def main(model='diehl_and_cook_2015', data='mnist', param_string=None, cmap='hot_r', p_destroy=0, p_delete=0):
assert param_string is not None, 'Pass "--param_string" argument on command line or main method.'
f = os.path.join(ROOT_DIR, 'params', data, model, f'{param_string}.pt')
if not os.path.isfile(f):
print('File not found locally. Attempting download from swarm2 cluster.')
download_params.main(model=model, data=data, param_string=param_string)
network = torch.load(open(f, 'rb'))
if data in ['mnist', 'letters', 'fashion_mnist']:
if model in ['diehl_and_cook_2015', 'two_level_inhibition', 'real_dac', 'unclamp_dac']:
params = param_string.split('_')
n_sqrt = int(np.ceil(np.sqrt(int(params[1]))))
side = int(np.sqrt(network.layers['X'].n))
w = network.connections['X', 'Y'].w
w = get_square_weights(w, n_sqrt, side)
plot_weights(w, cmap=cmap)
elif model in ['conv']:
w = network.connections['X', 'Y'].w
plot_conv2d_weights(w, wmax=network.connections['X', 'Y'].wmax, cmap=cmap)
elif model in ['fully_conv', 'locally_connected', 'crop_locally_connected', 'bern_crop_locally_connected']:
params = param_string.split('_')
kernel_size = int(params[1])
stride = int(params[2])
n_filters = int(params[3])
if model in ['crop_locally_connected', 'bern_crop_locally_connected']:
crop = int(params[4])
side_length = 28 - crop * 2
else:
side_length = 28
if kernel_size == side_length:
conv_size = 1
else:
conv_size = int((side_length - kernel_size) / stride) + 1
locations = torch.zeros(kernel_size, kernel_size, conv_size, conv_size).long()
for c1 in range(conv_size):
for c2 in range(conv_size):
for k1 in range(kernel_size):
for k2 in range(kernel_size):
location = c1 * stride * side_length + c2 * stride + k1 * side_length + k2
locations[k1, k2, c1, c2] = location
locations = locations.view(kernel_size ** 2, conv_size ** 2)
w = network.connections[('X', 'Y')].w
mask = torch.bernoulli(p_destroy * torch.ones(w.size())).byte()
w[mask] = 0
mask = torch.bernoulli(p_delete * torch.ones(w.size(1))).byte()
w[:, mask] = 0
plot_locally_connected_weights(
w, n_filters, kernel_size, conv_size, locations, side_length, wmin=w.min(), wmax=w.max(), cmap=cmap
)
elif model in ['backprop']:
w = network.connections['X', 'Y'].w
weights = [
w[:, i].view(28, 28) for i in range(10)
]
w = torch.zeros(5 * 28, 2 * 28)
for i in range(5):
for j in range(2):
w[i * 28: (i + 1) * 28, j * 28: (j + 1) * 28] = weights[i + j * 5]
plot_weights(w, wmin=-1, wmax=1, cmap=cmap)
elif model in ['two_layer_backprop']:
params = param_string.split('_')
sqrt = int(np.ceil(np.sqrt(int(params[1]))))
w = network.connections['Y', 'Z'].w
weights = [
w[:, i].view(sqrt, sqrt) for i in range(10)
]
w = torch.zeros(5 * sqrt, 2 * sqrt)
for i in range(5):
for j in range(2):
w[i * sqrt: (i + 1) * sqrt, j * sqrt: (j + 1) * sqrt] = weights[i + j * 5]
plot_weights(w, wmin=-1, wmax=1, cmap=cmap)
w = network.connections['X', 'Y'].w
square_weights = get_square_weights(w, sqrt, 28)
plot_weights(square_weights, wmin=-1, wmax=1, cmap=cmap)
else:
raise NotImplementedError('Weight plotting not implemented for this data, model combination.')
elif data in ['breakout']:
if model in ['crop', 'rebalance', 'two_level']:
params = param_string.split('_')
n_sqrt = int(np.ceil(np.sqrt(int(params[1]))))
side = (50, 72)
if model in ['crop', 'rebalance']:
w = network.connections[('X', 'Ae')].w
else:
w = network.connections[('X', 'Y')].w
w = get_square_weights(w, n_sqrt, side)
plot_weights(w, cmap=cmap)
elif model in ['backprop']:
w = network.connections['X', 'Y'].w
weights = [
w[:, i].view(50, 72) for i in range(4)
]
w = torch.zeros(2 * 50, 2 * 72)
for j in range(2):
for k in range(2):
w[j * 50: (j + 1) * 50, k * 72: (k + 1) * 72] = weights[j + k * 2]
plot_weights(w, cmap=cmap)
else:
raise NotImplementedError('Weight plotting not implemented for this data, model combination.')
if data in ['cifar10']:
if model in ['backprop']:
wmin = network.connections['X', 'Y'].wmin
wmax = network.connections['X', 'Y'].wmax
w = network.connections['X', 'Y'].w
weights = [w[:, i].view(32, 32, 3).mean(2) for i in range(10)]
w = torch.zeros(5 * 32, 2 * 32)
for i in range(5):
for j in range(2):
w[i * 32: (i + 1) * 32, j * 32: (j + 1) * 32] = weights[i + j * 5]
plot_weights(w, wmin=wmin, wmax=wmax, cmap=cmap)
else:
raise NotImplementedError('Weight plotting not implemented for this data, model combination.')
path = os.path.join(ROOT_DIR, 'plots', data, model, 'weights')
if not os.path.isdir(path):
os.makedirs(path)
plt.savefig(os.path.join(path, f'{param_string}.png'))
plt.ioff()
plt.show()
def main(seed=0,
n_train=60000,
n_test=10000,
inhib=250,
kernel_size=(16, ),
stride=(2, ),
time=50,
n_filters=25,
crop=0,
lr=1e-2,
lr_decay=0.99,
dt=1,
theta_plus=0.05,
theta_decay=1e-7,
norm=0.2,
progress_interval=10,
update_interval=250,
train=True,
relabel=False,
plot=False,
gpu=False):
assert n_train % update_interval == 0 and n_test % update_interval == 0 or relabel, \
'No. examples must be divisible by update_interval'
params = [
seed, kernel_size, stride, n_filters, crop, lr, lr_decay, n_train,
inhib, time, dt, theta_plus, theta_decay, norm, progress_interval,
update_interval
]
model_name = '_'.join([str(x) for x in params])
if not train:
test_params = [
seed, kernel_size, stride, n_filters, crop, lr, lr_decay, n_train,
n_test, inhib, time, dt, theta_plus, theta_decay, norm,
progress_interval, update_interval
]
np.random.seed(seed)
if gpu:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
torch.cuda.manual_seed_all(seed)
else:
torch.manual_seed(seed)
