Hey, I am trying to train some networks with nengo_dl and noticed something odd. Even though the encoders should be optimized according to the docs, they do not change during training.
According to the docs, Encoders should be optimised: Optimizing a NengoDL model — NengoDL documentation
When running the following code, you can see that the biases do change, but the encoders do not. I tried changing around the trainable setting but couldn’t get them to change. Furthermore, encoders also don’t seem to count towards simulator.keras_model.trainable_weights.
Code to reproduce:
# Adapted from https://www.nengo.ai/nengo-dl/examples/tensorflow-models.html
# Some setup
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import nengo
import nengo_dl
seed = 0
np.random.seed(seed)
tf.random.set_seed(seed)
(train_images, train_labels), (
test_images,
test_labels,
) = tf.keras.datasets.fashion_mnist.load_data()
train_images = train_images / 255.0
test_images = test_images / 255.0
train_images = train_images.reshape((train_images.shape[0], -1))
test_images = test_images.reshape((test_images.shape[0], -1))
class_names = [
"T-shirt/top",
"Trouser",
"Pullover",
"Dress",
"Coat",
"Sandal",
"Shirt",
"Sneaker",
"Bag",
"Ankle boot",
]
num_classes = len(class_names)
train_images = train_images[:, None, :]
train_labels = train_labels[:, None, None]
test_images = test_images[:, None, :]
test_labels = test_labels[:, None, None]
# MODEL: HERE IT GETS INTERESTING
with nengo.Network(seed=seed) as net:
net.config[nengo.Ensemble].gain = nengo.dists.Choice([1])
net.config[nengo.Ensemble].bias = nengo.dists.Choice([0])
net.config[nengo.Connection].synapse = None
net.config[nengo.Connection].transform = nengo_dl.dists.Glorot()
nengo_dl.configure_settings(trainable=True) # <<<< NOTE THIS
nengo_dl.configure_settings(lif_smoothing=0.1) # neccessary to train biases
inp = nengo.Node(output=np.ones(28 * 28))
# ensemble to train
hidden = nengo.Ensemble(5, 1, neuron_type=nengo.LIF())
net.config[hidden].trainable = True # <<<< NOTE THIS
nengo.Connection(inp, hidden.neurons)
out = nengo.Node(size_in=num_classes)
nengo.Connection(hidden.neurons, out)
# add a probe to collect output
out_p = nengo.Probe(out)
with net:
nengo_dl.configure_settings(stateful=True, use_loop=False)
with nengo_dl.Simulator(net, minibatch_size=20) as sim:
sim.compile(
optimizer=tf.optimizers.Adam(),
loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=["accuracy"],
)
for i in range(5):
sim.fit(train_images, train_labels, epochs=1)
print(sim.data[net.all_ensembles[0]].bias)
print(sim.data[net.all_ensembles[0]].encoders)
print(
"Test accuracy:",
sim.evaluate(test_images, test_labels, verbose=0)["probe_accuracy"],
)
Am I doing something wrong / missing something? Thanks for the help 