Source code for nengo_dl.tensor_node

from nengo import Node, Connection, Ensemble, builder
from nengo.base import NengoObject
from nengo.builder.operator import Reset
from nengo.exceptions import ValidationError, SimulationError
from nengo.neurons import NeuronType
from nengo.params import Default, IntParam, Parameter
import numpy as np
import tensorflow as tf

from nengo_dl.builder import Builder, OpBuilder

class TensorFuncParam(Parameter):
    """Performs validation on the function passed to TensorNode, and sets
    ``size_out`` if necessary."""

    def __init__(self, name, readonly=False):
        super(TensorFuncParam, self).__init__(
            name, optional=False, readonly=readonly)

    def coerce(self, node, func):
        output = super(TensorFuncParam, self).coerce(node, func)

        if node.size_out is None:
            node.size_out = self.check_size_out(node, func)

        return output

    def check_size_out(self, node, func):
        if not callable(func):
            raise ValidationError("TensorNode output must be a function",
                        , obj=node)

        t, x = tf.constant(0.0), tf.zeros((1, node.size_in))
        args = (t, x) if node.size_in > 0 else (t,)
            result = func(*args)
        except Exception as e:
            raise ValidationError(
                "Calling TensorNode function with arguments %s produced an "
                "error:\n%s" % (args, e),, obj=node)

        if not isinstance(result, tf.Tensor):
            raise ValidationError("TensorNode function must return a Tensor",
                        , obj=node)

        if result.get_shape().ndims != 2:
            raise ValidationError("Node output must be a minibatched vector "
                                  "(got shape %s)" % result.get_shape(),
                        , obj=node)

