inpt_axes, inpt_ims = plot_input(
image, datum, label=label, axes=inpt_axes, ims=inpt_ims
)
spike_ims, spike_axes = plot_spikes(
{layer: spikes[layer].get("s").view(-1, 250) for layer in spikes},
axes=spike_axes,
ims=spike_ims,
)
voltage_ims, voltage_axes = plot_voltages(
{layer: voltages[layer].get("v").view(-1, 250) for layer in voltages},
ims=voltage_ims,
axes=voltage_axes,
)
weights_im = plot_weights(
get_square_weights(w1, 23, 32), im=weights_im, wmin=-2, wmax=2
)
weights_im2 = plot_weights(w2, im=weights_im2, wmin=-2, wmax=2)
plt.pause(1e-8)
network.reset_()
if i > n_iters:
break
# Define logistic regression model using PyTorch.
class LogisticRegression(nn.Module):
def __init__(self, input_size, num_classes):
super(LogisticRegression, self).__init__()
inputs = {"X": image.view(time, 480, 480, 3)}
network.run(inputs=inputs, time=time, clamp=clamp)
# Get voltage recording.
exc_voltages = exc_voltage_monitor.get("v")
inh_voltages = inh_voltage_monitor.get("v")
# Add to spikes recording.
spike_record[i % update_interval] = spikes["Ae"].get("s").view(
time, n_neurons)
# Optionally plot various simulation information.
if plot:
inpt = inputs["X"].view(time, 480 * 480 * 3).sum(0).view(480, 480, 3)
input_exc_weights = network.connections[("X", "Ae")].w
square_weights = get_square_weights(
input_exc_weights.view(480 * 480 * 3, n_neurons), n_sqrt, 480)
square_assignments = get_square_assignments(assignments, n_sqrt)
voltages = {"Ae": exc_voltages, "Ai": inh_voltages}
inpt_axes, inpt_ims = plot_input(image.sum(1).view(480, 480, 3),
inpt,
label=label,
axes=inpt_axes,
ims=inpt_ims)
spike_ims, spike_axes = plot_spikes(
{
layer: spikes[layer].get("s").view(time, 1, -1)
for layer in spikes
},
ims=spike_ims,
axes=spike_axes,
def main(args):
if args.update_steps is None:
args.update_steps = max(
250 // args.batch_size, 1
) #Its value is 16 # why is it always multiplied with step? #update_steps is how many batch to classify before updating the graphs
update_interval = args.update_steps * args.batch_size # Value is 240 #update_interval is how many pictures to classify before updating the graphs
# Sets up GPU use
torch.backends.cudnn.benchmark = False
if args.gpu and torch.cuda.is_available():
torch.cuda.manual_seed_all(
args.seed
) #to enable reproducability of the code to get the same result
else:
torch.manual_seed(args.seed)
# Determines number of workers to use
if args.n_workers == -1:
args.n_workers = args.gpu * 4 * torch.cuda.device_count()
n_sqrt = int(np.ceil(np.sqrt(args.n_neurons)))
if args.reduction == "sum": #could have used switch to improve performance
reduction = torch.sum #weight updates for the batch
elif args.reduction == "mean":
reduction = torch.mean
elif args.reduction == "max":
reduction = max_without_indices
else:
raise NotImplementedError
# Build network.
network = DiehlAndCook2015v2( #Changed here
n_inpt=784, # input dimensions are 28x28=784
n_neurons=args.n_neurons,
inh=args.inh,
dt=args.dt,
norm=78.4,
nu=(1e-4, 1e-2),
reduction=reduction,
theta_plus=args.theta_plus,
inpt_shape=(1, 28, 28),
)
# Directs network to GPU
if args.gpu:
network.to("cuda")
# Load MNIST data.
dataset = MNIST(
PoissonEncoder(time=args.time, dt=args.dt),
None,
root=os.path.join(ROOT_DIR, "data", "MNIST"),
download=True,
train=True,
transform=transforms.Compose( #Composes several transforms together
[
transforms.ToTensor(),
transforms.Lambda(lambda x: x * args.intensity)
]),
)
test_dataset = MNIST(
PoissonEncoder(time=args.time, dt=args.dt),
None,
root=os.path.join(ROOT_DIR, "data", "MNIST"),
download=True,
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Lambda(lambda x: x * args.intensity)
]),
)
# Neuron assignments and spike proportions.
n_classes = 10 #changed
assignments = -torch.ones(args.n_neurons) #assignments is set to -1
proportions = torch.zeros(args.n_neurons,
n_classes) #matrix of 100x10 filled with zeros
rates = torch.zeros(args.n_neurons,
n_classes) #matrix of 100x10 filled with zeros
# Set up monitors for spikes and voltages
spikes = {}
for layer in set(network.layers):
spikes[layer] = Monitor(
network.layers[layer], state_vars=["s"], time=args.time
) # Monitors: Records state variables of interest. obj:An object to record state variables from during network simulation.
network.add_monitor(
spikes[layer], name="%s_spikes" % layer
) #state_vars: Iterable of strings indicating names of state variables to record.
#param time: If not ``None``, pre-allocate memory for state variable recording.
weights_im = None
spike_ims, spike_axes = None, None
# Record spikes for length of update interval.
spike_record = torch.zeros(update_interval, args.time, args.n_neurons)
if os.path.isdir(
args.log_dir): #checks if the path is a existing directory
shutil.rmtree(
args.log_dir) # is used to delete an entire directory tree
# Summary writer.
writer = SummaryWriter(
log_dir=args.log_dir, flush_secs=60
) #SummaryWriter: these utilities let you log PyTorch models and metrics into a directory for visualization
#flush_secs: in seconds, to flush the pending events and summaries to disk.
for epoch in range(args.n_epochs): #default is 1
print("\nEpoch: {epoch}\n")
labels = []
# Create a dataloader to iterate and batch data
dataloader = DataLoader( #It represents a Python iterable over a dataset
dataset,
batch_size=args.batch_size, #how many samples per batch to load
shuffle=
True, #set to True to have the data reshuffled at every epoch
num_workers=args.n_workers,
pin_memory=args.
gpu, #If True, the data loader will copy Tensors into CUDA pinned memory before returning them.
)
for step, batch in enumerate(
dataloader
): #Enumerate() method adds a counter to an iterable and returns it in a form of enumerate object
print("Step:", step)
global_step = 60000 * epoch + args.batch_size * step
if step % args.update_steps == 0 and step > 0:
# Convert the array of labels into a tensor
label_tensor = torch.tensor(labels)
# Get network predictions.
all_activity_pred = all_activity(spikes=spike_record,
assignments=assignments,
n_labels=n_classes)
proportion_pred = proportion_weighting(
spikes=spike_record,
assignments=assignments,
proportions=proportions,
n_labels=n_classes,
)
writer.add_scalar(
tag="accuracy/all vote",
scalar_value=torch.mean(
(label_tensor.long() == all_activity_pred).float()),
global_step=global_step,
)
#Vennila: Records the accuracies in each step
value = torch.mean(
(label_tensor.long() == all_activity_pred).float())
value = value.item()
accuracy.append(value)
print("ACCURACY:", value)
writer.add_scalar(
tag="accuracy/proportion weighting",
scalar_value=torch.mean(
(label_tensor.long() == proportion_pred).float()),
global_step=global_step,
)
writer.add_scalar(
tag="spikes/mean",
scalar_value=torch.mean(torch.sum(spike_record, dim=1)),
global_step=global_step,
)
square_weights = get_square_weights(
network.connections["X", "Y"].w.view(784, args.n_neurons),
n_sqrt,
28,
)
img_tensor = colorize(square_weights, cmap="hot_r")
writer.add_image(
tag="weights",
img_tensor=img_tensor,
global_step=global_step,
dataformats="HWC",
)
# Assign labels to excitatory layer neurons.
assignments, proportions, rates = assign_labels(
spikes=spike_record,
labels=label_tensor,
n_labels=n_classes,
rates=rates,
)
labels = []
labels.extend(
batch["label"].tolist()
) #for each batch or 16 pictures the labels of it is added to this list
# Prep next input batch.
inpts = {"X": batch["encoded_image"]}
if args.gpu:
inpts = {
k: v.cuda()
for k, v in inpts.items()
} #.cuda() is used to set up and run CUDA operations in the selected GPU
# Run the network on the input.
t0 = time()
network.run(inputs=inpts, time=args.time, one_step=args.one_step
) # Simulate network for given inputs and time.
