TensorNodes allow you to define parts of your model using TensorFlow, and then
insert those elements into a Nengo model. TensorNodes work very similarly to a
Node, except instead of executing arbitrary
Python code they execute arbitrary TensorFlow code.
Here is a simple example that uses a TensorNode to compute a
import nengo import nengo_dl import tensorflow as tf with nengo.Network() as net: node = nengo_dl.TensorNode(lambda t: tf.sin(t)) p = nengo.Probe(node) with nengo_dl.Simulator(net) as sim: sim.run_steps(1.0)
Note that probing and connecting to TensorNodes works in the same way as regular Nodes.
sin is something we could do with a regular Node. A
more useful application of TensorNodes is in defining network structures that
are not easily expressed in Nengo, such as convolutional neural networks.
For example, here is a network that applies a convolutional
layer to MNIST images:
import nengo import nengo_dl import numpy as np import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data class ConvLayer(object): def pre_build(self, shape_in, shape_out): self.n_mini = shape_in # minibatch size self.img_size = int(np.sqrt(shape_in)) # image height/width self.n_channels = 32 # number of convolutional filters self.kernel_size = 3 # convolutional filter size self.size_out = shape_out # output dimensionality def __call__(self, t, x): # reshape input signal to image shape image = tf.reshape(x, (self.n_mini, self.img_size, self.img_size, 1)) # apply convolutional layer conv = tf.contrib.layers.conv2d(image, self.n_channels, self.kernel_size) # apply dense layer dense = tf.contrib.layers.flatten(conv) dense = tf.contrib.layers.fully_connected(dense, self.size_out) return dense with nengo.Network() as net: # load input data (mnist images) mnist = input_data.read_data_sets("MNIST_data/") # create node to feed in images inp = nengo.Node(nengo.processes.PresentInput(mnist.train.images, 1)) # create TensorNode to insert the network defined in `ConvLayer` tf_node = nengo_dl.TensorNode(ConvLayer(), size_in=28 * 28, size_out=10) # create connections to/from TensorNodes, or probe their output, just # like a regular Node nengo.Connection(inp, tf_node) p = nengo.Probe(tf_node)
Note that the above example takes advantage of the
pre_build feature of
TensorNodes. If the object passed to TensorNode has a
NengoDL will call that function once when the model is constructed, and it will
pass in the shape of the input and output Tensors. This can be used to
define any constants or other operations that don’t need to be executed every
TensorNode(tensor_func, size_in=Default, size_out=Default, label=Default)[source]¶
Inserts TensorFlow code into a Nengo model. A TensorNode operates in much the same way as a
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
node.size_in > 0.
xwill have shape
(sim.minibatch_size, node.size_in), and the function should return a Tensor with shape
node.size_outwill be inferred by calling the function once and checking the output, if it isn’t set when the Node is created.
pre_buildattribute, 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.
- 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
- label : str, optional (Default: None)
a name for the node, used for debugging and visualization