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")`