t1 = time() - t0
# Add to spikes recording.
s = spikes["Y"].get("s").permute((1, 0, 2))
spike_record[(step * args.batch_size) %
update_interval:(step * args.batch_size %
update_interval) + s.size(0)] = s
writer.add_scalar(tag="time/simulation",
scalar_value=t1,
global_step=global_step)
# if(step==1):
# input_exc_weights = network.connections["X", "Y"].w
# an_array = input_exc_weights.detach().cpu().clone().numpy()
# #print(np.shape(an_array))
# data = asarray(an_array)
# savetxt('data.csv',data)
# print("Beginning weights saved")
# if(step==3749):
# input_exc_weights = network.connections["X", "Y"].w
# an_array = input_exc_weights.detach().cpu().clone().numpy()
# #print(np.shape(an_array))
# data2 = asarray(an_array)
# savetxt('data2.csv',data2)
# print("Ending weights saved")
# Plot simulation data.
if args.plot:
input_exc_weights = network.connections["X", "Y"].w
# print("Weights:",input_exc_weights)
square_weights = get_square_weights(
input_exc_weights.view(784, args.n_neurons), n_sqrt, 28)
spikes_ = {
layer: spikes[layer].get("s")[:, 0]
for layer in spikes
}
spike_ims, spike_axes = plot_spikes(spikes_,
ims=spike_ims,
axes=spike_axes)
weights_im = plot_weights(square_weights, im=weights_im)
plt.pause(1e-8)
# Reset state variables.
network.reset_state_variables()
print(end_accuracy()) #Vennila
clamp={'Ae': clamp},
clamp_v={'Ae': clamp_v})
# Get voltage recording.
exc_voltages = exc_voltage_monitor.get('v')
inh_voltages = inh_voltage_monitor.get('v')
# Add to spikes recording.
spike_record[i % update_interval] = spikes['Ae'].get('s').t()
# Optionally plot various simulation information.
if plot:
inpt = inpts['X'].view(time,
im_size**2).sum(0).view(im_size, im_size)
input_exc_weights = network.connections[('X', 'Ae')].w
square_weights = get_square_weights(
input_exc_weights.view(im_size**2, n_neurons), n_sqrt, im_size)
square_assignments = get_square_assignments(assignments, n_sqrt)
voltages = {'Ae': exc_voltages, 'Ai': inh_voltages}
if i == 0:
inpt_axes, inpt_ims = plot_input(images[i].view(
im_size, im_size),
inpt,
label=labels[i])
spike_ims, spike_axes = plot_spikes(
{layer: spikes[layer].get('s')
for layer in spikes})
weights_im = plot_weights(square_weights)
assigns_im = plot_assignments(square_assignments)
perf_ax = plot_performance(accuracy)
voltage_ims, voltage_axes = plot_voltages(voltages)
def main(seed=0,
n_neurons=100,
n_train=60000,
n_test=10000,
inhib=100,
lr=1e-2,
lr_decay=1,
time=350,
dt=1,
theta_plus=0.05,
theta_decay=1e7,
intensity=1,
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, n_neurons, n_train, inhib, lr, lr_decay, time, dt, theta_plus,
theta_decay, intensity, progress_interval, update_interval
]
test_params = [
seed, n_neurons, n_train, n_test, inhib, lr, lr_decay, time, dt,
theta_plus, theta_decay, intensity, progress_interval, update_interval
]
model_name = '_'.join([str(x) for x in params])
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)
n_examples = n_train if train else n_test
n_sqrt = int(np.ceil(np.sqrt(n_neurons)))
n_classes = 10
# Build network.
if train:
network = DiehlAndCook2015v2(n_inpt=784,
n_neurons=n_neurons,
inh=inhib,
dt=dt,
norm=78.4,
theta_plus=theta_plus,
theta_decay=theta_decay,
nu=[0, lr])
else:
network = load(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
# Load MNIST data.
dataset = MNIST(path=data_path, download=True)
if train:
images, labels = dataset.get_train()
else:
images, labels = dataset.get_test()
images = images.view(-1, 784)
images *= intensity
# Record spikes during the simulation.
spike_record = torch.zeros(update_interval, time, n_neurons)
full_spike_record = torch.zeros(n_examples, n_neurons).long()
# Neuron assignments and spike proportions.
if train:
assignments = -torch.ones_like(torch.Tensor(n_neurons))
proportions = torch.zeros_like(torch.Tensor(n_neurons, n_classes))
rates = torch.zeros_like(torch.Tensor(n_neurons, n_classes))
ngram_scores = {}
else:
path = os.path.join(params_path,
'_'.join(['auxiliary', model_name]) + '.pt')
assignments, proportions, rates, ngram_scores = torch.load(
open(path, 'rb'))
# Sequence of accuracy estimates.
curves = {'all': [], 'proportion': [], 'ngram': []}
predictions = {scheme: torch.Tensor().long() for scheme in curves.keys()}
if train:
best_accuracy = 0
spikes = {}
for layer in set(network.layers):
spikes[layer] = Monitor(network.layers[layer],
state_vars=['s'],
time=time)
network.add_monitor(spikes[layer], name='%s_spikes' % layer)
# Train the network.
if train:
print('\nBegin training.\n')
else:
print('\nBegin test.\n')
inpt_axes = None
inpt_ims = None
spike_ims = None
spike_axes = None
weights_im = None
assigns_im = None
perf_ax = None
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 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)]
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() < 1 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()
full_spike_record[i] = spikes['Y'].get('s').t().sum(0).long()
# Optionally plot various simulation information.
if plot:
# _input = image.view(28, 28)
# reconstruction = inpts['X'].view(time, 784).sum(0).view(28, 28)
_spikes = {layer: spikes[layer].get('s') for layer in spikes}
input_exc_weights = network.connections[('X', 'Y')].w
square_weights = get_square_weights(
input_exc_weights.view(784, n_neurons), n_sqrt, 28)
# square_assignments = get_square_assignments(assignments, n_sqrt)
# inpt_axes, inpt_ims = plot_input(_input, 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_weights(square_weights, im=weights_im)
# assigns_im = plot_assignments(square_assignments, im=assigns_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(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.
if train:
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,n_neurons,n_train,inhib,lr,lr_decay,time,timestep,theta_plus,theta_decay,intensity,'
'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,n_neurons,n_train,n_test,inhib,lr,lr_decay,time,timestep,theta_plus,theta_decay,'
'intensity,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))
# Save full spike record to disk.
torch.save(full_spike_record, os.path.join(spikes_path, f))
np.argmax(sums[1]) % 10 == lbls[i - 1]]
else:
p = [((a - 1) / a) * p[0] + (1 / a) * int(np.argmax(sums[0]) == lbls[i - 1]),
((a - 1) / a) * p[1] + (1 / a) * int(np.argmax(sums[1]) % 10 == lbls[i - 1])]
perfs.append([item * 100 for item in p])
print('Performance on iteration %d: (%.2f, %.2f)' % (i / change_interval, p[0] * 100, p[1] * 100))
for m in spike_monitors:
spike_monitors[m].reset_()
if plot:
if i == 0:
spike_ims, spike_axes = plot_spikes(spike_record)
weights_im = plot_weights(get_square_weights(econn.w, sqrt, side=28))
fig, ax = plt.subplots()
im = ax.matshow(torch.stack([avg_rates, target_rates]), cmap='hot_r')
ax.set_xticks(()); ax.set_yticks([0, 1])
ax.set_yticklabels(['Actual', 'Targets'])
ax.set_aspect('auto')
ax.set_title('Difference between target and actual firing rates.')
plt.tight_layout()
fig2, ax2 = plt.subplots()
line2, = ax2.semilogy(distances, label='Exc. distance')
ax2.axhline(0, ls='--', c='r')
ax2.set_title('Sum of squared differences over time')
ax2.set_xlabel('Timesteps')
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(args):
# Random seeding.
torch.manual_seed(args.seed)
# Device.
device = torch.device("cuda" if args.gpu else "cpu")
# No. workers.
if args.n_workers == -1:
args.n_workers = args.gpu * 4 * torch.cuda.device_count()
# Build network.
network = Network(batch_size=args.batch_size)
network.add_layer(Input(shape=(1, 28, 28), traces=True), name="I")
network.add_layer(LIFNodes(n=10,
traces=True,
rest=0,
reset=0,
thresh=1,
refrac=0),
name="O")
network.add_connection(
Connection(
source=network.layers["I"],
target=network.layers["O"],
nu=(0.0, 0.01),
update_rule=Hebbian,
wmin=0.0,
wmax=1.0,
norm=100.0,
reduction=torch.sum,
),
source="I",
target="O",
)
if args.plot:
for l in network.layers:
network.add_monitor(Monitor(network.layers[l],
state_vars=("s", ),
time=args.time),
name=l)
network.to(device)
# Load dataset.
dataset = MNIST(
image_encoder=PoissonEncoder(time=args.time, dt=1.0),
label_encoder=None,
root=os.path.join(ROOT_DIR, "data", "MNIST"),
download=True,
train=False,
transform=transforms.Compose(
[transforms.ToTensor(),
transforms.Lambda(lambda x: x * 250)]),
)
# Create a dataloader to iterate and batch data
dataloader = DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.n_workers,
pin_memory=args.gpu,
)
spike_ims = None
spike_axes = None
weights_im = None
t0 = time()
for step, batch in enumerate(tqdm(dataloader)):
# Prep next input batch.
inputs = batch["encoded_image"]
inpts = {"I": inputs}
if args.gpu:
inpts = {k: v.cuda() for k, v in inpts.items()}
clamp = torch.nn.functional.one_hot(batch["label"],
num_classes=10).byte()
unclamp = ~clamp
clamp = {"O": clamp}
unclamp = {"O": unclamp}
# Run the network on the input.
network.run(
inpts=inpts,
time=args.time,
one_step=args.one_step,
clamp=clamp,
unclamp=unclamp,
)
if args.plot:
# Plot output spikes.
spikes = {
l: network.monitors[l].get("s")[:, 0]
for l in network.monitors
}
spike_ims, spike_axes = plot_spikes(spikes=spikes,
ims=spike_ims,
axes=spike_axes)