side_length = 28 - crop * 2
n_inpt = side_length**2
n_examples = n_train if train else n_test
n_classes = 10
# Build network.
if train:
network = LocallyConnectedNetwork(
n_inpt=n_inpt,
input_shape=[side_length, side_length],
kernel_size=kernel_size,
stride=stride,
n_filters=n_filters,
inh=inhib,
dt=dt,
nu=[.1 * lr, lr],
theta_plus=theta_plus,
theta_decay=theta_decay,
wmin=0,
wmax=1.0,
norm=norm)
network.layers['Y'].thresh = 1
network.layers['Y'].reset = 0
network.layers['Y'].rest = 0
else:
network = load_network(os.path.join(params_path, model_name + '.pt'))
network.connections['X', 'Y'].update_rule = NoOp(
connection=network.connections['X', 'Y'],
nu=network.connections['X', 'Y'].nu)
network.layers['Y'].theta_decay = 0
network.layers['Y'].theta_plus = 0
conv_size = network.connections['X', 'Y'].conv_size
locations = network.connections['X', 'Y'].locations
conv_prod = int(np.prod(conv_size))
n_neurons = n_filters * conv_prod
# Voltage recording for excitatory and inhibitory layers.
voltage_monitor = Monitor(network.layers['Y'], ['v'], time=time)
network.add_monitor(voltage_monitor, name='output_voltage')
# Load Fashion-MNIST data.
dataset = FashionMNIST(path=data_path, download=True)
if train:
images, labels = dataset.get_train()
else:
images, labels = dataset.get_test()
if crop != 0:
images = images[:, crop:-crop, crop:-crop]
# Record spikes during the simulation.
if not train:
update_interval = n_examples
spike_record = torch.zeros(update_interval, time, n_neurons)
# Neuron assignments and spike proportions.
if train:
assignments = -torch.ones_like(torch.Tensor(n_neurons))
proportions = torch.zeros_like(torch.Tensor(n_neurons, 10))
rates = torch.zeros_like(torch.Tensor(n_neurons, 10))
ngram_scores = {}
else:
path = os.path.join(params_path,
'_'.join(['auxiliary', model_name]) + '.pt')
assignments, proportions, rates, ngram_scores = torch.load(
open(path, 'rb'))
if train:
best_accuracy = 0
# Sequence of accuracy estimates.
curves = {'all': [], 'proportion': [], 'ngram': []}
predictions = {scheme: torch.Tensor().long() for scheme in curves.keys()}
spikes = {}
for layer in set(network.layers):
spikes[layer] = Monitor(network.layers[layer],
state_vars=['s'],
time=time)
network.add_monitor(spikes[layer], name=f'{layer}_spikes')
# Train the network.
if train:
print('\nBegin training.\n')
else:
print('\nBegin test.\n')
spike_ims = None
spike_axes = None
weights_im = None
start = t()
for i in range(n_examples):
if i % progress_interval == 0 and train:
network.connections['X', 'Y'].update_rule.nu[1] *= lr_decay
if i % progress_interval == 0:
print(f'Progress: {i} / {n_examples} ({t() - start:.4f} seconds)')
start = t()
if i % update_interval == 0 and i > 0:
if i % len(labels) == 0:
current_labels = labels[-update_interval:]
else:
current_labels = labels[i % len(images) - update_interval:i %
len(images)]
# Update and print accuracy evaluations.
curves, preds = update_curves(curves,
current_labels,
n_classes,
spike_record=spike_record,
assignments=assignments,
proportions=proportions,
ngram_scores=ngram_scores,
n=2)
print_results(curves)
for scheme in preds:
predictions[scheme] = torch.cat(
[predictions[scheme], preds[scheme]], -1)
# Save accuracy curves to disk.
to_write = ['train'] + params if train else ['test'] + params
f = '_'.join([str(x) for x in to_write]) + '.pt'
torch.save((curves, update_interval, n_examples),
open(os.path.join(curves_path, f), 'wb'))
if train:
if any([x[-1] > best_accuracy for x in curves.values()]):
print(
'New best accuracy! Saving network parameters to disk.'
)
# Save network to disk.
network.save(os.path.join(params_path, model_name + '.pt'))
path = os.path.join(
params_path,
'_'.join(['auxiliary', model_name]) + '.pt')
torch.save((assignments, proportions, rates, ngram_scores),
open(path, 'wb'))
best_accuracy = max([x[-1] for x in curves.values()])
# Assign labels to excitatory layer neurons.
assignments, proportions, rates = assign_labels(
spike_record, current_labels, n_classes, rates)
# Compute ngram scores.
ngram_scores = update_ngram_scores(spike_record,
current_labels, n_classes,
2, ngram_scores)
print()
# Get next input sample.
image = images[i % len(images)].contiguous().view(-1)
sample = poisson(datum=image, time=time, dt=dt)
inpts = {'X': sample}
# Run the network on the input.
network.run(inpts=inpts, time=time)
retries = 0
while spikes['Y'].get('s').sum() < 5 and retries < 3:
retries += 1
image *= 2
sample = poisson(datum=image, time=time, dt=dt)
inpts = {'X': sample}
network.run(inpts=inpts, time=time)
# Add to spikes recording.
spike_record[i % update_interval] = spikes['Y'].get('s').t()
# Optionally plot various simulation information.
if plot:
_spikes = {
'X': spikes['X'].get('s').view(side_length**2, time),
'Y': spikes['Y'].get('s').view(n_filters * conv_prod, time)
}
spike_ims, spike_axes = plot_spikes(spikes=_spikes,
ims=spike_ims,
axes=spike_axes)
weights_im = plot_locally_connected_weights(
network.connections['X', 'Y'].w,
n_filters,
kernel_size,
conv_size,
locations,
side_length,
im=weights_im,
wmin=0,
wmax=1)
plt.pause(1e-8)
network.reset_() # Reset state variables.
print(f'Progress: {n_examples} / {n_examples} ({t() - start:.4f} seconds)')
i += 1
if i % len(labels) == 0:
current_labels = labels[-update_interval:]
else:
current_labels = labels[i % len(images) - update_interval:i %
len(images)]
if not train and relabel:
# Assign labels to excitatory layer neurons.
assignments, proportions, rates = assign_labels(
spike_record, current_labels, n_classes, rates)
# Compute ngram scores.
ngram_scores = update_ngram_scores(spike_record, current_labels,
n_classes, 2, ngram_scores)
# Update and print accuracy evaluations.
curves, preds = update_curves(curves,
current_labels,
n_classes,
spike_record=spike_record,
assignments=assignments,
proportions=proportions,
ngram_scores=ngram_scores,
n=2)
print_results(curves)
for scheme in preds:
predictions[scheme] = torch.cat([predictions[scheme], preds[scheme]],
-1)
if train:
if any([x[-1] > best_accuracy for x in curves.values()]):
print('New best accuracy! Saving network parameters to disk.')
