def compare_spiking_to_nonspiking(hidden_sizes = [300, 300], eta=0.01, w_init=0.01, fractional = False, n_epochs = 20,
forward_discretize = 'rect-herding', back_discretize = 'noreset-herding', test_discretize='rect-herding', save_results = False):
mnist = get_mnist_dataset(flat=True).to_onehot()
test_epochs=[0.0, 0.05, 0.1, 0.2, 0.5]+range(1, n_epochs+1)
if is_test_mode():
mnist = mnist.shorten(500)
eta = 0.01
w_init=0.01
test_epochs = [0.0, 0.05, 0.1]
spiking_net = JavaSpikingNetWrapper.from_init(
fractional = fractional,
depth_first=False,
smooth_grads = False,
forward_discretize = forward_discretize,
back_discretize = back_discretize,
test_discretize = test_discretize,
w_init=w_init,
hold_error=True,
rng = 1234,
n_steps = 10,
eta=eta,
layer_sizes=[784]+hidden_sizes+[10],
)
relu_net = GradientBasedPredictor(
MultiLayerPerceptron.from_init(
hidden_activation = 'relu',
output_activation = 'relu',
layer_sizes=[784]+hidden_sizes+[10],
use_bias=False,
w_init=w_init,
rng=1234,
),
cost_function = 'mse',
optimizer=GradientDescent(eta)
).compile()
# Listen for spikes
forward_eavesdropper = jp.JClass('nl.uva.deepspike.eavesdroppers.SpikeCountingEavesdropper')()
backward_eavesdropper = jp.JClass('nl.uva.deepspike.eavesdroppers.SpikeCountingEavesdropper')()
for lay in spiking_net.jnet.layers:
lay.forward_herder.add_eavesdropper(forward_eavesdropper)
for lay in spiking_net.jnet.layers[1:]:
lay.backward_herder.add_eavesdropper(backward_eavesdropper)
spiking_net.jnet.error_counter.add_eavesdropper(backward_eavesdropper)
forward_counts = []
backward_counts = []
def register_counts():
forward_counts.append(forward_eavesdropper.get_count())
backward_counts.append(backward_eavesdropper.get_count())
results = compare_predictors(
dataset=mnist,
online_predictors={
'Spiking-MLP': spiking_net,
'ReLU-MLP': relu_net,
},
test_epochs=test_epochs,
online_test_callbacks=lambda p: register_counts() if p is spiking_net else None,
minibatch_size = 1,
test_on = 'training+test',
evaluation_function=percent_argmax_incorrect,
)
spiking_params = [np.array(lay.forward_weights.w.asFloat()).copy() for lay in spiking_net.jnet.layers]
relu_params = [param.get_value().astype(np.float64) for param in relu_net.parameters]
# See what the score is when we apply the final spiking weights to the
offline_trained_spiking_net = JavaSpikingNetWrapper(
ws=relu_params,
fractional = fractional,
depth_first=False,
smooth_grads = False,
forward_discretize = forward_discretize,
back_discretize = back_discretize,
test_discretize = test_discretize,
hold_error=True,
n_steps = 10,
eta=eta,
)
# for spiking_layer, p in zip(spiking_net.jnet.layers, relu_params):
# spiking_layer.w = p.astype(np.float64)
error = [
('Test', percent_argmax_incorrect(offline_trained_spiking_net.predict(mnist.test_set.input), mnist.test_set.target)),
('Training', percent_argmax_incorrect(offline_trained_spiking_net.predict(mnist.training_set.input), mnist.training_set.target))
]
results['Spiking-MLP with ReLU weights'] = LearningCurveData()
results['Spiking-MLP with ReLU weights'].add(None, error)
print 'Spiking-MLP with ReLU weights: %s' % error
# --------------------------------------------------------------------------
# See what the score is when we plug the spiking weights into the ReLU net.
for param, sval in zip(relu_net.parameters, spiking_params):
param.set_value(sval)
error = [
('Test', percent_argmax_incorrect(relu_net.predict(mnist.test_set.input), mnist.test_set.target)),
('Training', percent_argmax_incorrect(relu_net.predict(mnist.training_set.input), mnist.training_set.target))
]
results['ReLU-MLP with Spiking weights'] = LearningCurveData()
results['ReLU-MLP with Spiking weights'].add(None, error)
print 'ReLU-MLP with Spiking weights: %s' % error
# --------------------------------------------------------------------------
if save_results:
with open("mnist_relu_vs_spiking_results-%s.pkl" % datetime.now(), 'w') as f:
pickle.dump(results, f)
# Problem: this currently includes test
forward_rates = np.diff(forward_counts) / (np.diff(test_epochs)*60000)
backward_rates = np.diff(backward_counts) / (np.diff(test_epochs)*60000)
plt.figure('ReLU vs Spikes')
plt.subplot(211)
plot_learning_curves(results, title = "MNIST Learning Curves", hang=False, figure_name='ReLU vs Spikes', xscale='linear', yscale='log', y_title='Percent Error')
plt.subplot(212)
plt.plot(test_epochs[1:], forward_rates)
plt.plot(test_epochs[1:], backward_rates)
plt.xlabel('Epoch')
plt.ylabel('n_spikes')
plt.legend(['Mean Forward Spikes', 'Mean Backward Spikes'], loc='best')
plt.interactive(is_test_mode())
plt.show()
fractional = False,
depth_first = False,
smooth_grads = False,
back_discretize = 'noreset-herding',
n_steps = 10,
hidden_sizes = [200, 200],
hold_error = True,
:
compare_predictors(
dataset=(get_mnist_dataset(flat=True).shorten(100) if is_test_mode() else get_mnist_dataset(flat=True)).to_onehot(),
online_predictors={'Spiking MLP': JavaSpikingNetWrapper.from_init(
fractional = fractional,
depth_first = depth_first,
smooth_grads = smooth_grads,
back_discretize = back_discretize,
w_init=0.01,
rng = 1234,
eta=0.01,
n_steps = n_steps,
hold_error=hold_error,
layer_sizes=[784]+hidden_sizes+[10],
)},
test_epochs=[0.0, 0.05] if is_test_mode() else [0.0, 0.05, 0.1, 0.2, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4],
minibatch_size = 1,
report_test_scores=True,
test_on = 'test',
evaluation_function='percent_argmax_incorrect'
)),
versions={
'Baseline': dict(),
'Fractional-Updates': dict(fractional = True),
'Depth-First': dict(depth_first = True),
