Thanks so much to both @msanch35 and @zerone !!
That was exactly my problem, now if I scale the firing rate to like 10k I get actual spiking activity.
I had taken a while off the project until I had more time to implement a few things to try and get it working, but thanks to the magic of the forum it solved itself! (or well someone else solved it for me) 
now I might actually get to the LIF neuron stage
Hi @Eric,
I am using NengoDL version 3.3.0
I am having the same issue with:
nengo.exceptions.SimulationError: Number of saved parameters in ./my-model (5) != number of variables in the model (4)
My network is a custom network with 3 hidden layers 50 neurons each,
I’ve tried the suggested solutions #1 and #3 and all don’t work in my network architecture.
I am trying to avoid rewriting code as suggested in approach #2
Do you have any additional suggestion for my case?
Best Regards,
Alex
Hi @alex.hex,
Are you sure that your model is the same architecture that you used to save my-model? The other likely cause of this error is that your model changed between when you created my-model and the current version of your script, so that the number of parameter tensors is actually different.
Otherwise, I can’t think of any reasons why you should get this error. If you can reduce the problem to a specific reproducible test case, you can post it as a bug report on NengoDL issues.
Hi Eric,
I don’t know why reason is when I convert Darknet 19 to SNN, I have an error " SimulationError: Number of saved parameters in keras_to_snn_params (41) != number of variables in the model (44)" if synapse =0.05 or non zeros and no error for synapse=None even I turn off optimizer " ```
with converter.net:
nengo_dl.configure_settings(simplifications=[])
inp=tf.keras.layers.Input(shape=(28,28,1))
conv0 = tf.keras.layers.Conv2D(filters=8,kernel_size=3,padding="same", activation=tf.nn.relu)(inp)
pool0 = tf.keras.layers.AveragePooling2D(pool_size=2,strides=2)(conv0)
conv1 = tf.keras.layers.Conv2D(filters=16,kernel_size=3,padding="same", activation=tf.nn.relu)(pool0)
pool1 = tf.keras.layers.AveragePooling2D(pool_size=2,strides=2)(conv1)
conv2 = tf.keras.layers.Conv2D(filters=32,kernel_size=3,padding="same", activation=tf.nn.relu)(pool1)
conv3 = tf.keras.layers.Conv2D(filters=16,kernel_size=1,padding="same", activation=tf.nn.relu)(conv2)
conv4 = tf.keras.layers.Conv2D(filters=32,kernel_size=3,padding="same", activation=tf.nn.relu)(conv3)
pool2 = tf.keras.layers.AveragePooling2D(pool_size=2,strides=2)(conv4)
conv5 = tf.keras.layers.Conv2D(filters=64,kernel_size=3,padding="same", activation=tf.nn.relu)(pool2)
conv6 = tf.keras.layers.Conv2D(filters=32,kernel_size=1,padding="same", activation=tf.nn.relu)(conv5)
conv7 = tf.keras.layers.Conv2D(filters=64,kernel_size=3,padding="same", activation=tf.nn.relu)(conv6)
pool3 = tf.keras.layers.AveragePooling2D(pool_size=2,strides=2)(conv7)
conv8 = tf.keras.layers.Conv2D(filters=128,kernel_size=3,padding="same", activation=tf.nn.relu)(pool3)
conv9 = tf.keras.layers.Conv2D(filters=64,kernel_size=1,padding="same", activation=tf.nn.relu)(conv8)
conv10 = tf.keras.layers.Conv2D(filters=128,kernel_size=3,padding="same", activation=tf.nn.relu)(conv9)
conv11 = tf.keras.layers.Conv2D(filters=64,kernel_size=1,padding="same", activation=tf.nn.relu)(conv10)
conv12 = tf.keras.layers.Conv2D(filters=128,kernel_size=3,padding="same", activation=tf.nn.relu)(conv11)
# pool4 = tf.keras.layers.AveragePooling2D(pool_size=2,strides=2,padding='same')(conv12)
conv13 = tf.keras.layers.Conv2D(filters=256,kernel_size=3,padding="same", activation=tf.nn.relu)(conv12)
conv14 = tf.keras.layers.Conv2D(filters=128,kernel_size=1,padding="same", activation=tf.nn.relu)(conv13)
conv15 = tf.keras.layers.Conv2D(filters=356,kernel_size=3,padding="same", activation=tf.nn.relu)(conv14)
conv16 = tf.keras.layers.Conv2D(filters=128,kernel_size=1,padding="same", activation=tf.nn.relu)(conv15)