# Save network to disk.
network.save(os.path.join(params_path, model_name + '.pt'))
path = os.path.join(params_path,
'_'.join(['auxiliary', model_name]) + '.pt')
torch.save((assignments, proportions, rates, ngram_scores),
open(path, 'wb'))
if train:
print('\nTraining complete.\n')
else:
print('\nTest complete.\n')
print('Average accuracies:\n')
for scheme in curves.keys():
print('\t%s: %.2f' % (scheme, float(np.mean(curves[scheme]))))
# Save accuracy curves to disk.
to_write = ['train'] + params if train else ['test'] + params
f = '_'.join([str(x) for x in to_write]) + '.pt'
torch.save((curves, update_interval, n_examples),
open(os.path.join(curves_path, f), 'wb'))
# Save results to disk.
path = os.path.join('..', '..', 'results', data, model)
if not os.path.isdir(path):
os.makedirs(path)
results = [
np.mean(curves['all']),
np.mean(curves['proportion']),
np.mean(curves['ngram']),
np.max(curves['all']),
np.max(curves['proportion']),
np.max(curves['ngram'])
]
to_write = params + results if train else test_params + results
to_write = [str(x) for x in to_write]
name = 'train.csv' if train else 'test.csv'
if not os.path.isfile(os.path.join(results_path, name)):
with open(os.path.join(path, name), 'w') as f:
if train:
f.write(
'random_seed,kernel_size,stride,n_filters,crop,n_train,inhib,time,lr,lr_decay,timestep,theta_plus,'
'theta_decay,norm,progress_interval,update_interval,mean_all_activity,mean_proportion_weighting,'
'mean_ngram,max_all_activity,max_proportion_weighting,max_ngram\n'
)
else:
f.write(
'random_seed,kernel_size,stride,n_filters,crop,n_train,n_test,inhib,time,lr,lr_decay,timestep,'
'theta_plus,theta_decay,norm,progress_interval,update_interval,mean_all_activity,'
'mean_proportion_weighting,mean_ngram,max_all_activity,max_proportion_weighting,max_ngram\n'
)
with open(os.path.join(results_path, name), 'a') as f:
f.write(','.join(to_write) + '\n')
if labels.numel() > n_examples:
labels = labels[:n_examples]
else:
while labels.numel() < n_examples:
if 2 * labels.numel() > n_examples:
labels = torch.cat(
[labels, labels[:n_examples - labels.numel()]])
else:
labels = torch.cat([labels, labels])
# Compute confusion matrices and save them to disk.
confusions = {}
for scheme in predictions:
confusions[scheme] = confusion_matrix(labels, predictions[scheme])
to_write = ['train'] + params if train else ['test'] + test_params
f = '_'.join([str(x) for x in to_write]) + '.pt'
torch.save(confusions, os.path.join(confusion_path, f))
if plot:
# Optionally plot various simulation information.
_spikes = {
'X': network.monitors['X_spikes'].get('s').view(n_input, time),
'Y': network.monitors['Y_spikes'].get('s').view(n_neurons, time)
}
spike_ims, spike_axes = plot_spikes(spikes=_spikes,
ims=spike_ims,
axes=spike_axes)
w = network.connections[('X', 'Y')].w
weights_im = plot_locally_connected_weights(w,
150,
12, (3, 3),
locations,
20,
im=weights_im)
weights2_im = plot_weights(model.coef_, im=weights2_im)
spikes = spikes.view(
network.connections['X', 'Y'].n_filters,
network.connections['X', 'Y'].conv_size[0] *
network.connections['X', 'Y'].conv_size[1])
_, max_indices = torch.max(spikes, dim=0)
w = w.view(400, 9 * 150)
w = w[locations[:, max_indices % 9], max_indices]
print(max_indices % 9)
def main(seed=0, n_examples=100, gpu=False, plot=False):
np.random.seed(seed)
if gpu:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
torch.cuda.manual_seed_all(seed)
else:
torch.manual_seed(seed)
model_name = '0_12_4_150_4_0.01_0.99_60000_250.0_250_1.0_0.05_1e-07_0.5_0.2_10_250'
network = load_network(os.path.join(params_path, f'{model_name}.pt'))
for l in network.layers:
network.layers[l].dt = network.dt
for c in network.connections:
network.connections[c].dt = network.dt
network.layers['Y'].one_spike = True
network.layers['Y'].lbound = None
kernel_size = 12
side_length = 20
n_filters = 150
time = 250
intensity = 0.5
crop = 4
conv_size = network.connections['X', 'Y'].conv_size
locations = network.connections['X', 'Y'].locations
conv_prod = int(np.prod(conv_size))
n_neurons = n_filters * conv_prod
n_classes = 10
# Voltage recording for excitatory and inhibitory layers.
voltage_monitor = Monitor(network.layers['Y'], ['v'], time=time)
network.add_monitor(voltage_monitor, name='output_voltage')
# Load MNIST data.
dataset = MNIST(path=data_path, download=True)
images, labels = dataset.get_test()
images *= intensity
images = images[:, crop:-crop, crop:-crop]
# Neuron assignments and spike proportions.
path = os.path.join(params_path,
'_'.join(['auxiliary', model_name]) + '.pt')
assignments, proportions, rates, ngram_scores = torch.load(open(
path, 'rb'))
spikes = {}
for layer in set(network.layers):
spikes[layer] = Monitor(network.layers[layer],
state_vars=['s'],
time=time)
network.add_monitor(spikes[layer], name=f'{layer}_spikes')
# Train the network.
print('\nBegin black box adversarial attack.\n')
spike_ims = None
spike_axes = None
weights_im = None
inpt_ims = None
inpt_axes = None
max_iters = 25
delta = 0.1
epsilon = 0.1
for i in range(n_examples):
# Get next input sample.
original = images[i % len(images)].contiguous().view(-1)
label = labels[i % len(images)]
# Check if the image is correctly classified.
sample = poisson(datum=original, time=time)
inpts = {'X': sample}
# Run the network on the input.
network.run(inpts=inpts, time=time)
# Check for incorrect classification.
s = spikes['Y'].get('s').view(1, n_neurons, time)
prediction = ngram(spikes=s,
ngram_scores=ngram_scores,
n_labels=10,
n=2).item()
if prediction != label:
continue
# Create adversarial example.
adversarial = False
while not adversarial:
adv_example = 255 * torch.rand(original.size())
sample = poisson(datum=adv_example, time=time)
inpts = {'X': sample}
# Run the network on the input.
network.run(inpts=inpts, time=time)
# Check for incorrect classification.
s = spikes['Y'].get('s').view(1, n_neurons, time)
prediction = ngram(spikes=s,
ngram_scores=ngram_scores,
n_labels=n_classes,
n=2).item()
if prediction == label:
adversarial = True
j = 0
current = original.clone()
while j < max_iters:
# Orthogonal perturbation.
# perturb = orthogonal_perturbation(delta=delta, image=adv_example, target=original)
# temp = adv_example + perturb
# # Forward perturbation.
# temp = temp.clone() + forward_perturbation(epsilon * get_diff(temp, original), temp, adv_example)
# print(temp)
perturbation = torch.randn(original.size())
unnormed_source_direction = original - perturbation
source_norm = torch.norm(unnormed_source_direction)
source_direction = unnormed_source_direction / source_norm
dot = torch.dot(perturbation, source_direction)
perturbation -= dot * source_direction
perturbation *= epsilon * source_norm / torch.norm(perturbation)
D = 1 / np.sqrt(epsilon**2 + 1)
direction = perturbation - unnormed_source_direction
spherical_candidate = current + D * direction
spherical_candidate = torch.clamp(spherical_candidate, 0, 255)
new_source_direction = original - spherical_candidate
new_source_direction_norm = torch.norm(new_source_direction)
# length if spherical_candidate would be exactly on the sphere
length = delta * source_norm
# length including correction for deviation from sphere
deviation = new_source_direction_norm - source_norm
length += deviation
# make sure the step size is positive
length = max(0, length)
# normalize the length
length = length / new_source_direction_norm
candidate = spherical_candidate + length * new_source_direction
candidate = torch.clamp(candidate, 0, 255)
sample = poisson(datum=candidate, time=time)
inpts = {'X': sample}
# Run the network on the input.
network.run(inpts=inpts, time=time)
# Check for incorrect classification.
s = spikes['Y'].get('s').view(1, n_neurons, time)
prediction = ngram(spikes=s,
ngram_scores=ngram_scores,
n_labels=10,
n=2).item()
# Optionally plot various simulation information.
if plot:
_input = original.view(side_length, side_length)
reconstruction = candidate.view(side_length, side_length)
_spikes = {
'X': spikes['X'].get('s').view(side_length**2, time),
'Y': spikes['Y'].get('s').view(n_neurons, time)
}
w = network.connections['X', 'Y'].w
spike_ims, spike_axes = plot_spikes(spikes=_spikes,
ims=spike_ims,
axes=spike_axes)
weights_im = plot_locally_connected_weights(w,
n_filters,
kernel_size,
conv_size,
locations,
side_length,
im=weights_im)
inpt_axes, inpt_ims = plot_input(_input,
reconstruction,
label=labels[i],
ims=inpt_ims,
axes=inpt_axes)
plt.pause(1e-8)
if prediction == label:
print('Attack failed.')
else:
print('Attack succeeded.')
adv_example = candidate
j += 1
network.reset_() # Reset state variables.
print('\nAdversarial attack complete.\n')
network.run(inpts=inpts, time=time)
# Add to spikes recording.
spike_record[i % update_interval] = spikes['Y'].get('s').t()
# Optionally plot various simulation information.
if plot:
inpt = images[i % len(images)].view(80, 80)
reconstruction = inpts['X'].view(time, 80 ** 2).sum(0).view(80, 80)
_spikes = {layer: spikes[layer].get('s') for layer in spikes}
input_exc_weights = network.connections[('X', 'Y')].w
inpt_axes, inpt_ims = plot_input(inpt, reconstruction, label=labels[i], axes=inpt_axes, ims=inpt_ims)
spike_ims, spike_axes = plot_spikes(_spikes, ims=spike_ims, axes=spike_axes)
weights_im = plot_locally_connected_weights(
input_exc_weights, n_filters, kernel_size, conv_size, locations, 80, im=weights_im
)
perf_ax = plot_performance(curves, ax=perf_ax)
plt.pause(1e-8)
network.reset_() # Reset state variables.
print(f'Progress: {n_examples} / {n_examples} ({t() - start:.4f} seconds)')
i += 1
if i % len(labels) == 0:
current_labels = labels[-update_interval:]
else:
current_labels = labels[i % len(labels) - update_interval:i % len(labels)]
def main(seed=0,
n_train=60000,
n_test=10000,
inhib=250,
kernel_size=(16, ),
stride=(2, ),
n_filters=25,
n_output=100,
time=100,
crop=0,
lr=1e-2,
lr_decay=0.99,
dt=1,
theta_plus=0.05,
theta_decay=1e-7,
intensity=1,
norm=0.2,
progress_interval=10,
update_interval=250,
train=True,
plot=False,
gpu=False):
assert n_train % update_interval == 0, 'No. examples must be divisible by update_interval'
params = [
seed, kernel_size, stride, n_filters, crop, lr, lr_decay, n_train,
inhib, time, dt, theta_plus, theta_decay, intensity, norm,
progress_interval, update_interval
]
model_name = '_'.join([str(x) for x in params])
if not train:
test_params = [
seed, kernel_size, stride, n_filters, crop, lr, lr_decay, n_train,
n_test, inhib, time, dt, theta_plus, theta_decay, intensity, norm,
progress_interval, update_interval
]
np.random.seed(seed)
if gpu:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
torch.cuda.manual_seed_all(seed)
else:
torch.manual_seed(seed)
side_length = 28 - crop * 2
n_inpt = side_length**2
n_examples = n_train if train else n_test
n_classes = 10
# Build network.
if train:
network = load_network(
os.path.join(
ROOT_DIR, 'params', 'mnist', 'crop_locally_connected',
'0_12_4_150_4_0.01_0.99_60000_250.0_250_1.0_0.05_1e-07_0.5_0.2_10_250.pt'
))
for l in network.layers:
network.layers[l].dt = 1
network.layers[l].lbound = None
for m in network.monitors:
network.monitors[m].record_length = 0
network.connections['X', 'Y'].update_rule = NoOp(
connection=network.connections['X', 'Y'],
nu=network.connections['X', 'Y'].nu)
network.layers['Y'].theta_decay = 0
network.layers['Y'].theta_plus = 0
network.layers['Y'].theta -= 0.5 * network.layers['Y'].theta.mean()
network.layers['Y'].one_spike = False
del network.connections['Y', 'Y']
output_layer = DiehlAndCookNodes(n=n_output,
traces=True,
rest=0,
reset=0,
thresh=1,
refrac=0,
decay=1e-2,
trace_tc=5e-2)
hidden_output_connection = Connection(
network.layers['Y'],
output_layer,
nu=[0, lr],
update_rule=WeightDependentPostPre,
wmin=0,
wmax=1,
norm=norm * network.layers['Y'].n)
w = -inhib * (torch.ones(n_output, n_output) -
torch.diag(torch.ones(n_output)))
output_recurrent_connection = Connection(output_layer,
output_layer,
w=w,
update_rule=NoOp,
wmin=-inhib,
wmax=0)
network.add_layer(output_layer, name='Z')
network.add_connection(hidden_output_connection,
source='Y',
target='Z')
network.add_connection(output_recurrent_connection,
source='Z',
target='Z')
else:
network = load_network(os.path.join(params_path, model_name + '.pt'))
network.connections['Y', 'Z'].update_rule = NoOp(
connection=network.connections['Y', 'Z'], nu=0)
# network.layers['Z'].theta = 0
# network.layers['Z'].theta_decay = 0
# network.layers['Z'].theta_plus = 0
# del network.connections['Z', 'Z']
conv_size = network.connections['X', 'Y'].conv_size
locations = network.connections['X', 'Y'].locations
conv_prod = int(np.prod(conv_size))
