nengo.Connection(inp, ens, synapse=0.01)
+        nengo.Connection(inp, ens, synapse=0.01, transform=1)
         p = nengo.Probe(ens)
 
         ens2 = nengo.Ensemble(n_hidden, 1, neuron_type=nengo.RectifiedLinear())
-        nengo.Connection(inp, ens2, synapse=0.01)
+        nengo.Connection(inp, ens2, synapse=0.01, transform=1)
         p2 = nengo.Probe(ens2)
 
     with Simulator(
@@ -688,7 +689,8 @@ def test_tensorboard(Simulator, tmpdir):
     with nengo.Network() as net:
         a = nengo.Node([0])
         b = nengo.Ensemble(10, 1, neuron_type=nengo.LIFRate())
-        c = nengo.Connection(a, b)
+        c = nengo.Connection(a, b, transform=1)
+        c0 = nengo.Connection(a, b)
         p = nengo.Probe(b)
         p2 = nengo.Probe(c)
 
@@ -768,6 +770,10 @@ def test_tensorboard(Simulator, tmpdir):
     with pytest.raises(ValidationError, match="Unknown summary object"):
         callbacks.NengoSummaries(log_dir=log_dir + "/nengo", sim=sim, objects=[a])
 
+    if LooseVersion(nengo.__version__) >= "3.1.0":
+        with pytest.raises(ValidationError, match="does not have any weights"):
+            callbacks.NengoSummaries(log_dir=log_dir + "/nengo", sim=sim, objects=[c0])
+
 
 @pytest.mark.parametrize("mode", ("predict", "train"))
 @pytest.mark.training
@@ -1259,7 +1265,7 @@ def test_get_nengo_params(Simulator, seed):
 
         # check that single objects are returned as single dicts
         params = sim.get_nengo_params(d)
-        assert params["transform"] == 1
+        assert params["transform"] is default_transform
 
         fetches = [a.neurons, b, c, d, e, h]
 
@@ -1290,7 +1296,7 @@ def test_direct_grads(Simulator, mixed):
     if mixed:
         with net:
             c = nengo.Node(size_in=1)
-            nengo.Connection(a, c, synapse=None)
+            nengo.Connection(a, c, synapse=None, transform=1)
             p2 = nengo.Probe(c)
 
     with Simulator(net, minibatch_size=1) as sim:
@@ -1328,7 +1334,7 @@ def test_non_differentiable(Simulator):
     with nengo.Network() as net:
         a = nengo.Node([0])
         b = nengo.Node(lambda t, x: x, size_in=1)
-        c = nengo.Connection(a, b)
+        c = nengo.Connection(a, b, transform=1)
         p = nengo.Probe(b)
 
     with Simulator(net) as sim:
@@ -1429,7 +1435,7 @@ def test_inference_only(Simulator, neuron_type, seed):
 
         a = nengo.Node([0])
         b = nengo.Ensemble(10, 1, neuron_type=neuron_type)
-        c = nengo.Connection(a, b, synapse=None)
+        c = nengo.Connection(a, b, synapse=None, transform=1)
         p = nengo.Probe(b)
 
     with Simulator(net) as sim:

nengo_dl/tests/test_tensor_node.py

@@ -1,5 +1,6 @@
 # pylint: disable=missing-docstring
 
+from distutils.version import LooseVersion
 from functools import partial
 
 import nengo
@@ -261,12 +262,19 @@ def call(self, x):
         assert np.allclose(sim.data[p2], 3)
 
         # note: when inference-only=True the weights will be marked as non-trainable
+
+        default_conn_params = 2 if LooseVersion(nengo.__version__) < "3.1.0" else 0
+
         if sim.tensor_graph.inference_only:
-            assert len(sim.keras_model.non_trainable_variables) == 10
+            assert (
+                len(sim.keras_model.non_trainable_variables) == 8 + default_conn_params
+            )
             assert len(sim.keras_model.trainable_variables) == 0
             vars = sim.keras_model.non_trainable_variables[-2:]
         else:
-            assert len(sim.keras_model.non_trainable_variables) == 8
+            assert (
+                len(sim.keras_model.non_trainable_variables) == 6 + default_conn_params
+            )
             assert len(sim.keras_model.trainable_variables) == 2
             vars = sim.keras_model.trainable_variables