        return result.get_shape()[1].value

[docs]class TensorNode(Node): """Inserts TensorFlow code into a Nengo model. A TensorNode operates in much the same way as a :class:`~nengo:nengo.Node`, except its inputs and outputs are defined using TensorFlow operations. The TensorFlow code is defined in a function or callable class (``tensor_func``). This function accepts the current simulation time as input, or the current simulation time and a Tensor ``x`` if ``node.size_in > 0``. ``x`` will have shape ``(sim.minibatch_size, node.size_in``), and the function should return a Tensor with shape ``(sim.minibatch_size, node.size_out)``. ``node.size_out`` will be inferred by calling the function once and checking the output, if it isn't set when the Node is created. If ``tensor_func`` has a ``pre_build`` attribute, that function will be called once when the model is constructed. This can be used to compute any constant values or set up variables -- things that don't need to execute every simulation timestep. Parameters ---------- tensor_func : callable a function that maps node inputs to outputs size_in : int, optional (Default: 0) the number of elements in the input vector size_out : int, optional (Default: None) the number of elements in the output vector (if None, value will be inferred by calling ``tensor_func``) label : str, optional (Default: None) a name for the node, used for debugging and visualization """ tensor_func = TensorFuncParam('tensor_func') size_in = IntParam('size_in', default=0, low=0, optional=True) size_out = IntParam('size_out', default=None, low=1, optional=True) def __init__(self, tensor_func, size_in=Default, size_out=Default, label=Default): # note: we bypass the Node constructor, because we don't want to # perform validation on `output` NengoObject.__init__(self, label=label, seed=None) self.size_in = size_in self.size_out = size_out self.tensor_func = tensor_func
@builder.Builder.register(TensorNode) def build_tensor_node(model, node): """This is the Nengo build function, so that Nengo knows what to do with TensorNodes.""" # input signal if node.size_in > 0: sig_in = builder.Signal(np.zeros(node.size_in), name="" % node) model.add_op(Reset(sig_in)) else: sig_in = None sig_out = builder.Signal(np.zeros(node.size_out), name="%s.out" % node) model.sig[node]['in'] = sig_in model.sig[node]['out'] = sig_out model.params[node] = None model.add_op(SimTensorNode(node.tensor_func, model.time, sig_in, sig_out)) class SimTensorNode(builder.Operator): """Operator for TensorNodes (constructed by :func:`.build_tensor_node`). Parameters ---------- func : callable the TensorNode function (``tensor_func``) time : :class:`~nengo:nengo.builder.Signal` Signal representing the current simulation time input : :class:`~nengo:nengo.builder.Signal` or None input Signal for the TensorNode (or None if size_in==0) output : :class:`~nengo:nengo.builder.Signal` output Signal for the TensorNode tag : str, optional a label associated with the operator, for debugging Notes ----- 1. sets ``[output]`` 2. incs ``[]`` 3. reads ``[time] if input is None else [time, input]`` 4. updates ``[]`` """ def __init__(self, func, time, input, output, tag=None): super(SimTensorNode, self).__init__(tag=tag) self.func = func self.input = input self.output = output self.sets = [output] self.incs = [] self.reads = [time] if input is None else [time, input] self.updates = [] def make_step(self, *args, **kwargs): """``make_step`` is never called by the NengoDL simulator, so if this is called it means that someone is trying to execute a TensorNode in some other Simulator.""" def error(): raise SimulationError("TensorNode can only be simulated in the " "NengoDL simulator") return error
[docs]@Builder.register(SimTensorNode) class SimTensorNodeBuilder(OpBuilder): """Builds a :class:`.SimTensorNode` operator into a NengoDL model.""" def __init__(self, ops, signals): # SimTensorNodes should never be merged assert len(ops) == 1 op = ops[0] if op.input is None: self.src_data = None else: self.src_data = signals.sig_map[op.input] self.src_data.load_indices() assert self.src_data.ndim == 1 self.dst_data = signals.sig_map[op.output] self.dst_data.load_indices() self.func = op.func if hasattr(self.func, "pre_build"): self.func.pre_build( (signals.minibatch_size,) + self.src_data.shape, (signals.minibatch_size,) + self.dst_data.shape)
[docs] def build_step(self, signals): if self.src_data is None: output = self.func(signals.time) else: input = signals.gather(self.src_data) # move minibatch dimension to front input = tf.transpose(input, (1, 0)) output = self.func(signals.time, input) # move minibatch dimension back to end output = tf.transpose(output, (1, 0)) signals.scatter(self.dst_data, output)
def reshaped(shape_in): """A decorator to reshape the inputs to a function into non-vector shapes. The output of the function will be flatten back into (batched) vectors. Parameters ---------- shape_in : tuple of int the desired shape for inputs to the function (not including the first dimension, which corresponds to the batch axis) Returns ------- callable the decorated function """ def reshape_dec(func): def reshaped_func(t, x): batch_size = x.get_shape()[0].value x = tf.reshape(x, (batch_size,) + shape_in) x = func(t, x) x = tf.reshape(x, (batch_size, -1)) return x return reshaped_func return reshape_dec
[docs]def tensor_layer(input, layer_func, shape_in=None, synapse=None, transform=1, return_conn=False, **layer_args): """A utility function to construct TensorNodes that apply some function to their input (analogous to the ``tf.layers`` syntax). Parameters ---------- input : :class:`~nengo:nengo.base.NengoObject` object providing input to the layer layer_func : callable or :class:`~nengo:nengo.neurons.NeuronType` a function that takes the value from ``input`` (represented as a ``tf.Tensor``) and maps it to some output value. or a Nengo neuron type, defining a nonlinearity that will be applied to ``input`` shape_in : tuple of int, optional if not None, reshape the input to the given shape synapse : float or :class:`~nengo:nengo.synapses.Synapse`, optional synapse to apply on connection from ``input`` to this layer transform : :class:`~numpy:numpy.ndarray`, optional transform matrix to apply on connection from ``input`` to this layer return_conn : bool, optional if True, also return the connection linking this layer to ``input`` layer_args : dict, optional these arguments will be passed to ``layer_func`` if it is callable, or :class:`~nengo:nengo.Ensemble` if ``layer_func`` is a :class:`~nengo:nengo.neurons.NeuronType` Returns ------- :class:`.TensorNode` or :class:`~nengo:nengo.ensemble.Neurons` a TensorNode that implements the given layer function (if ``layer_func`` was a callable), or a Neuron object with the given neuron type, connected to ``input`` """ if isinstance(transform, np.ndarray) and transform.ndim == 2: size_in = transform.shape[0] elif shape_in is not None: size_in = else: size_in = input.size_out if isinstance(layer_func, NeuronType): node = Ensemble(size_in, 1, neuron_type=layer_func, **layer_args).neurons else: # add (ignored) time input and pass kwargs def node_func(_, x): return layer_func(x, **layer_args) # reshape input if necessary if shape_in is not None: node_func = reshaped(shape_in)(node_func) node = TensorNode(node_func, size_in=size_in) conn = Connection(input, node, synapse=synapse, transform=transform) return (node, conn) if return_conn else node