n_neurons = n_filters * conv_prod
# Voltage recording for excitatory and inhibitory layers.
voltage_monitor = Monitor(network.layers['Y'], ['v'], time=time)
network.add_monitor(voltage_monitor, name='output_voltage')
# Load MNIST data.
dataset = MNIST(path=data_path, download=True)
if train:
images, labels = dataset.get_train()
else:
images, labels = dataset.get_test()
images *= intensity
images = images[:, crop:-crop,
crop:-crop].contiguous().view(-1, side_length**2)
spikes = {}
for layer in set(network.layers):
spikes[layer] = Monitor(network.layers[layer],
state_vars=['s'],
time=time)
network.add_monitor(spikes[layer], name=f'{layer}_spikes')
# Train the network.
if train:
print('\nBegin training.\n')
else:
print('\nBegin test.\n')
spike_ims = None
spike_axes = None
weights_im = None
weights2_im = None
unclamps = {}
per_class = int(n_output / n_classes)
for label in range(n_classes):
unclamp = torch.ones(n_output).byte()
unclamp[label * per_class:(label + 1) * per_class] = 0
unclamps[label] = unclamp
predictions = torch.zeros(n_examples)
corrects = torch.zeros(n_examples)
start = t()
for i in range(n_examples):
if i % progress_interval == 0:
print(f'Progress: {i} / {n_examples} ({t() - start:.4f} seconds)')
start = t()
if i % update_interval == 0 and i > 0:
if train:
network.save(os.path.join(params_path, model_name + '.pt'))
network.connections['X', 'Y'].update_rule.nu[1] *= lr_decay
# Get next input sample.
image = images[i % len(images)]
label = labels[i % len(images)].item()
sample = poisson(datum=image, time=time, dt=dt)
inpts = {'X': sample}
# Run the network on the input.
if train:
network.run(inpts=inpts, time=time, unclamp={'Z': unclamps[label]})
else:
network.run(inpts=inpts, time=time)
if not train:
retries = 0
while spikes['Z'].get('s').sum() < 5 and retries < 3:
retries += 1
image *= 1.5
sample = poisson(datum=image, time=time, dt=dt)
inpts = {'X': sample}
if train:
network.run(inpts=inpts,
time=time,
unclamp={'Z': unclamps[label]})
else:
network.run(inpts=inpts, time=time)
output = spikes['Z'].get('s')
summed_neurons = output.sum(dim=1).view(per_class, n_classes)
summed_classes = summed_neurons.sum(dim=1)
prediction = torch.argmax(summed_classes).item()
correct = prediction == label
predictions[i] = prediction
corrects[i] = int(correct)
# print(spikes[].get('s').sum(), spikes['Z'].get('s').sum())
# Optionally plot various simulation information.
if plot:
_spikes = {
'X': spikes['X'].get('s').view(side_length**2, time),
'Y': spikes['Y'].get('s').view(n_neurons, time),
'Z': spikes['Z'].get('s').view(n_output, time)
}
spike_ims, spike_axes = plot_spikes(spikes=_spikes,
ims=spike_ims,
axes=spike_axes)
weights_im = plot_locally_connected_weights(
network.connections['X', 'Y'].w,
n_filters,
kernel_size,
conv_size,
locations,
side_length,
im=weights_im)
w = network.connections['Y', 'Z'].w
weights2_im = plot_weights(w, im=weights2_im, wmax=1)
plt.pause(1e-8)
network.reset_() # Reset state variables.
print(f'Progress: {n_examples} / {n_examples} ({t() - start:.4f} seconds)')
if train:
network.save(os.path.join(params_path, model_name + '.pt'))
if train:
print('\nTraining complete.\n')
else:
print('\nTest complete.\n')
accuracy = torch.mean(corrects).item() * 100
print(f'\nAccuracy: {accuracy}\n')
to_write = params + [accuracy] if train else test_params + [accuracy]
to_write = [str(x) for x in to_write]
name = 'train.csv' if train else 'test.csv'
if not os.path.isfile(os.path.join(results_path, name)):
with open(os.path.join(results_path, name), 'w') as f:
if train:
f.write(
'random_seed,kernel_size,stride,n_filters,crop,lr,lr_decay,n_train,inhib,time,timestep,theta_plus,'
'theta_decay,intensity,norm,progress_interval,accuracy\n')
else:
f.write(
'random_seed,kernel_size,stride,n_filters,crop,lr,lr_decay,n_train,n_test,inhib,time,timestep,'
'theta_plus,theta_decay,intensity,norm,progress_interval,update_interval,accuracy\n'
)
with open(os.path.join(results_path, name), 'a') as f:
f.write(','.join(to_write) + '\n')
if labels.numel() > n_examples:
labels = labels[:n_examples]
else:
while labels.numel() < n_examples:
if 2 * labels.numel() > n_examples:
labels = torch.cat(
[labels, labels[:n_examples - labels.numel()]])
else:
labels = torch.cat([labels, labels])
# Compute confusion matrices and save them to disk.
confusion = confusion_matrix(labels, predictions)
to_write = ['train'] + params if train else ['test'] + test_params
f = '_'.join([str(x) for x in to_write]) + '.pt'
torch.save(confusion, os.path.join(confusion_path, f))
reconstruction = inpts['X'].view(time, 50 * 72).sum(0).view(50, 72)
_spikes = {layer: spikes[layer].get('s') for layer in spikes}
input_exc_weights = network.connections[('X', 'Y')].w
inpt_axes, inpt_ims = plot_input(inpt,
reconstruction,
label=labels[i],
axes=inpt_axes,
ims=inpt_ims)
spike_ims, spike_axes = plot_spikes(_spikes,
ims=spike_ims,
axes=spike_axes)
weights_im = plot_locally_connected_weights(
network.connections[('X', 'Y')].w,
n_filters,
kernel_size,
conv_size,
locations, (50, 72),
im=weights_im)
perf_ax = plot_performance(curves, ax=perf_ax)
plt.pause(1e-8)
network.reset_() # Reset state variables.
