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TensorNodes allow parts of a model to be defined using TensorFlow and smoothly
integrated with the rest of a Nengo model. TensorNodes work very similarly to
Node, except instead of executing arbitrary
Python code they execute arbitrary TensorFlow code.
The TensorFlow code is defined in a function or callable class.
This function accepts the current simulation time (if
pass_time=True) and/or an
node.shape_in is specified).
x will have shape
(sim.minibatch_size,) + node.shape_in, and the function should return a
Tensor with shape
(sim.minibatch_size,) + node.shape_out.
node.shape_out will be inferred by calling the function once and
checking the output, if it isn’t set when the Node is created.
def tensor_func(t, x): print(t) # current simulation time print(x) # input on current timestep return x + 1 my_node = nengo_dl.TensorNode(tensor_func, shape_in=(1,))
TensorNodes can also be used with Keras Layers, by passing an
instantiated Layer to the TensorNode. Since Keras layers typically don’t take the
simulation time as input, we can use the
pass_time=False parameter to only pass
my_node = nengo_dl.TensorNode(tf.keras.layers.Dense(units=10), shape_in=(1,), pass_time=False)
This also means that we can use custom Keras layers to implement more complicated
TensorNode behaviour. For example, if a TensorNode requires internal parameter
variables, those can be created inside a Layer’s
class MyLayer(tf.keras.layers.Layer): def build(self, input_shapes): self.w = self.add_weight() def call(self, inputs): return inputs * self.w my_node = nengo_dl.TensorNode(MyLayer(), shape_in=(1,), pass_time=False)
See the TensorFlow documentation for more details on creating custom Layers.
Once created, a TensorNode can then be used in a Nengo network just like any other Nengo object (for example, it can receive input from Connections or have its output recorded via Probes)
inp = nengo.Node(output=np.sin) conn = nengo.Connection(inp, my_node) probe = nengo.Probe(my_node)
NengoDL also provides another syntax for creating TensorNodes, designed for users more
familiar with the Keras functional API. This is the
class. Under the hood, this is just a different way of creating TensorNodes, it simply
combines the creation of a TensorNode and a Connection from some input object to that
TensorNode in a single step.
For example, in Keras we would create a Layer like
x = tf.keras.Input(shape=(1,)) y = tf.keras.layers.Dense(units=10)(x)
The equivalent, using
nengo_dl.Layer, would be
x = nengo.Node() y = nengo_dl.Layer(tf.keras.layers.Dense(units=10))(x)
Which, under the hood, is equivalent to
x = nengo.Node() y = nengo_dl.TensorNode( tf.keras.layers.Dense(units=10), pass_time=False, shape_in=(1,)) nengo.Connection(x, y, synapse=None)
See the TensorNode API for more details, or the examples below for demonstrations of using TensorNodes in practice.