# Plot connection weights.
weights = network.connections["I", "O"].w
weights = get_square_weights(weights, n_sqrt=4, side=28)
weights_im = plot_weights(weights,
wmax=network.connections["I", "O"].wmax,
im=weights_im)
plt.pause(1e-2)
# Reset state variables.
network.reset_()
network.learning = False
for step, batch in enumerate(tqdm(dataloader)):
# Prep next input batch.
inputs = batch["encoded_image"]
inpts = {"I": inputs}
if args.gpu:
inpts = {k: v.cuda() for k, v in inpts.items()}
# Run the network on the input.
network.run(inpts=inpts, time=args.time, one_step=args.one_step)
if args.plot:
# Plot output spikes.
spikes = {
l: network.monitors[l].get("s")[:, 0]
for l in network.monitors
}
spike_ims, spike_axes = plot_spikes(spikes=spikes,
ims=spike_ims,
axes=spike_axes)
# Plot connection weights.
weights = network.connections["I", "O"].w
weights = get_square_weights(weights, n_sqrt=4, side=28)
weights_im = plot_weights(weights,
wmax=network.connections["I", "O"].wmax,
im=weights_im)
plt.pause(1e-2)
t1 = time() - t0
print(f"Time: {t1}")
training_pairs.append([spikes["TNN_Column"].get("s").sum(0), label])
if plot:
inpt_axes, inpt_ims = plot_input(
dataPoint["image"].view(28, 28),
datum.view(time, 784).sum(0).view(28, 28),
label=label,
axes=inpt_axes,
ims=inpt_ims,
)
spike_ims, spike_axes = plot_spikes(
{layer: spikes[layer].get("s").view(time, -1)
for layer in spikes},
axes=spike_axes,
ims=spike_ims,
)
weights_im = plot_weights(get_square_weights(
input_to_column.w, int(math.ceil(math.sqrt(tnn_layer_sz))), 28),
im=weights_im,
wmin=0,
wmax=max_weight)
plt.pause(1e-8)
network.reset_state_variables()
# Define logistic regression model using PyTorch.
class NN(nn.Module):
def __init__(self, input_size, num_classes):
super(NN, self).__init__()
self.linear_1 = nn.Linear(input_size, num_classes)
def forward(self, x):
out = torch.sigmoid(self.linear_1(x.float().view(-1)))
label=label,
axes=inpt_axes,
ims=inpt_ims,
)
spike_ims, spike_axes = plot_spikes(
{layer: spikes[layer].get("s").view(-1, 250) for layer in spikes},
axes=spike_axes,
ims=spike_ims,
)
voltage_ims, voltage_axes = plot_voltages(
{layer: voltages[layer].get("v").view(-1, 250) for layer in voltages},
ims=voltage_ims,
axes=voltage_axes,
)
weights_im = plot_weights(
get_square_weights(conv_conn.w, 23, 28), im=weights_im, wmin=-2, wmax=2
)
weights_im2 = plot_weights(recurrent_conn.w, im=weights_im2, wmin=-2, wmax=2)
plt.pause(1e-8)
plt.show()
# Optionally plot various simulation information.
# if plot:
# image = batch["image"].view(464, 464)
#
# inpt = inputs["X"].view(time, 215296).sum(0).view(464, 464)
# weights1 = conv_conn.w
def main(n_hidden=100, time=100, lr=5e-2, plot=False, gpu=False):
if gpu:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
network = Network()
input_layer = Input(n=784, traces=True)
hidden_layer = DiehlAndCookNodes(n=n_hidden, rest=0, reset=0, thresh=1, traces=True)
output_layer = LIFNodes(n=784, rest=0, reset=0, thresh=1, traces=True)
input_hidden_connection = Connection(
input_layer, hidden_layer, wmin=0, wmax=1, norm=75, update_rule=Hebbian, nu=[0, lr]
)
hidden_hidden_connection = Connection(
hidden_layer, hidden_layer, wmin=-500, wmax=0,
w=-500 * torch.zeros(n_hidden, n_hidden) - torch.diag(torch.ones(n_hidden))
)
hidden_output_connection = Connection(
hidden_layer, input_layer, wmin=0, wmax=1, norm=15, update_rule=Hebbian, nu=[lr, 0]
)
network.add_layer(input_layer, name='X')
network.add_layer(hidden_layer, name='H')
network.add_layer(output_layer, name='Y')
network.add_connection(input_hidden_connection, source='X', target='H')
network.add_connection(hidden_hidden_connection, source='H', target='H')
network.add_connection(hidden_output_connection, source='H', target='Y')
for layer in network.layers:
monitor = Monitor(
obj=network.layers[layer], state_vars=('s',), time=time
)
network.add_monitor(monitor, name=layer)
dataset = MNIST(
path=os.path.join(ROOT_DIR, 'data', 'MNIST'), shuffle=True, download=True
)
images, labels = dataset.get_train()
images = images.view(-1, 784)
images /= 4
labels = labels.long()
spikes_ims = None
spikes_axes = None
weights1_im = None
weights2_im = None
inpt_ims = None
inpt_axes = None
for image, label in zip(images, labels):
spikes = poisson(image, time=time, dt=network.dt)
inpts = {'X': spikes}
clamp = {'Y': spikes}
unclamp = {'Y': ~spikes}
network.run(
inpts=inpts, time=time, clamp=clamp, unclamp=unclamp
)
if plot:
spikes = {
l: network.monitors[l].get('s') for l in network.layers
}
spikes_ims, spikes_axes = plot_spikes(
spikes, ims=spikes_ims, axes=spikes_axes
)
inpt = spikes['X'].float().mean(1).view(28, 28)
rcstn = spikes['Y'].float().mean(1).view(28, 28)
inpt_axes, inpt_ims = plot_input(
inpt, rcstn, label=label, axes=inpt_axes, ims=inpt_ims
)
w1 = get_square_weights(
network.connections['X', 'H'].w.view(784, n_hidden), int(np.ceil(np.sqrt(n_hidden))), 28
)
w2 = get_square_weights(
network.connections['H', 'Y'].w.view(n_hidden, 784).t(), int(np.ceil(np.sqrt(n_hidden))), 28
)
weights1_im = plot_weights(
w1, wmin=0, wmax=1, im=weights1_im
)
weights2_im = plot_weights(
w2, wmin=0, wmax=1, im=weights2_im
)
plt.pause(0.01)
label=label,
axes=inpt_axes,
ims=inpt_ims,
)
spike_ims, spike_axes = plot_spikes(
{layer: spikes[layer].get("s").view(time,-1) for layer in spikes},
axes=spike_axes,
ims=spike_ims,
)
# voltage_ims, voltage_axes = plot_voltages(
# {layer: voltages[layer].get("v").view(-1, time) for layer in voltages},
# ims=voltage_ims,
# axes=voltage_axes,
# )
weights_im = plot_weights(
get_square_weights(C1.w, 23, 28), im=weights_im, wmin=-2, wmax=2
)
weights_im2 = plot_weights(C2.w, im=weights_im2, wmin=-2, wmax=2)
plt.pause(1e-8)
network.reset_state_variables()
# Define logistic regression model using PyTorch.
class NN(nn.Module):
def __init__(self, input_size, num_classes):
super(NN, self).__init__()
# h = int(input_size/2)
self.linear_1 = nn.Linear(input_size, num_classes)
# self.linear_1 = nn.Linear(input_size, h)
# self.linear_2 = nn.Linear(h, num_classes)
image *= 2
sample = bernoulli(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:
inpt = images[i % len(images)].view(50, 72)
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
square_weights = get_square_weights(input_exc_weights.view(
50 * 72, n_neurons),
n_sqrt,
side=(50, 72))
square_assignments = get_square_assignments(assignments, n_sqrt)
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_weights(square_weights, im=weights_im)
assigns_im = plot_assignments(square_assignments, im=assigns_im)
perf_ax = plot_performance(curves, ax=perf_ax)
def main(seed=0,
n_neurons=100,
n_train=60000,
n_test=10000,
c_low=2.5,
c_high=250,
p_low=0.1,
time=250,
dt=1,
theta_plus=0.05,
theta_decay=1e-7,
intensity=1,
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, n_neurons, n_train, c_low, c_high, p_low, time, dt, theta_plus,
theta_decay, intensity, progress_interval, update_interval
]
model_name = '_'.join([str(x) for x in params])
if not train:
test_params = [
seed, n_neurons, n_train, n_test, c_low, c_high, p_low, time, dt,
theta_plus, theta_decay, intensity, 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)
n_examples = n_train if train else n_test
n_sqrt = int(np.ceil(np.sqrt(n_neurons)))
n_classes = 10
if train:
iter_increase = int(n_train * p_low)
print(f'Iteration to increase from c_low to c_high: {iter_increase}\n')