print(f'Progress: {n_examples} / {n_examples} ({t() - start:.4f} seconds)')
i += 1
if i % len(labels) == 0:
current_labels = labels[-update_interval:]
def main(seed=0, n_train=60000, n_test=10000, inhib=250, kernel_size=(16,), stride=(2,), n_filters=25, crop=4, lr=0.01,
lr_decay=1, time=100, dt=1, theta_plus=0.05, theta_decay=1e-7, intensity=1, norm=0.2, progress_interval=10,
update_interval=250, plot=False, train=True, gpu=False):
assert n_train % update_interval == 0 and n_test % update_interval == 0, \
'No. examples must be divisible by update_interval'
params = [
seed, kernel_size, stride, n_filters, crop, lr, lr_decay, n_train, inhib, time, dt,
theta_plus, theta_decay, intensity, norm, progress_interval, update_interval
]
model_name = '_'.join([str(x) for x in params])
if not train:
test_params = [
seed, kernel_size, stride, n_filters, crop, lr, lr_decay, n_train, n_test, inhib, time, dt,
theta_plus, theta_decay, intensity, norm, progress_interval, update_interval
]
np.random.seed(seed)
if gpu:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
torch.cuda.manual_seed_all(seed)
else:
torch.manual_seed(seed)
side_length = 28 - crop * 2
n_examples = n_train if train else n_test
network = load_network(os.path.join(params_path, model_name + '.pt'))
network.layers['X'] = Input(n=400)
network.layers['Y'] = DiehlAndCookNodes(
n=network.layers['Y'].n, thresh=network.layers['Y'].thresh, rest=network.layers['Y'].rest,
reset=network.layers['Y'].reset, theta_plus=network.layers['Y'].theta_plus,
theta_decay=network.layers['Y'].theta_decay
)
network.add_layer(network.layers['X'], name='X')
network.add_layer(network.layers['Y'], name='Y')
network.connections['X', 'Y'].source = network.layers['X']
network.connections['X', 'Y'].target = network.layers['Y']
network.connections['X', 'Y'].update_rule = NoOp(
connection=network.connections['X', 'Y'], nu=network.connections['X', 'Y'].nu
)
network.layers['Y'].theta_decay = 0
network.layers['Y'].theta_plus = 0
conv_size = network.connections['X', 'Y'].conv_size
locations = network.connections['X', 'Y'].locations
conv_prod = int(np.prod(conv_size))
n_neurons = n_filters * conv_prod
n_classes = 10
# Voltage recording for excitatory and inhibitory layers.
voltage_monitor = Monitor(network.layers['Y'], ['v'], time=time)
network.add_monitor(voltage_monitor, name='output_voltage')
# Load MNIST data.
dataset = MNIST(path=data_path, download=True)
images, labels = dataset.get_test()
images *= intensity
images = images[:, crop:-crop, crop:-crop]
# Neuron assignments and spike proportions.
path = os.path.join(params_path, '_'.join(['auxiliary', model_name]) + '.pt')
assignments, proportions, rates, ngram_scores = torch.load(open(path, 'rb'))
spikes = {}
for layer in set(network.layers):
spikes[layer] = Monitor(network.layers[layer], state_vars=['s'], time=time)
network.add_monitor(spikes[layer], name=f'{layer}_spikes')
# Train the network.
print('\nBegin black box adversarial attack.\n')
spike_ims = None
spike_axes = None
weights_im = None
inpt_ims = None
inpt_axes = None
max_iters = 25
delta = 0.1
epsilon = 0.1
for i in range(n_examples):
# Get next input sample.
original = images[i % len(images)].contiguous().view(-1)
label = labels[i % len(images)]
# Check if the image is correctly classified.
sample = poisson(datum=original, time=time)
inpts = {'X': sample}
# Run the network on the input.
network.run(inpts=inpts, time=time)
# Check for incorrect classification.
s = spikes['Y'].get('s').view(1, n_neurons, time)
prediction = ngram(spikes=s, ngram_scores=ngram_scores, n_labels=10, n=2).item()
if prediction != label:
continue
# Create adversarial example.
adversarial = False
while not adversarial:
adv_example = 255 * torch.rand(original.size())
sample = poisson(datum=adv_example, time=time)
inpts = {'X': sample}
# Run the network on the input.
network.run(inpts=inpts, time=time)
# Check for incorrect classification.
s = spikes['Y'].get('s').view(1, n_neurons, time)
prediction = ngram(spikes=s, ngram_scores=ngram_scores, n_labels=n_classes, n=2).item()
if prediction == label:
adversarial = True
j = 0
current = original.clone()
while j < max_iters:
# Orthogonal perturbation.
# perturb = orthogonal_perturbation(delta=delta, image=adv_example, target=original)
# temp = adv_example + perturb
# # Forward perturbation.
# temp = temp.clone() + forward_perturbation(epsilon * get_diff(temp, original), temp, adv_example)
# print(temp)
perturbation = torch.randn(original.size())
unnormed_source_direction = original - perturbation
source_norm = torch.norm(unnormed_source_direction)
source_direction = unnormed_source_direction / source_norm
dot = torch.dot(perturbation, source_direction)
perturbation -= dot * source_direction
perturbation *= epsilon * source_norm / torch.norm(perturbation)
D = 1 / np.sqrt(epsilon ** 2 + 1)
direction = perturbation - unnormed_source_direction
spherical_candidate = current + D * direction
spherical_candidate = torch.clamp(spherical_candidate, 0, 255)
new_source_direction = original - spherical_candidate
new_source_direction_norm = torch.norm(new_source_direction)
# length if spherical_candidate would be exactly on the sphere
length = delta * source_norm
# length including correction for deviation from sphere
deviation = new_source_direction_norm - source_norm
length += deviation
# make sure the step size is positive
length = max(0, length)
# normalize the length
length = length / new_source_direction_norm
candidate = spherical_candidate + length * new_source_direction
candidate = torch.clamp(candidate, 0, 255)
sample = poisson(datum=candidate, time=time)
inpts = {'X': sample}
# Run the network on the input.
network.run(inpts=inpts, time=time)
# Check for incorrect classification.
s = spikes['Y'].get('s').view(1, n_neurons, time)
prediction = ngram(spikes=s, ngram_scores=ngram_scores, n_labels=10, n=2).item()
# Optionally plot various simulation information.
if plot:
_input = original.view(side_length, side_length)
reconstruction = candidate.view(side_length, side_length)
_spikes = {
'X': spikes['X'].get('s').view(side_length ** 2, time),
'Y': spikes['Y'].get('s').view(n_neurons, time)
}
w = network.connections['X', 'Y'].w
spike_ims, spike_axes = plot_spikes(spikes=_spikes, ims=spike_ims, axes=spike_axes)
weights_im = plot_locally_connected_weights(
w, n_filters, kernel_size, conv_size, locations, side_length, im=weights_im
)
inpt_axes, inpt_ims = plot_input(
_input, reconstruction, label=labels[i], ims=inpt_ims, axes=inpt_axes
)
plt.pause(1e-8)
if prediction == label:
print('Attack failed.')
