Probing the firing rates in a TensorNode

Deep Learning
1

Hello all. I was going through this example. How can I probe the firing rates of the neurons in the TensorNode “out”?

[3]:

with nengo.Network(seed=0) as net:
    # set some default parameters for the neurons that will make
    # the training progress more smoothly
    net.config[nengo.Ensemble].max_rates = nengo.dists.Choice([100])
    net.config[nengo.Ensemble].intercepts = nengo.dists.Choice([0])
    net.config[nengo.Connection].synapse = None
    neuron_type = nengo.LIF(amplitude=0.01)

    # this is an optimization to improve the training speed,
    # since we won't require stateful behaviour in this example
    nengo_dl.configure_settings(stateful=False)

    # the input node that will be used to feed in input images
    inp = nengo.Node(np.zeros(28 * 28))

    # add the first convolutional layer
    x = nengo_dl.Layer(tf.keras.layers.Conv2D(filters=32, kernel_size=3))(
        inp, shape_in=(28, 28, 1)
    )
    x = nengo_dl.Layer(neuron_type)(x)

    # add the second convolutional layer
    x = nengo_dl.Layer(tf.keras.layers.Conv2D(filters=64, strides=2, kernel_size=3))(
        x, shape_in=(26, 26, 32)
    )
    x = nengo_dl.Layer(neuron_type)(x)

    # add the third convolutional layer
    x = nengo_dl.Layer(tf.keras.layers.Conv2D(filters=128, strides=2, kernel_size=3))(
        x, shape_in=(12, 12, 64)
    )
    x = nengo_dl.Layer(neuron_type)(x)

    # linear readout
    out = nengo_dl.Layer(tf.keras.layers.Dense(units=10))(x)

    # we'll create two different output probes, one with a filter
    # (for when we're simulating the network over time and
    # accumulating spikes), and one without (for when we're
    # training the network using a rate-based approximation)
    out_p = nengo.Probe(out, label="out_p")
    out_p_filt = nengo.Probe(out, synapse=0.1, label="out_p_filt")