Once a model has been designed, we often want to run controllable experiments to gather statistics about the performance of the model. As an example, we may want to read the input data from a file and save the corresponding outputs to a separate file. This allows us to automate the process of running these simulations, rather than using the interactive plots viewer.
Input can be provided for the simulation using the nef.SimpleNode approach, covered in more detail in Adding Arbitary Code to a Model. Here, we create an origin that has a fixed set of input stimuli, and shows each one for 100 milliseconds each:
inputs=[[ 1, 1], [ 1,-1], [-1,-1], [-1, 1], ] dt=0.001 steps_per_input=100 class Input(nef.SimpleNode): def origin_input(self,dimensions=2): step=int(round(self.t/dt)) # find time step we are on index=(step/steps_per_input)%len(inputs) # find stimulus to show return inputs[index]
This origin will cycle through showing the values 1,1, 1,-1, -1,-1, and -1,1 for 0.1 seconds each. Instead of manually specifying the inputs, we could also have read these values from a file:
inputs= for line in file('inputs.csv').readlines(): row=[float(x) for x in line.strip().split(',')] inputs.append(row)
When running experiments, we often don’t want the complete record of every output the network makes over time. Instead, we’re interested in what it is doing at specific points. For this case, we want to find out what the model’s output is for each of the inputs. In particular, we want its output value just before we change the input to the next one in the list. The following code will collect these values and save them to a file called experiments.csv:
class Output(nef.SimpleNode): def termination_save(self,x,dimensions=1,pstc=0.01): step=int(round(self.t/dt)) if step%steps_per_input==(steps_per_input-1): f=file('experiment.csv','a+') f.write('%d,%1.3f\n'%(step/steps_per_input,x)) f.close()
For this particular example, here is a model that simply computes the product of its inputs. The inputs to the model are connected to the Input node and the outputs go to the Output node to be saved:
net=nef.Network('Experiment Example') input=net.add(Input('input')) # create the input node output=net.add(Output('output')) # create the output node net.make('A',100,2,radius=1.5) net.make('B',50,1) net.connect(input.getOrigin('input'),'A') # connect the input net.connect('B',output.getTermination('save')) # connect the output def multiply(x): return x*x net.connect('A','B',func=multiply) net.add_to_nengo()
The model so far should run successfully within Nengo using the standard approach of going into the interactive plots mode and clicking the run button. However, we can also have the model automatically run right from within the script. This bypasses the visual display, making it run faster. The following commands runs the simulation for 2 seconds (this is 2 simulated seconds, of course – the actual time needed to run the simulation is dependent on your computer’s speed and the complexity of the network):
The parameter indicates how long to run the simulation for, and you can also optionally specify the time step (default of dt=0.001).
As an alternative method, you can also run the simulation like this, producing an equivalent result:
t=0 while t<=2.0: net.run(0.1) # insert any code you want to run every 0.1 seconds here t+=dt
With either approach, the simulation will be automatically run when you run the script.
With this approach, you can even run a script without using the Nengo user interface at all. Instead, you can run the model from the command line. Instead of running nengo (or nengo.bat on Windows), you can do:
This will run whatever script is in experiment.py.