Loading saved keras model into NengoDL and converting it to Spiking network

[alex.hex]

Hi @xchoo,

Thank you for your quick reply!

But my intention is not to insert keras model as it is into nengo network, but replace the entire model relu with nengo.SpikingRectifiedLinear() and use the ex-keras trained model as a spiking network model.
If i understand correctly inserting keras model into nengo brings the model inside as is, without conversion to spikes. Meaning the network will have a tensorflow non spiking part inside.

I have a model which i do convert to spiking network, but because an issue with nengodl simulator save and load params i am unable to make any customizations to the number of my hidden layers, anything beyond 2 layers results in loading parameters mismatch as described above.
my model is the same one discussed in this topic:

Converting simple Keras network to Nengo Spiking neural network results in bad prediction

Hi, I have a pretty simple 4 layer network with 3 input neurons, 2 hidden layers of 50 neurons each, and output layer of 5 neurons. All will RELU activation function. All inputs and outputs are scaled to be between 0 to1 range. The network is trained with SGD optimizer on 70000 samples using batch of 250 and MSE as a loss function. 15k samples for validation, and 15k samples for test After several epochs the MSE loss of the network is under 0.02 Epoch 15/15 280/280 [=====================…

but when i try to simply add to it another hidden layer:
input = tf.keras.Input(shape=(3,))
l1 = tf.keras.layers.Dense(50,activation=tf.nn.relu)(input)
l2 = tf.keras.layers.Dense(50,activation=tf.nn.relu)(l1)
l3 = tf.keras.layers.Dense(50,activation=tf.nn.relu)(l2)
output = tf.keras.layers.Dense(5,activation=tf.nn.relu)(l3)

simulator will refuse to use it after the training during load next time.

So it seems that i cannot use use the keras model as is, since i need a full spiking network and i can’t extend it using nengodl approach.

Best Regards,
Alex