def initialize_network(n_inputs, hidden_list, n_outputs):
network = list()
hidden_layer = [{'weights': [random() for i in range(n_inputs + 1)]} for i in range(hidden_list[0])]
network.append(hidden_layer)
for h in range(len(hidden_list) - 1):
# print("h:",h)
hidden_layer = [{'weights': [random() for i in range(hidden_list[h] + 1)]} for i in range(hidden_list[h + 1])]
network.append(hidden_layer)
output_layer = [{'weights': [random() for i in range(hidden_list[-1] + 1)]} for i in range(n_outputs)]
network.append(output_layer)
for layer in network:
print("\nlayer no:", network.index(layer))
for neuron in layer:
print("neuron no:", layer.index(neuron))
print(neuron)
return network
Re: FCN for Multi-Hidden Layers 
From Chartreuse Sheep, 3 Years ago, written in Java.
This paste is a reply to
FCN for Multi-Hidden Layers from Sina Birecik
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