conv17 = tf.keras.layers.Conv2D(filters=256,kernel_size=3,padding="same", activation=tf.nn.relu)(conv16)
conv18 = tf.keras.layers.Conv2D(filters=250,kernel_size=1,padding="same", activation=tf.nn.relu)(conv16)
pool5=tf.keras.layers.GlobalAveragePooling2D()(conv18)
flatten = tf.keras.layers.Flatten()(pool5)
dense = tf.keras.layers.Dense(units=10)(flatten)
model = tf.keras.Model(inputs = inp, outputs = dense)
model.summary()
converter = nengo_dl.Converter(model)
do_training = True
if do_training:
with converter.net:
nengo_dl.configure_settings(simplifications=[])
with nengo_dl.Simulator(converter.net, minibatch_size=200) as sim:
# run training
sim.compile(
optimizer=tf.optimizers.Adam(0.001),
loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[tf.metrics.sparse_categorical_accuracy],
)
sim.fit(
{converter.inputs[inp]: train_images},
{converter.outputs[dense]: train_labels},
validation_data=(
{converter.inputs[inp]: test_images},
{converter.outputs[dense]: test_labels},
),
epochs=2,
)
# save the parameters to file
sim.save_params("./keras_to_snn_params")
else:
# download pretrained weights
urlretrieve(
"https://drive.google.com/uc?export=download&"
"id=1lBkR968AQo__t8sMMeDYGTQpBJZIs2_T",
"keras_to_snn_params.npz",
)
def run_network(
activation,
params_file="keras_to_snn_params",
n_steps=120,
scale_firing_rates=1,
synapse=None,
n_test=400,
):
# convert the keras model to a nengo network
nengo_converter = nengo_dl.Converter(
model,
swap_activations={tf.nn.relu: activation},
scale_firing_rates=scale_firing_rates,
synapse=synapse,
)
# get input/output objects
nengo_input = nengo_converter.inputs[inp]
nengo_output = nengo_converter.outputs[dense]
with nengo_converter.net:
nengo_dl.configure_settings(simplifications=[])
# add a probe to the first convolutional layer to record activity.
# we'll only record from a subset of neurons, to save memory.
sample_neurons = np.linspace(
0,
np.prod(conv0.shape[1:]),
1000,
endpoint=False,
dtype=np.int32,
)
with nengo_converter.net:
conv0_probe = nengo.Probe(nengo_converter.layers[conv0][sample_neurons])
# repeat inputs for some number of timesteps
tiled_test_images = np.tile(test_images[:n_test], (1, n_steps, 1))
# set some options to speed up simulation
with nengo_converter.net:
nengo_dl.configure_settings(stateful=False)
# build network, load in trained weights, run inference on test images
with nengo_dl.Simulator(
nengo_converter.net, minibatch_size=10, progress_bar=False
) as nengo_sim:
params = list(nengo_sim.keras_model.weights)
print(len(params))
nengo_sim.load_params(params_file)
data = nengo_sim.predict({nengo_input: tiled_test_images})
# compute accuracy on test data, using output of network on
# last timestep
predictions = np.argmax(data[nengo_output][:, -1], axis=-1)
accuracy = (predictions == test_labels[:n_test, 0, 0]).mean()
print(f"Test accuracy: {100 * accuracy:.2f}%")
# plot the results
for ii in range(3):
plt.figure(figsize=(12, 4))
plt.subplot(1, 3, 1)
plt.title("Input image")
plt.imshow(test_images[ii, 0].reshape((28, 28)), cmap="gray")
plt.axis("off")
plt.subplot(1, 3, 2)
scaled_data = data[conv0_probe][ii] * scale_firing_rates
if isinstance(activation, nengo.SpikingRectifiedLinear):
scaled_data *= 0.001
rates = np.sum(scaled_data, axis=0) / (n_steps * nengo_sim.dt)
plt.ylabel("Number of spikes")
else:
rates = scaled_data
plt.ylabel("Firing rates (Hz)")
plt.xlabel("Timestep")
plt.title(
f"Neural activities (conv0 mean={rates.mean():.1f} Hz, "
f"max={rates.max():.1f} Hz)"
)
plt.plot(scaled_data)
plt.subplot(1, 3, 3)
plt.title("Output predictions")
plt.plot(tf.nn.softmax(data[nengo_output][ii]))
plt.legend([str(j) for j in range(10)], loc="upper left")
plt.xlabel("Timestep")
plt.ylabel("Probability")
plt.tight_layout()
print("Loaded pretrained weights")
for s in [0.1, 0.02, 0.07]:
print(f"Synapse={s:.3f}")
run_network(
activation=nengo.SpikingRectifiedLinear(),
n_steps=10,
synapse=s,
)
plt.show()
This has been addressed here.