# --- Run model in Nengo presentation_time = 0.2 model = nengo.Network() with model: u = nengo.Node(nengo.processes.PresentInput(X_test, presentation_time)) seq = SequentialNetwork(kmodel, synapse=nengo.synapses.Alpha(0.005)) nengo.Connection(u, seq.input, synapse=None) input_p = nengo.Probe(u) output_p = nengo.Probe(seq.output) # --- image display image_shape = kmodel.input_shape[1:] display_f = image_display_function(image_shape) display_node = nengo.Node(display_f, size_in=u.size_out) nengo.Connection(u, display_node, synapse=None) # --- output spa display vocab_names = ['ZERO', 'ONE', 'TWO', 'THREE', 'FOUR', 'FIVE', 'SIX', 'SEVEN', 'EIGHT', 'NINE'] vocab_vectors = np.eye(len(vocab_names)) vocab = nengo.spa.Vocabulary(len(vocab_names)) for name, vector in zip(vocab_names, vocab_vectors): vocab.add(name, vector) config = nengo.Config(nengo.Ensemble) config[nengo.Ensemble].neuron_type = nengo.Direct() with config:
# solver = nengo.solvers.LstsqL2(reg=0.0001) presentation_time = 0.1 with nengo.Network(seed=3) as model: u = nengo.Node(nengo.processes.PresentInput(X_test, presentation_time)) a = nengo.Ensemble(n_hid, n_vis, **ens_params) v = nengo.Node(size_in=n_out) nengo.Connection(u, a, synapse=None) conn = nengo.Connection( a, v, synapse=None, eval_points=X_train, function=train_targets, solver=solver) # --- image display image_shape = (1, 28, 28) display_f = image_display_function(image_shape, offset=1, scale=128) display_node = nengo.Node(display_f, size_in=u.size_out) nengo.Connection(u, display_node, synapse=None) # --- output spa display vocab_names = ['ZERO', 'ONE', 'TWO', 'THREE', 'FOUR', 'FIVE', 'SIX', 'SEVEN', 'EIGHT', 'NINE'] vocab_vectors = np.eye(len(vocab_names)) vocab = nengo.spa.Vocabulary(len(vocab_names)) for name, vector in zip(vocab_names, vocab_vectors): vocab.add(name, vector) config = nengo.Config(nengo.Ensemble) config[nengo.Ensemble].neuron_type = nengo.Direct() with config:
dimensions=env_iface.n_actions, radius=actor_radius) sensor_conn = nengo.Connection(sensor, sensor_net) sensor_srf_conn = nengo.Connection(sensor_net.neurons, srf_net.exc.neurons, synapse=nengo.Lowpass(tau), transform=np.eye(n_input) * srf_params['w_input']) srf_actor_conn = nengo.Connection( srf_net.output.neurons, actor_net.neurons, synapse=nengo.Lowpass(tau), transform=np.eye(n_actor, n_place) * srf_params['w_actor'], learning_rule_type=HSP(learning_rate=2e-4)) step_node = nengo.Node(env_iface.step, size_in=env_iface.n_actions) nengo.Connection(actor_net, step_node, synapse=fast_tau) display_func = image_display_function(image_shape) display_node = nengo.Node(display_func, size_in=sensor.size_out) nengo.Connection(sensor, display_node, synapse=None) #dt = 0.01 #t_end = 10 #with nengo.Simulator(model, optimize=True, dt=dt, progress_bar=TerminalProgressBar()) as sim: # sim.run(np.max(t_end))
# --- Run model in Nengo presentation_time = 0.2 model = nengo.Network() with model: u = nengo.Node(nengo.processes.PresentInput(X_test, presentation_time)) ccnet = CudaConvnetNetwork(cc_model, synapse=nengo.synapses.Alpha(0.001)) nengo.Connection(u, ccnet.inputs['data'], synapse=None) # input_p = nengo.Probe(u) output_p = nengo.Probe(ccnet.output) # --- image display display_f = image_display_function(image_shape, scale=1., offset=data_mean) display_node = nengo.Node(display_f, size_in=u.size_out) nengo.Connection(u, display_node, synapse=None) # --- output spa display vocab_names = spasafe_names(label_names) vocab_vectors = np.eye(len(vocab_names)) vocab = nengo.spa.Vocabulary(len(vocab_names)) for name, vector in zip(vocab_names, vocab_vectors): vocab.add(name, vector) config = nengo.Config(nengo.Ensemble) config[nengo.Ensemble].neuron_type = nengo.Direct() with config: output = nengo.spa.State(len(vocab_names), subdimensions=10, vocab=vocab)
cc_model = load_model_pickle('ilsvrc2012-lif-48.pkl') # --- Run model in Nengo presentation_time = 0.2 model = nengo.Network() with model: u = nengo.Node(nengo.processes.PresentInput(X_test, presentation_time)) ccnet = CudaConvnetNetwork(cc_model, synapse=nengo.synapses.Alpha(0.001)) nengo.Connection(u, ccnet.inputs['data'], synapse=None) # input_p = nengo.Probe(u) output_p = nengo.Probe(ccnet.output) # --- image display display_f = image_display_function(image_shape, scale=1., offset=data_mean) display_node = nengo.Node(display_f, size_in=u.size_out) nengo.Connection(u, display_node, synapse=None) # --- output spa display vocab_names = spasafe_names(label_names) vocab_vectors = np.eye(len(vocab_names)) vocab = nengo.spa.Vocabulary(len(vocab_names)) for name, vector in zip(vocab_names, vocab_vectors): vocab.add(name, vector) config = nengo.Config(nengo.Ensemble) config[nengo.Ensemble].neuron_type = nengo.Direct() with config: output = nengo.spa.State(len(vocab_names),