else:
print('Attack succeeded.')
adv_example = candidate
j += 1
network.reset_() # Reset state variables.
print('\nAdversarial attack complete.\n')
def main(seed=0, n_train=60000, n_test=10000, c_low=1, c_high=25, p_low=0.5, kernel_size=(16,), stride=(2,),
n_filters=25, crop=4, lr=0.01, lr_decay=1, time=100, dt=1, theta_plus=0.05, theta_decay=1e-7, intensity=1,
norm=0.2, progress_interval=10, update_interval=250, plot=False, train=True, gpu=False):
assert n_train % update_interval == 0 and n_test % update_interval == 0, \
'No. examples must be divisible by update_interval'
params = [
seed, kernel_size, stride, n_filters, crop, lr, lr_decay, n_train, c_low, c_high, p_low, time, dt,
theta_plus, theta_decay, intensity, norm, progress_interval, update_interval
]
model_name = '_'.join([str(x) for x in params])
if not train:
test_params = [
seed, kernel_size, stride, n_filters, crop, lr, lr_decay, n_train, n_test, c_low, c_high, p_low, time, dt,
theta_plus, theta_decay, intensity, norm, progress_interval, update_interval
]
np.random.seed(seed)
if gpu:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
torch.cuda.manual_seed_all(seed)
else:
torch.manual_seed(seed)
side_length = 28 - crop * 2
n_inpt = side_length ** 2
input_shape = [side_length, side_length]
n_examples = n_train if train else n_test
n_classes = 10
if _pair(kernel_size) == input_shape:
conv_size = [1, 1]
else:
conv_size = (int((input_shape[0] - _pair(kernel_size)[0]) / _pair(stride)[0]) + 1,
int((input_shape[1] - _pair(kernel_size)[1]) / _pair(stride)[1]) + 1)
# Build network.
if train:
network = Network()
input_layer = Input(n=n_inpt, traces=True, trace_tc=5e-2)
output_layer = DiehlAndCookNodes(
n=n_filters * conv_size[0] * conv_size[1], traces=True, rest=-65.0, reset=-60.0,
thresh=-52.0, refrac=5, decay=1e-2, trace_tc=5e-2, theta_plus=theta_plus, theta_decay=theta_decay
)
input_output_conn = LocallyConnectedConnection(
input_layer, output_layer, kernel_size=kernel_size, stride=stride, n_filters=n_filters,
nu=[0, lr], update_rule=PostPre, wmin=0, wmax=1, norm=norm, input_shape=input_shape
)
w = torch.zeros(n_filters, *conv_size, n_filters, *conv_size)
for fltr1 in range(n_filters):
for fltr2 in range(n_filters):
if fltr1 != fltr2:
for j in range(conv_size[0]):
for k in range(conv_size[1]):
x1, y1 = fltr1 // np.sqrt(n_filters), fltr1 % np.sqrt(n_filters)
x2, y2 = fltr2 // np.sqrt(n_filters), fltr2 % np.sqrt(n_filters)
w[fltr1, j, k, fltr2, j, k] = max(-c_high, -c_low * np.sqrt(euclidean([x1, y1], [x2, y2])))
w = w.view(n_filters * conv_size[0] * conv_size[1], n_filters * conv_size[0] * conv_size[1])
recurrent_conn = Connection(output_layer, output_layer, w=w)
plt.matshow(w)
plt.colorbar()
network.add_layer(input_layer, name='X')
network.add_layer(output_layer, name='Y')
network.add_connection(input_output_conn, source='X', target='Y')
network.add_connection(recurrent_conn, source='Y', target='Y')
else:
network = load_network(os.path.join(params_path, model_name + '.pt'))
network.connections['X', 'Y'].update_rule = NoOp(
connection=network.connections['X', 'Y'], nu=network.connections['X', 'Y'].nu
)
network.layers['Y'].theta_decay = 0
network.layers['Y'].theta_plus = 0
conv_size = network.connections['X', 'Y'].conv_size
locations = network.connections['X', 'Y'].locations
conv_prod = int(np.prod(conv_size))
n_neurons = n_filters * conv_prod
# Voltage recording for excitatory and inhibitory layers.
voltage_monitor = Monitor(network.layers['Y'], ['v'], time=time)
network.add_monitor(voltage_monitor, name='output_voltage')
# Load MNIST data.
dataset = MNIST(path=data_path, download=True)
if train:
images, labels = dataset.get_train()
else:
images, labels = dataset.get_test()
images *= intensity
images = images[:, crop:-crop, crop:-crop]
# Record spikes during the simulation.
spike_record = torch.zeros(update_interval, time, n_neurons)
# Neuron assignments and spike proportions.
if train:
assignments = -torch.ones_like(torch.Tensor(n_neurons))
proportions = torch.zeros_like(torch.Tensor(n_neurons, 10))
rates = torch.zeros_like(torch.Tensor(n_neurons, 10))
ngram_scores = {}
else:
path = os.path.join(params_path, '_'.join(['auxiliary', model_name]) + '.pt')
assignments, proportions, rates, ngram_scores = torch.load(open(path, 'rb'))
if train:
best_accuracy = 0
# Sequence of accuracy estimates.
curves = {'all': [], 'proportion': [], 'ngram': []}
predictions = {
scheme: torch.Tensor().long() for scheme in curves.keys()
}
spikes = {}
for layer in set(network.layers):
spikes[layer] = Monitor(network.layers[layer], state_vars=['s'], time=time)
network.add_monitor(spikes[layer], name=f'{layer}_spikes')
# Train the network.
if train:
print('\nBegin training.\n')
else:
print('\nBegin test.\n')