# Build network.
if train:
network = Network(dt=dt)
input_layer = Input(n=784, traces=True)
exc_layer = DiehlAndCookNodes(n=n_neurons, traces=True)
w = torch.rand(input_layer.n, exc_layer.n)
input_exc_conn = Connection(input_layer,
exc_layer,
w=w,
update_rule=PostPre,
norm=78.4,
nu=(1e-4, 1e-2),
wmax=1.0)
w = torch.zeros(exc_layer.n, exc_layer.n)
for k1 in range(n_neurons):
for k2 in range(n_neurons):
if k1 != k2:
x1, y1 = k1 // np.sqrt(n_neurons), k1 % np.sqrt(n_neurons)
x2, y2 = k2 // np.sqrt(n_neurons), k2 % np.sqrt(n_neurons)
w[k1, k2] = max(
-c_high,
-c_low * np.sqrt(euclidean([x1, y1], [x2, y2])))
recurrent_conn = Connection(exc_layer, exc_layer, w=w)
network.add_layer(input_layer, name='X')
network.add_layer(exc_layer, name='Y')
network.add_connection(input_exc_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
# Load MNIST data.
dataset = MNIST(data_path, download=True)
if train:
images, labels = dataset.get_train()
else:
images, labels = dataset.get_test()
images = images.view(-1, 784)
images *= intensity
# Record spikes during the simulation.
spike_record = torch.zeros(update_interval, int(time / dt), 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'))
# Sequence of accuracy estimates.
curves = {'all': [], 'proportion': [], 'ngram': []}
predictions = {scheme: torch.Tensor().long() for scheme in curves.keys()}
if train:
best_accuracy = 0
spikes = {}
for layer in set(network.layers) - {'X'}:
spikes[layer] = Monitor(network.layers[layer],
state_vars=['s'],
time=int(time / dt))
network.add_monitor(spikes[layer], name='%s_spikes' % layer)
# Train the network.
if train:
print('\nBegin training.\n')
else:
print('\nBegin test.\n')
inpt_axes = None
inpt_ims = None
spike_ims = None
spike_axes = None
weights_im = None
assigns_im = None
perf_ax = None
start = t()
for i in range(n_examples):
if train and i == iter_increase:
print(
'\nChanging inhibition from low and graded to high and constant.\n'
)
w = -c_high * (torch.ones(n_neurons, n_neurons) -
torch.diag(torch.ones(n_neurons)))
network.connections['Y', 'Y'].w = w
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, labels[i - update_interval:i], 10, rates)
# Compute ngram scores.
ngram_scores = update_ngram_scores(
spike_record, labels[i - update_interval:i], 10, 2,
ngram_scores)
print()
# Get next input sample.
image = images[i]
sample = poisson(datum=image, time=int(time / 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=int(time / 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:
inpt = inpts['X'].view(time, 784).sum(0).view(28, 28)
_spikes = {layer: spikes[layer].get('s') for layer in spikes}
input_exc_weights = network.connections['X', 'Y'].w
square_weights = get_square_weights(
input_exc_weights.view(784, n_neurons), n_sqrt, 28)
square_assignments = get_square_assignments(assignments, n_sqrt)
# inpt_axes, inpt_ims = plot_input(images[i].view(28, 28), inpt, 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_weights(square_weights, im=weights_im)
# assigns_im = plot_assignments(square_assignments, im=assigns_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(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(f'\t%s: %.2f' % (scheme, float(np.mean(curves[scheme]))))
# Save accuracy curves to disk.
to_write = ['train'] + params if train else ['test'] + params
to_write = [str(x) for x in to_write]
f = '_'.join(to_write) + '.pt'
torch.save((curves, update_interval, n_examples),
open(os.path.join(curves_path, f), 'wb'))
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,n_neurons,n_train,excite,inhib,time,timestep,theta_plus,theta_decay,'
'intensity,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,n_neurons,n_train,n_test,excite,inhib,time,timestep,theta_plus,theta_decay,'
'intensity,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))
print()
def main(seed=0,
n_neurons=100,
n_train=60000,
n_test=10000,
inhib=250,
lr=1e-2,
lr_decay=1,
time=100,
dt=1,
theta_plus=0.05,
theta_decay=1e-7,
intensity=1,
progress_interval=10,
update_interval=100,
plot=False,
train=True,
gpu=False,
no_inhib=False,
no_theta=False):
assert n_train % update_interval == 0 and n_test % update_interval == 0, \
'No. examples must be divisible by update_interval'
params = [
seed, n_neurons, n_train, inhib, lr, lr_decay, time, dt, theta_plus,
theta_decay, intensity, progress_interval, update_interval
]
test_params = [
seed, n_neurons, n_train, n_test, inhib, lr, lr_decay, time, dt,
theta_plus, theta_decay, intensity, progress_interval, update_interval
]
model_name = '_'.join([str(x) for x in params])
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)
n_examples = n_train if train else n_test
n_sqrt = int(np.ceil(np.sqrt(n_neurons)))
n_classes = 10
# Build network.
if train:
network = Network()
input_layer = Input(n=784, traces=True, trace_tc=5e-2)
network.add_layer(input_layer, name='X')
output_layer = DiehlAndCookNodes(n=n_neurons,
traces=True,
rest=0,
reset=0,
thresh=5,
refrac=0,
decay=1e-2,
trace_tc=5e-2,
theta_plus=theta_plus,
theta_decay=theta_decay)
network.add_layer(output_layer, name='Y')
w = 0.3 * torch.rand(784, n_neurons)
input_connection = Connection(source=network.layers['X'],
target=network.layers['Y'],
w=w,
update_rule=WeightDependentPostPre,
nu=[0, lr],
wmin=0,
wmax=1,
norm=78.4)
network.add_connection(input_connection, source='X', target='Y')
w = -inhib * (torch.ones(n_neurons, n_neurons) -
torch.diag(torch.ones(n_neurons)))
recurrent_connection = Connection(source=network.layers['Y'],
target=network.layers['Y'],
w=w,
wmin=-inhib,
wmax=0,
update_rule=WeightDependentPostPre,
nu=[0, -100 * lr],
norm=inhib / 2 * n_neurons)
network.add_connection(recurrent_connection, source='Y', target='Y')
mask = network.connections['Y', 'Y'].w == 0
masks = {('Y', 'Y'): mask}
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.connections['Y', '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
if no_inhib:
del network.connections['Y', 'Y']
if no_theta:
network.layers['Y'].theta = 0
# Load MNIST data.
dataset = MNIST(path=data_path, download=True)
if train:
images, labels = dataset.get_train()
else:
images, labels = dataset.get_test()
images = images.view(-1, 784)
images *= intensity
labels = labels.long()
monitors = {}
for layer in set(network.layers):
if 'v' in network.layers[layer].__dict__:
monitors[layer] = Monitor(network.layers[layer],
state_vars=['s', 'v'],
time=time)
else:
monitors[layer] = Monitor(network.layers[layer],
state_vars=['s'],
time=time)
network.add_monitor(monitors[layer], name=layer)
# Train the network.
if train:
print('\nBegin training.\n')
else:
print('\nBegin test.\n')
inpt_axes = None
inpt_ims = None
spike_ims = None
spike_axes = None
voltage_ims = None
voltage_axes = None
weights_im = None
weights2_im = None
unclamps = {}
per_class = int(n_neurons / n_classes)
for label in range(n_classes):
unclamp = torch.ones(n_neurons).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 and 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={'Y': unclamps[label]},
masks=masks)
else:
network.run(inpts=inpts, time=time)
if not train:
retries = 0
while monitors['Y'].get('s').sum() == 0 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={'Y': unclamps[label]},
masks=masks)
else:
network.run(inpts=inpts, time=time)
output = monitors['Y'].get('s')
summed_neurons = output.sum(dim=1).view(n_classes, per_class)
summed_classes = summed_neurons.sum(dim=1)
prediction = torch.argmax(summed_classes).item()
correct = prediction == label
predictions[i] = prediction
corrects[i] = int(correct)
# Optionally plot various simulation information.
if plot:
# _input = image.view(28, 28)
# reconstruction = inpts['X'].view(time, 784).sum(0).view(28, 28)
# v = {'Y': monitors['Y'].get('v')}
s = {layer: monitors[layer].get('s') for layer in monitors}
input_exc_weights = network.connections['X', 'Y'].w
square_weights = get_square_weights(
input_exc_weights.view(784, n_neurons), n_sqrt, 28)
recurrent_weights = network.connections['Y', 'Y'].w
# inpt_axes, inpt_ims = plot_input(_input, reconstruction, label=labels[i], axes=inpt_axes, ims=inpt_ims)
# voltage_ims, voltage_axes = plot_voltages(v, ims=voltage_ims, axes=voltage_axes)
spike_ims, spike_axes = plot_spikes(s,
ims=spike_ims,
axes=spike_axes)
weights_im = plot_weights(square_weights, im=weights_im)
weights2_im = plot_weights(recurrent_weights,
im=weights2_im,
wmin=-inhib,
wmax=0)
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,n_neurons,n_train,inhib,lr,lr_decay,time,timestep,theta_plus,'
'theta_decay,intensity,progress_interval,update_interval,accuracy\n'
)
else:
f.write(
'random_seed,n_neurons,n_train,n_test,inhib,lr,lr_decay,time,timestep,'
'theta_plus,theta_decay,intensity,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))
# Run training data on reservoir computer and store (spikes per neuron, label) per example.
n_iters = 500
training_pairs = []
for i, (datum, label) in enumerate(loader):
if i % 100 == 0:
print('Train progress: (%d / %d)' % (i, n_iters))
network.run(inpts={'I' : datum}, time=250)
training_pairs.append([spikes['O'].get('s').sum(-1), label])
inpt_axes, inpt_ims = plot_input(images[i], datum.sum(0), label=label, axes=inpt_axes, ims=inpt_ims)
spike_ims, spike_axes = plot_spikes({layer: spikes[layer].get('s').view(-1, 250) for layer in spikes},
axes=spike_axes, ims=spike_ims)
voltage_ims, voltage_axes = plot_voltages({layer: voltages[layer].get('v').view(-1, 250) for layer in voltages},
ims=voltage_ims, axes=voltage_axes)
weights_im = plot_weights(get_square_weights(C1.w, 23, 28), im=weights_im, wmin=-2, wmax=2)
weights_im2 = plot_weights(C2.w, im=weights_im2, wmin=-2, wmax=2)
plt.pause(1e-8)
network.reset_()
if i > n_iters:
break
# Define logistic regression model using PyTorch.
class LogisticRegression(nn.Module):
def __init__(self, input_size, num_classes):
super(LogisticRegression, self).__init__()
self.linear = nn.Linear(input_size, num_classes)
network.run(inputs=inputs, time=time, clamp=clamp)
# Get voltage recording.
exc_voltages = exc_voltage_monitor.get("v")
inh_voltages = inh_voltage_monitor.get("v")
# Add to spikes recording.
spike_record[i % update_interval] = spikes["Ae"].get("s").view(
time, n_neurons)
# Optionally plot various simulation information.
if plot:
inpt = inputs["X"].view(time, 32 * 32 * 3).sum(0).view(32, 32 * 3)
input_exc_weights = network.connections[("X", "Ae")].w
square_weights = get_square_weights(
input_exc_weights.view(32 * 32 * 3, n_neurons), n_sqrt,
(32, 32 * 3))
square_assignments = get_square_assignments(assignments, n_sqrt)
voltages = {"Ae": exc_voltages, "Ai": inh_voltages}
inpt_axes, inpt_ims = plot_input(image.sum(1).view(32, 32, 3),
inpt,
label=label,
axes=inpt_axes,
ims=inpt_ims)
spike_ims, spike_axes = plot_spikes(
{
layer: spikes[layer].get("s").view(time, 1, -1)
for layer in spikes
},
ims=spike_ims,
def main():
#TEST
# hyperparameters
n_neurons = 100
n_test = 10000
inhib = 100
time = 350
dt = 1
intensity = 0.25
# extra args
progress_interval = 10
update_interval = 250
plot = True
seed = 0
train = True
gpu = False
n_classes = 10
n_sqrt = int(np.ceil(np.sqrt(n_neurons)))
# TESTING
assert n_test % update_interval == 0
np.random.seed(seed)
save_weights_fn = "plots_snn/weights/weights_test.png"
save_performance_fn = "plots_snn/performance/performance_test.png"
save_assaiments_fn = "plots_snn/assaiments/assaiments_test.png"
# load network
network = load('net_output.pt') # here goes file with network to load
network.train(False)
# pull dataset
data, targets = torch.load(
'data/MNIST/TorchvisionDatasetWrapper/processed/test.pt')
data = data * intensity
data_stretched = data.view(len(data), -1, 784)
testset = torch.utils.data.TensorDataset(data_stretched, targets)
testloader = torch.utils.data.DataLoader(testset,
batch_size=1,
shuffle=True)
# spike init
spike_record = torch.zeros(update_interval, time, n_neurons)
full_spike_record = torch.zeros(n_test, n_neurons).long()
# load parameters
assignments, proportions, rates, ngram_scores = torch.load(
'parameters_output.pt') # here goes file with parameters to load
# accuracy initialization
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='%s_spikes' % layer)
print("Begin test.")
inpt_axes = None
inpt_ims = None
spike_ims = None
spike_axes = None
weights_im = None
assigns_im = None
perf_ax = None
i = 0
current_labels = torch.zeros(update_interval)
# test
test_time = t.time()
time1 = t.time()
for sample, label in testloader:
sample = sample.view(1, 1, 28, 28)
if i % progress_interval == 0:
print(f'Progress: {i} / {n_test} took {(t.time()-time1)*10000} s')
if i % update_interval == 0 and i > 0:
# update accuracy evaluation
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)
sample_enc = poisson(datum=sample, time=time, dt=dt)
inpts = {'X': sample_enc}
# Run the network on the input.
network.run(inputs=inpts, time=time)
retries = 0
while spikes['Ae'].get('s').sum() < 1 and retries < 3:
retries += 1
sample = sample * 2
inpts = {'X': poisson(datum=sample, time=time, dt=dt)}
network.run(inputs=inpts, time=time)
# Spikes reocrding
spike_record[i % update_interval] = spikes['Ae'].get('s').view(
time, n_neurons)
full_spike_record[i] = spikes['Ae'].get('s').view(
time, n_neurons).sum(0).long()
if plot:
_input = sample.view(28, 28)
reconstruction = inpts['X'].view(time, 784).sum(0).view(28, 28)
_spikes = {layer: spikes[layer].get('s') for layer in spikes}
input_exc_weights = network.connections[('X', 'Ae')].w
square_assignments = get_square_assignments(assignments, n_sqrt)
assigns_im = plot_assignments(square_assignments, im=assigns_im)
if i % update_interval == 0: # plot weights on every update interval
square_weights = get_square_weights(
input_exc_weights.view(784, n_neurons), n_sqrt, 28)
weights_im = plot_weights(square_weights, im=weights_im)
[weights_im,
save_weights_fn] = plot_weights(square_weights,
im=weights_im,
save=save_weights_fn)
inpt_axes, inpt_ims = plot_input(_input,
reconstruction,
label=label,
axes=inpt_axes,
ims=inpt_ims)
spike_ims, spike_axes = plot_spikes(_spikes,
ims=spike_ims,
axes=spike_axes)
assigns_im = plot_assignments(square_assignments,
im=assigns_im,
save=save_assaiments_fn)
perf_ax = plot_performance(curves,
ax=perf_ax,
save=save_performance_fn)
plt.pause(1e-8)
current_labels[i % update_interval] = label[0]
network.reset_state_variables()
if i % 10 == 0 and i > 0:
preds = ngram(
spike_record[i % update_interval - 10:i % update_interval],
ngram_scores, n_classes, 2)
print(f'Predictions: {(preds*1.0).numpy()}')
print(
f'True value: {current_labels[i%update_interval-10:i%update_interval].numpy()}'
)
time1 = t.time()
i += 1
# Compute confusion matrices and save them to disk.
confusions = {}
for scheme in predictions:
confusions[scheme] = confusion_matrix(targets, predictions[scheme])
to_write = 'confusion_test'
f = '_'.join([str(x) for x in to_write]) + '.pt'
torch.save(confusions, os.path.join('.', f))
print("Test completed. Testing took " + str((t.time() - test_time) / 6) +
" min.")
def main(seed=0,
n_neurons=100,
n_train=60000,
n_test=10000,
inhib=250,
time=50,
lr=1e-2,
lr_decay=0.99,
dt=1,
theta_plus=0.05,
theta_decay=1e-7,
progress_interval=10,
update_interval=250,
train=True,
plot=False,
gpu=False):
assert n_train % update_interval == 0 and n_test % update_interval == 0, \
'No. examples must be divisible by update_interval'
params = [
seed, n_neurons, n_train, inhib, time, lr, lr_decay, theta_plus,
theta_decay, progress_interval, update_interval
]
test_params = [
seed, n_neurons, n_train, n_test, inhib, time, lr, lr_decay,
theta_plus, theta_decay, progress_interval, update_interval
]
model_name = '_'.join([str(x) for x in params])
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)
if train:
n_examples = n_train
else:
n_examples = n_test
n_sqrt = int(np.ceil(np.sqrt(n_neurons)))
n_classes = 10
# Build network.
if train:
network = Network(dt=dt)
input_layer = Input(n=784, traces=True, trace_tc=5e-2)
network.add_layer(input_layer, name='X')
output_layer = DiehlAndCookNodes(n=n_neurons,
traces=True,
rest=0,
reset=0,
thresh=1,
refrac=0,
decay=1e-2,
trace_tc=5e-2,
theta_plus=theta_plus,
theta_decay=theta_decay)
network.add_layer(output_layer, name='Y')
w = 0.3 * torch.rand(784, n_neurons)
input_connection = Connection(source=network.layers['X'],
target=network.layers['Y'],
w=w,
update_rule=PostPre,
nu=[0, lr],
wmin=0,
wmax=1,
norm=78.4)
network.add_connection(input_connection, source='X', target='Y')
w = -inhib * (torch.ones(n_neurons, n_neurons) -
torch.diag(torch.ones(n_neurons)))
recurrent_connection = Connection(source=network.layers['Y'],
target=network.layers['Y'],
w=w,
wmin=-inhib,
wmax=0)
network.add_connection(recurrent_connection, source='Y', target='Y')
else:
path = os.path.join('..', '..', 'params', data, model)
network = load_network(os.path.join(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
# Load Fashion-MNIST data.
dataset = FashionMNIST(path=os.path.join('..', '..', 'data',
'FashionMNIST'),
download=True)
if train:
images, labels = dataset.get_train()
else:
images, labels = dataset.get_test()
images = images.view(-1, 784)
images = images / 255
# if train:
# for i in range(n_neurons):
# network.connections['X', 'Y'].w[:, i] = images[i] + images[i].mean() * torch.randn(784)
# 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, n_classes))
rates = torch.zeros_like(torch.Tensor(n_neurons, n_classes))
ngram_scores = {}
else:
path = os.path.join('..', '..', 'params', data, model)
path = os.path.join(path, '_'.join(['auxiliary', model_name]) + '.pt')
assignments, proportions, rates, ngram_scores = torch.load(
open(path, 'rb'))
# Sequence of accuracy estimates.
curves = {'all': [], 'proportion': [], 'ngram': []}
if train:
best_accuracy = 0
spikes = {}
for layer in set(network.layers):
spikes[layer] = Monitor(network.layers[layer],
state_vars=['s'],
time=time)
network.add_monitor(spikes[layer], name='%s_spikes' % layer)
# Train the network.
if train:
print('\nBegin training.\n')
else:
print('\nBegin test.\n')
inpt_axes = None
inpt_ims = None
spike_ims = None
spike_axes = None
weights_im = None
assigns_im = None
perf_ax = 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, predictions = update_curves(curves,
current_labels,
n_classes,
spike_record=spike_record,
assignments=assignments,
proportions=proportions,
ngram_scores=ngram_scores,
n=2)
print_results(curves)
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.