spike_ims = None
spike_axes = None
weights_im = None
# Calculate linear increase every update interval.
if train:
n_increase = int(p_low * n_examples) / update_interval
increase = (c_high - c_low) / n_increase
increases = 0
inhib = c_low
start = t()
for i in range(n_examples):
if i % progress_interval == 0:
print(f'Progress: {i} / {n_examples} ({t() - start:.4f} seconds)')
start = t()
if i % update_interval == 0 and i > 0:
if train:
network.connections['X', 'Y'].update_rule.nu[1] *= lr_decay
if increases < n_increase:
inhib = inhib + increase
print(f'\nIncreasing inhibition to {inhib}.\n')
w = torch.zeros(n_filters, *conv_size, n_filters, *conv_size)
for fltr1 in range(n_filters):
for fltr2 in range(n_filters):
if fltr1 != fltr2:
for j in range(conv_size[0]):
for k in range(conv_size[1]):
x1, y1 = fltr1 // np.sqrt(n_filters), fltr1 % np.sqrt(n_filters)
x2, y2 = fltr2 // np.sqrt(n_filters), fltr2 % np.sqrt(n_filters)
w[fltr1, j, k, fltr2, j, k] = max(-c_high, -c_low * np.sqrt(euclidean([x1, y1], [x2, y2])))
w = w.view(n_filters * conv_size[0] * conv_size[1], n_filters * conv_size[0] * conv_size[1])
network.connections['Y', 'Y'].w = w
if i % len(labels) == 0:
current_labels = labels[-update_interval:]
else:
current_labels = labels[i % len(images) - update_interval:i % len(images)]
# Update and print accuracy evaluations.
curves, preds = update_curves(
curves, current_labels, n_classes, spike_record=spike_record, assignments=assignments,
proportions=proportions, ngram_scores=ngram_scores, n=2
)
print_results(curves)
for scheme in preds:
predictions[scheme] = torch.cat([predictions[scheme], preds[scheme]], -1)
# Save accuracy curves to disk.
to_write = ['train'] + params if train else ['test'] + params
f = '_'.join([str(x) for x in to_write]) + '.pt'
torch.save((curves, update_interval, n_examples), open(os.path.join(curves_path, f), 'wb'))
if train:
if any([x[-1] > best_accuracy for x in curves.values()]):
print('New best accuracy! Saving network parameters to disk.')
# Save network to disk.
network.save(os.path.join(params_path, model_name + '.pt'))
path = os.path.join(params_path, '_'.join(['auxiliary', model_name]) + '.pt')
torch.save((assignments, proportions, rates, ngram_scores), open(path, 'wb'))
best_accuracy = max([x[-1] for x in curves.values()])
# Assign labels to excitatory layer neurons.
assignments, proportions, rates = assign_labels(spike_record, current_labels, 10, rates)
# Compute ngram scores.
ngram_scores = update_ngram_scores(spike_record, current_labels, 10, 2, ngram_scores)
print()
# Get next input sample.
image = images[i % update_interval].contiguous().view(-1)
sample = poisson(datum=image, time=time, dt=dt)
inpts = {'X': sample}
# Run the network on the input.
network.run(inpts=inpts, time=time)
retries = 0
while spikes['Y'].get('s').sum() < 5 and retries < 3:
retries += 1
image *= 2
sample = poisson(datum=image, time=time, dt=dt)
inpts = {'X': sample}
network.run(inpts=inpts, time=time)
# Add to spikes recording.
spike_record[i % update_interval] = spikes['Y'].get('s').t()
# Optionally plot various simulation information.
if plot:
_spikes = {
'X': spikes['X'].get('s').view(side_length ** 2, time),
'Y': spikes['Y'].get('s').view(n_filters * conv_prod, time)
}
spike_ims, spike_axes = plot_spikes(spikes=_spikes, ims=spike_ims, axes=spike_axes)
weights_im = plot_locally_connected_weights(
network.connections[('X', 'Y')].w, n_filters, kernel_size, conv_size, locations, side_length, im=weights_im
)
plt.pause(1e-8)
network.reset_() # Reset state variables.
print(f'Progress: {n_examples} / {n_examples} ({t() - start:.4f} seconds)')
i += 1
if i % len(labels) == 0:
current_labels = labels[-update_interval:]
else:
current_labels = labels[i % len(images) - update_interval:i % len(images)]
# Update and print accuracy evaluations.
curves, preds = update_curves(
curves, current_labels, n_classes, spike_record=spike_record, assignments=assignments,
proportions=proportions, ngram_scores=ngram_scores, n=2
)
print_results(curves)
for scheme in preds:
predictions[scheme] = torch.cat([predictions[scheme], preds[scheme]], -1)
if train:
if any([x[-1] > best_accuracy for x in curves.values()]):
print('New best accuracy! Saving network parameters to disk.')
# Save network to disk.
network.save(os.path.join(params_path, model_name + '.pt'))
path = os.path.join(params_path, '_'.join(['auxiliary', model_name]) + '.pt')
torch.save((assignments, proportions, rates, ngram_scores), open(path, 'wb'))
if train:
print('\nTraining complete.\n')
else:
print('\nTest complete.\n')
print('Average accuracies:\n')
for scheme in curves.keys():
print('\t%s: %.2f' % (scheme, float(np.mean(curves[scheme]))))
# Save accuracy curves to disk.
to_write = ['train'] + params if train else ['test'] + params
f = '_'.join([str(x) for x in to_write]) + '.pt'
torch.save((curves, update_interval, n_examples), open(os.path.join(curves_path, f), 'wb'))
# Save results to disk.
results = [
np.mean(curves['all']), np.mean(curves['proportion']), np.mean(curves['ngram']),
np.max(curves['all']), np.max(curves['proportion']), np.max(curves['ngram'])
]
to_write = params + results if train else test_params + results
to_write = [str(x) for x in to_write]
name = 'train.csv' if train else 'test.csv'
if not os.path.isfile(os.path.join(results_path, name)):
with open(os.path.join(results_path, name), 'w') as f:
if train:
f.write(
'random_seed,kernel_size,stride,n_filters,crop,lr,lr_decay,n_train,c_low,c_high,p_low,time,timestep,theta_plus,'
'theta_decay,intensity,norm,progress_interval,update_interval,mean_all_activity,'
'mean_proportion_weighting,mean_ngram,max_all_activity,max_proportion_weighting,max_ngram\n'
)
else:
f.write(
'random_seed,kernel_size,stride,n_filters,crop,lr,lr_decay,n_train,n_test,c_low,c_high,p_low,time,timestep,'
'theta_plus,theta_decay,intensity,norm,progress_interval,update_interval,mean_all_activity,'
'mean_proportion_weighting,mean_ngram,max_all_activity,max_proportion_weighting,max_ngram\n'
)
with open(os.path.join(results_path, name), 'a') as f:
f.write(','.join(to_write) + '\n')
if labels.numel() > n_examples:
labels = labels[:n_examples]
else:
while labels.numel() < n_examples:
if 2 * labels.numel() > n_examples:
labels = torch.cat([labels, labels[:n_examples - labels.numel()]])
else:
labels = torch.cat([labels, labels])
# Compute confusion matrices and save them to disk.
confusions = {}
for scheme in predictions:
confusions[scheme] = confusion_matrix(labels, predictions[scheme])
to_write = ['train'] + params if train else ['test'] + test_params
f = '_'.join([str(x) for x in to_write]) + '.pt'
torch.save(confusions, os.path.join(confusion_path, f))