path = os.path.join('..', '..', 'params', data, model)
if not os.path.isdir(path):
os.makedirs(path)
network.save(os.path.join(path, model_name + '.pt'))
path = os.path.join(
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 % n_examples]
sample = rank_order(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 = rank_order(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:
_input = images[i % n_examples].view(28, 28)
reconstruction = inpts['X'].view(time, 784).sum(0).view(28, 28)
_spikes = {layer: spikes[layer].get('s') for layer in spikes}
input_exc_weights = network.connections['X', 'Y'].w
square_weights = get_square_weights(
input_exc_weights.view(784, n_neurons), n_sqrt, 28)
square_assignments = get_square_assignments(assignments, n_sqrt)
# inpt_axes, inpt_ims = plot_input(_input, 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_weights(square_weights, im=weights_im, wmax=0.25)
# assigns_im = plot_assignments(square_assignments, im=assigns_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(images) - update_interval:i %
len(images)]
# Update and print accuracy evaluations.
curves, predictions = update_curves(curves,
current_labels,
n_classes,
spike_record=spike_record,
assignments=assignments,
proportions=proportions,
ngram_scores=ngram_scores,
n=2)
print_results(curves)
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.
if train:
path = os.path.join('..', '..', 'params', data, model)
if not os.path.isdir(path):
os.makedirs(path)
network.save(os.path.join(path, model_name + '.pt'))
path = os.path.join(
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.
path = os.path.join('..', '..', 'curves', data, model)
if not os.path.isdir(path):
os.makedirs(path)
if train:
to_write = ['train'] + params
else:
to_write = ['test'] + params
to_write = [str(x) for x in to_write]
f = '_'.join(to_write) + '.pt'
torch.save((curves, update_interval, n_examples),
open(os.path.join(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'])
]
if train:
to_write = params + results
else:
to_write = test_params + results
to_write = [str(x) for x in to_write]
if train:
name = 'train.csv'
else:
name = 'test.csv'
if not os.path.isfile(os.path.join(path, name)):
with open(os.path.join(path, name), 'w') as f:
if train:
f.write(
'random_seed,n_neurons,n_train,inhib,time,lr,lr_decay,theta_plus,theta_decay,'
'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,n_neurons,n_train,n_test,inhib,time,lr,lr_decay,theta_plus,theta_decay,'
'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(path, name), 'a') as f:
f.write(','.join(to_write) + '\n')
if plot:
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]
spike_ims, spike_axes = plot_spikes(spikes,
ims=spike_ims,
axes=spike_axes)
weights1_im = plot_weights(w, im=weights1_im, wmin=-1, wmax=1)
w = network.connections['X', 'Y'].w
square_weights = get_square_weights(w, sqrt, 28)
weights2_im = plot_weights(square_weights,
im=weights2_im,
wmin=-1,
wmax=1)
plt.pause(1e-8)
network.reset_() # Reset state variables.
accuracies.append(correct.mean() * 100)
if train:
lr *= lr_decay
if accuracies[-1] > best:
else:
image = pipeline.obs.view(3, 32, 32).numpy().transpose(
1, 2, 0) / intensity
image /= image.max()
inpt = 255 - pipeline.encoded['X'].view(
time, 3 * 32 * 32).sum(0).view(3, 32, 32).sum(0)
weights = network.connections[('X',
'Ae')].w.view(3, 32, 32,
n_neurons).numpy()
weights = weights.transpose(1, 2, 0,
3).sum(2).reshape(32 * 32, n_neurons)
weights = torch.from_numpy(weights)
square_assignments = get_square_assignments(assignments, n_sqrt)
square_weights = get_square_weights(weights, n_sqrt, 32)
if i == 0:
inpt_axes, inpt_ims = plot_input(image, inpt, label=labels[i])
assigns_im = plot_assignments(square_assignments, classes=classes)
perf_ax = plot_performance(accuracy)
weights_ax = plot_weights(square_weights, wmin=0.0, wmax=0.025)
else:
inpt_axes, inpt_ims = plot_input(image,
inpt,
label=labels[i],
axes=inpt_axes,
ims=inpt_ims)
assigns_im = plot_assignments(square_assignments, im=assigns_im)
perf_ax = plot_performance(accuracy, ax=perf_ax)
weights_im = plot_weights(square_weights, im=weights_ax)
def plot_every_step(
self,
batch: Dict[str, torch.Tensor],
inputs: Dict[str, torch.Tensor],
spikes: Monitor,
voltages: Monitor,
timestep: float,
network: Network,
accuracy: Dict[str, List[float]] = None,
) -> None:
"""
Visualize network's training process.
*** This function is currently broken and unusable. ***
:param batch: Current batch from dataset.
:param inputs: Current inputs from batch.
:param spikes: Spike monitor.
:param voltages: Voltage monitor.
:param timestep: Timestep of the simulation.
:param network: Network object.
:param accuracy: Network accuracy.
"""
n_inpt = network.n_inpt
n_neurons = network.n_neurons
n_outpt = network.n_outpt
inpt_sqrt = int(np.ceil(np.sqrt(n_inpt)))
neu_sqrt = int(np.ceil(np.sqrt(n_neurons)))
outpt_sqrt = int(np.ceil(np.sqrt(n_outpt)))
inpt_view = (inpt_sqrt, inpt_sqrt)
image = batch["image"].view(inpt_view)
inpt = inputs["X"].view(timestep, n_inpt).sum(0).view(inpt_view)
input_exc_weights = network.connections[("X", "Y")].w
in_square_weights = get_square_weights(
input_exc_weights.view(n_inpt, n_neurons), neu_sqrt, inpt_sqrt)
output_exc_weights = network.connections[("Y", "Z")].w
out_square_weights = get_square_weights(
output_exc_weights.view(n_neurons, n_outpt), outpt_sqrt, neu_sqrt)
spikes_ = {layer: spikes[layer].get("s") for layer in spikes}
#voltages_ = {'Y': voltages['Y'].get("v")}
voltages_ = {layer: voltages[layer].get("v") for layer in voltages}
""" For mini-batch.
# image = batch["image"][:, 0].view(28, 28)
# inpt = inputs["X"][:, 0].view(time, 784).sum(0).view(28, 28)
# spikes_ = {
# layer: spikes[layer].get("s")[:, 0].contiguous() for layer in spikes
# }
"""
# self.inpt_axes, self.inpt_ims = plot_input(
# image, inpt, label=batch["label"], axes=self.inpt_axes, ims=self.inpt_ims
# )
self.spike_ims, self.spike_axes = plot_spikes(spikes_,
ims=self.spike_ims,
axes=self.spike_axes)
self.in_weights_im = plot_weights(in_square_weights,
im=self.in_weights_im)
self.out_weights_im = plot_weights(out_square_weights,
im=self.out_weights_im)
if accuracy is not None:
self.perf_ax = plot_performance(accuracy, ax=self.perf_ax)
self.voltage_ims, self.voltage_axes = plot_voltages(
voltages_,
ims=self.voltage_ims,
axes=self.voltage_axes,
plot_type="line")
plt.pause(1e-4)
'''class_spike=torch.zeros(10)
print(label[0])
for i in range(time):
for j in range(n_neurons):
if spikes["Ae"].get("s")[i,0,j]:
class_spike[j//10] += 1
print("\nlabel\n")
print(class_spike)
'''
# Optionally plot various simulation information.
if plot:
inpt = inputs["X"].view(time, 784 // 4).sum(0).view(14, 14)
input_exc_weights = network.connections[("X", "Ae")].w
square_weights = get_square_weights(
input_exc_weights.view((784 // 4), n_neurons), n_sqrt, 14)
square_assignments = get_square_assignments(assignments, n_sqrt)
voltages = {"Ae": exc_voltages, "Ai": inh_voltages}
inpt_axes, inpt_ims = plot_input(image.sum(1).view(14, 14),
inpt,
label=label,
axes=inpt_axes,
ims=inpt_ims)
spike_ims, spike_axes = plot_spikes(
{
layer: spikes[layer].get("s").view(time, 1, -1)
for layer in spikes
},
ims=spike_ims,
def plot_weight_maps(
self,
weights: torch.Tensor,
fig_shape: Tuple[int, int] = (3, 3),
c_min: float = 0.0,
c_max: float = 1.0,
cmap: str = cmap,
overview: bool = False,
gif: bool = False,
save: bool = False,
file_path: str = str(uuid.uuid4()),
) -> None:
"""
Plot overview image of all weight maps (all neurons).
or
Plot weight maps of certain neurons ([fig_shape] number of neurons).
:param weights: Weight matrix of ``Connection`` object.
:param fig_shape: Horizontal, vertical figure shape for plot.
:param c_min: Lower bound of the range that the colormap covers.
:param c_max: Upper bound of the range that the colormap covers.
:param cmap: Matplotlib colormap.
:param overview: Whether to plot overview image of all weight maps.
:param gif: Save plot of weight maps for gif.
:param save: Whether to save the plot's figure.
:param file_path: Path (contains filename) to use when saving the object.
"""
# Turn the interactive mode off if just for saving.
if save or gif:
plt.ioff()
# Detach the weight from computational graph and clone it.
weights = weights.detach().clone()
# Number of neurons from front layer.
n_pre_neu = len(weights)
# Calculate the perfect square which is closest to the number of neurons.
size = int(np.sqrt(n_pre_neu))
# max_size = 30
# if restrict and size > max_size:
# size = max_size
sq_size = size**2
sq_shape = (size, size)
# Convert torch Tensor to numpy Array and transpose it.
weights = weights.cpu().numpy().T
# weights = weights.detach().clone().cpu().numpy().T
# Number of neurons from current layer.
n_post_neu = len(weights)
if overview:
### Plot overview image of all weight maps! ###
# Transpose and convert back to torch Tensor.
weights = torch.from_numpy(weights.T)
# Square root of number of neurons from current layer.
neu_sqrt = int(np.sqrt(n_post_neu))
# BindsNET's functions.
square_weights = get_square_weights(weights, neu_sqrt, size)
plot_weights(square_weights, cmap=cmap, wmin=c_min, wmax=c_max)
else:
### Plot a few of neurons' weight maps only! ###
# Create figure of shape (m, n).
fig, axes = plt.subplots(ncols=fig_shape[0], nrows=fig_shape[1])
fig.subplots_adjust(right=0.8, hspace=0.28)
index = 0
for cols in axes:
for ax in cols:
if index >= n_post_neu:
ax.axis('off')
continue
# Slice the array of weight map to fit perfect square's shape.
if len(weights[index]) > sq_size:
tmp_weights = weights[index][:sq_size]
else:
tmp_weights = weights[index]
tmp_weights = tmp_weights.reshape(sq_shape)
im = ax.imshow(tmp_weights,
cmap=cmap,
vmin=c_min,
vmax=c_max)
ax.set_title('Map ({})'.format(index))
ax.tick_params(labelbottom=False,
labelleft=False,
labelright=False,
labeltop=False,
bottom=False,
left=False,
right=False,
top=False)
index += 1
# cbar_ax = fig.add_axes([0.85, 0.11, 0.03, 0.77])
cbar_ax = fig.add_axes([0.85, 0.15, 0.03, 0.7])
fig.colorbar(im, cax=cbar_ax)
if gif:
self.wmaps_for_gif()
if save:
self.save_plot(file_path=file_path)
plt.close()
network.run(inputs={"I": datum}, time=time)
training_pairs.append(
[spikes["TNN_1"].get("s").view(time, -1).sum(0), label])
if plot and i % 10 == 0:
spike_ims, spike_axes = plot_spikes(
{layer: spikes[layer].get("s").view(time, -1)
for layer in spikes},
axes=spike_axes,
ims=spike_ims,
)
if (i == 0):
for axis in spike_axes:
axis.set_xticks(range(time))
axis.set_xticklabels(range(time))
weights_im = plot_weights(get_square_weights(
C1.w, int(math.ceil(math.sqrt(tnn_layer_sz))), 28),
im=weights_im,
wmin=0,
wmax=max_weight)
plt.pause(1e-12)
network.reset_state_variables()
# Define logistic regression model using PyTorch.
class NN(nn.Module):
def __init__(self, input_size, num_classes):
super(NN, self).__init__()
# h = int(input_size/2)
self.linear_1 = nn.Linear(input_size, num_classes)
# self.linear_1 = nn.Linear(input_size, h)
def main(seed=0, n_neurons=100, n_train=60000, n_test=10000, inhib=100, lr=0.01, lr_decay=1, time=350, dt=1,
theta_plus=0.05, theta_decay=1e-7, 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, n_neurons, n_train, inhib, lr_decay, time, dt,
theta_plus, theta_decay, progress_interval, update_interval
]
model_name = '_'.join([str(x) for x in params])
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)
n_examples = n_train if train else n_test
n_classes = 10
# Build network.
if train:
network = Network(dt=dt)
input_layer = Input(n=784, traces=True, trace_tc=5e-2)
network.add_layer(input_layer, name='X')
output_layer = DiehlAndCookNodes(
n=n_classes, rest=0, reset=1, thresh=1, decay=1e-2,
theta_plus=theta_plus, theta_decay=theta_decay, traces=True, trace_tc=5e-2
)
network.add_layer(output_layer, name='Y')
w = torch.rand(784, n_classes)
input_connection = Connection(
source=input_layer, target=output_layer, w=w,
update_rule=MSTDPET, nu=lr, wmin=0, wmax=1,
norm=78.4, tc_e_trace=0.1
)
network.add_connection(input_connection, source='X', target='Y')
else:
network = load(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 = torch.IntTensor([0])
network.layers['Y'].theta_plus = torch.IntTensor([0])
# Load MNIST data.
environment = MNISTEnvironment(
dataset=MNIST(root=data_path, download=True), train=train, time=time
)
# Create pipeline.
pipeline = Pipeline(
network=network, environment=environment, encoding=repeat,
action_function=select_spiked, output='Y', reward_delay=None
)
spikes = {}
for layer in set(network.layers):
spikes[layer] = Monitor(network.layers[layer], state_vars=('s',), time=time)
network.add_monitor(spikes[layer], name='%s_spikes' % layer)
if train:
network.add_monitor(Monitor(
network.connections['X', 'Y'].update_rule, state_vars=('tc_e_trace',), time=time
), 'X_Y_e_trace')
# Train the network.
if train:
print('\nBegin training.\n')
else:
print('\nBegin test.\n')
spike_ims = None
spike_axes = None
weights_im = None
elig_axes = None
elig_ims = None
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 > 0 and train:
network.connections['X', 'Y'].update_rule.nu[1] *= lr_decay
# Run the network on the input.
# print("Example",i,"Results:")
# for j in range(time):
# result = pipeline.env_step()
# pipeline.step(result,a_plus=1, a_minus=0)
# print(result)
for j in range(time):
pipeline.train()
if not train:
_spikes = {layer: spikes[layer].get('s') for layer in spikes}
if plot:
_spikes = {layer: spikes[layer].get('s') for layer in spikes}
w = network.connections['X', 'Y'].w
square_weights = get_square_weights(w.view(784, n_classes), 4, 28)
spike_ims, spike_axes = plot_spikes(_spikes, ims=spike_ims, axes=spike_axes)
weights_im = plot_weights(square_weights, im=weights_im)
elig_ims, elig_axes = plot_voltages(
{'Y': network.monitors['X_Y_e_trace'].get('e_trace').view(-1, time)[1500:2000]},
plot_type='line', ims=elig_ims, axes=elig_axes
)
plt.pause(1e-8)
pipeline.reset_state_variables() # Reset state variables.
network.connections['X', 'Y'].update_rule.tc_e_trace = torch.zeros(784, n_classes)
print(f'Progress: {n_examples} / {n_examples} ({t() - start:.4f} seconds)')
if train:
network.save(os.path.join(params_path, model_name + '.pt'))
print('\nTraining complete.\n')
else:
print('\nTest complete.\n')
label=label,
axes=inpt_axes,
ims=inpt_ims)
spike_ims, spike_axes = plot_spikes(
{layer: spikes[layer].get("s").view(-1, 250)
for layer in spikes},
axes=spike_axes,
ims=spike_ims,
)
voltage_ims, voltage_axes = plot_voltages(
{layer: voltages[layer].get("v").view(-1, 250)
for layer in voltages},
ims=voltage_ims,
axes=voltage_axes,
)
weights_im = plot_weights(get_square_weights(C1.w, 23, 28),
im=weights_im,
wmin=-2,
wmax=2)
weights_im2 = plot_weights(C2.w, im=weights_im2, wmin=-2, wmax=2)
plt.pause(1e-8)
network.reset_()
if i > n_iters:
break
# Define logistic regression model using PyTorch.
class LogisticRegression(nn.Module):
def __init__(self, input_size, num_classes):
def main(args):
update_interval = args.update_steps * args.batch_size
# Sets up GPU use
torch.backends.cudnn.benchmark = False
if args.gpu and torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
else:
torch.manual_seed(args.seed)
# Determines number of workers to use
if args.n_workers == -1:
args.n_workers = args.gpu * 4 * torch.cuda.device_count()
n_sqrt = int(np.ceil(np.sqrt(args.n_neurons)))
if args.reduction == "sum":
reduction = torch.sum
elif args.reduction == "mean":
reduction = torch.mean
elif args.reduction == "max":
reduction = max_without_indices
else:
raise NotImplementedError
# Build network.
network = DiehlAndCook2015v2(
n_inpt=784,
n_neurons=args.n_neurons,
inh=args.inh,
dt=args.dt,
norm=78.4,
nu=(0.0, 1e-2),
reduction=reduction,
theta_plus=args.theta_plus,
inpt_shape=(1, 28, 28),
)
# Directs network to GPU.
if args.gpu:
network.to("cuda")
# Load MNIST data.
dataset = MNIST(
PoissonEncoder(time=args.time, dt=args.dt),
None,
root=os.path.join(ROOT_DIR, "data", "MNIST"),
download=True,
train=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Lambda(lambda x: x * args.intensity)
]),
)
dataset, valid_dataset = torch.utils.data.random_split(
dataset, [59000, 1000])
test_dataset = MNIST(
PoissonEncoder(time=args.time, dt=args.dt),
None,
root=os.path.join(ROOT_DIR, "data", "MNIST"),
download=True,
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Lambda(lambda x: x * args.intensity)
]),
)
# Neuron assignments and spike proportions.
n_classes = 10
assignments = -torch.ones(args.n_neurons)
proportions = torch.zeros(args.n_neurons, n_classes)
rates = torch.zeros(args.n_neurons, n_classes)
# Set up monitors for spikes and voltages
spikes = {}
for layer in set(network.layers):
spikes[layer] = Monitor(network.layers[layer],
state_vars=["s"],
time=args.time)
network.add_monitor(spikes[layer], name="%s_spikes" % layer)
weights_im = None
spike_ims, spike_axes = None, None
# Record spikes for length of update interval.
spike_record = torch.zeros(update_interval, args.time, args.n_neurons)
if os.path.isdir(args.log_dir):
shutil.rmtree(args.log_dir)
# Summary writer.
writer = SummaryWriter(log_dir=args.log_dir, flush_secs=60)
for epoch in range(args.n_epochs):
print(f"\nEpoch: {epoch}\n")
labels = []
# Get training data loader.
dataloader = DataLoader(
dataset=dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.n_workers,
pin_memory=args.gpu,
)
for step, batch in enumerate(dataloader):
print(f"Step: {step} / {len(dataloader)}")
global_step = 60000 * epoch + args.batch_size * step
if step % args.update_steps == 0 and step > 0:
# Disable learning.
network.train(False)
# Get test data loader.
valid_dataloader = DataLoader(
dataset=valid_dataset,
batch_size=args.test_batch_size,
shuffle=True,
num_workers=args.n_workers,
pin_memory=args.gpu,
)
test_labels = []
test_spike_record = torch.zeros(len(valid_dataset), args.time,
args.n_neurons)
t0 = time()
for test_step, test_batch in enumerate(valid_dataloader):
# Prep next input batch.
inpts = {"X": test_batch["encoded_image"]}
if args.gpu:
inpts = {k: v.cuda() for k, v in inpts.items()}
# Run the network on the input (inference mode).
network.run(inpts=inpts,
time=args.time,
one_step=args.one_step)
# Add to spikes recording.
s = spikes["Y"].get("s").permute((1, 0, 2))
test_spike_record[(test_step * args.test_batch_size
):(test_step * args.test_batch_size) +
s.size(0)] = s
# Plot simulation data.
if args.valid_plot:
input_exc_weights = network.connections["X", "Y"].w
square_weights = get_square_weights(
input_exc_weights.view(784, args.n_neurons),
n_sqrt, 28)
spikes_ = {
layer: spikes[layer].get("s")[:, 0]
for layer in spikes
}
spike_ims, spike_axes = plot_spikes(spikes_,
ims=spike_ims,
axes=spike_axes)
weights_im = plot_weights(square_weights,
im=weights_im)
plt.pause(1e-8)
# Reset state variables.
network.reset_()
test_labels.extend(test_batch["label"].tolist())
t1 = time() - t0
writer.add_scalar(tag="time/test",
scalar_value=t1,
global_step=global_step)
# Convert the list of labels into a tensor.
test_label_tensor = torch.tensor(test_labels)
# Get network predictions.
all_activity_pred = all_activity(
spikes=test_spike_record,
assignments=assignments,
n_labels=n_classes,
)
proportion_pred = proportion_weighting(
spikes=test_spike_record,
assignments=assignments,
proportions=proportions,
n_labels=n_classes,
)
writer.add_scalar(
tag="accuracy/valid/all vote",
scalar_value=100 * torch.mean(
(test_label_tensor.long()
== all_activity_pred).float()),
global_step=global_step,
)
writer.add_scalar(
tag="accuracy/valid/proportion weighting",
scalar_value=100 * torch.mean(
(test_label_tensor.long() == proportion_pred).float()),
global_step=global_step,
)
square_weights = get_square_weights(
network.connections["X", "Y"].w.view(784, args.n_neurons),
n_sqrt,
28,
)
img_tensor = colorize(square_weights, cmap="hot_r")
writer.add_image(
tag="weights",
img_tensor=img_tensor,
global_step=global_step,
dataformats="HWC",
)
# Convert the array of labels into a tensor
label_tensor = torch.tensor(labels)
# Get network predictions.
all_activity_pred = all_activity(spikes=spike_record,
assignments=assignments,
n_labels=n_classes)
proportion_pred = proportion_weighting(
spikes=spike_record,
assignments=assignments,
proportions=proportions,
n_labels=n_classes,
)
writer.add_scalar(
tag="accuracy/train/all vote",
scalar_value=100 * torch.mean(
(label_tensor.long() == all_activity_pred).float()),
global_step=global_step,
)
writer.add_scalar(
tag="accuracy/train/proportion weighting",
scalar_value=100 * torch.mean(
(label_tensor.long() == proportion_pred).float()),
global_step=global_step,
)
# Assign labels to excitatory layer neurons.
assignments, proportions, rates = assign_labels(
spikes=spike_record,
labels=label_tensor,
n_labels=n_classes,
rates=rates,
)
# Re-enable learning.
network.train(True)
labels = []
labels.extend(batch["label"].tolist())
# Prep next input batch.
inpts = {"X": batch["encoded_image"]}
if args.gpu:
inpts = {k: v.cuda() for k, v in inpts.items()}
# Run the network on the input (training mode).
t0 = time()
network.run(inpts=inpts, time=args.time, one_step=args.one_step)
t1 = time() - t0
writer.add_scalar(tag="time/train/step",
scalar_value=t1,
global_step=global_step)
# Add to spikes recording.
s = spikes["Y"].get("s").permute((1, 0, 2))
spike_record[(step * args.batch_size) %
update_interval:(step * args.batch_size %
update_interval) + s.size(0)] = s
# Plot simulation data.
if args.plot:
input_exc_weights = network.connections["X", "Y"].w
square_weights = get_square_weights(
input_exc_weights.view(784, args.n_neurons), n_sqrt, 28)
spikes_ = {
layer: spikes[layer].get("s")[:, 0]
for layer in spikes
}
spike_ims, spike_axes = plot_spikes(spikes_,
ims=spike_ims,
axes=spike_axes)
weights_im = plot_weights(square_weights, im=weights_im)
plt.pause(1e-8)
# Reset state variables.
network.reset_()
inpts = {"X": image.view(time, 1, 28, 28)}
network.run(inpts=inpts, time=time, clamp=clamp)
# Get voltage recording.
exc_voltages = exc_voltage_monitor.get("v")
inh_voltages = inh_voltage_monitor.get("v")
# Add to spikes recording.
spike_record[i % update_interval] = spikes["Ae"].get("s").view(
50, n_neurons)
# Optionally plot various simulation information.
if plot:
inpt = inpts["X"].view(time, 784).sum(0).view(28, 28)
input_exc_weights = network.connections[("X", "Ae")].w
square_weights = get_square_weights(
input_exc_weights.view(784, n_neurons), n_sqrt, 28)
square_assignments = get_square_assignments(assignments, n_sqrt)
voltages = {"Ae": exc_voltages, "Ai": inh_voltages}
if i == 0:
inpt_axes, inpt_ims = plot_input(image.sum(1).view(28, 28),
inpt,
label=label)
spike_ims, spike_axes = plot_spikes(
{layer: spikes[layer].get("s")
for layer in spikes})
weights_im = plot_weights(square_weights)
assigns_im = plot_assignments(square_assignments)
perf_ax = plot_performance(accuracy)
voltage_ims, voltage_axes = plot_voltages(voltages)
retries += 1
image *= 2
sample = bernoulli(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['Ae'].get('s').t()
# Optionally plot various simulation information.
if plot:
inpt = images[i].view(80, 80)
reconstruction = inpts['X'].view(time, 6400).sum(0).view(80, 80)
_spikes = {layer: spikes[layer].get('s') for layer in spikes}
input_exc_weights = network.connections[('X', 'Ae')].w
square_weights = get_square_weights(input_exc_weights.view(6400, n_neurons), n_sqrt, 80)
square_assignments = get_square_assignments(assignments, n_sqrt)
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_weights(square_weights, im=weights_im)
assigns_im = plot_assignments(square_assignments, im=assigns_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
