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snnlib.py
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snnlib.py
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"""
snnlib.py
@description A tiny library to use BindsNET easily.
@author Hiroshi ARAKI
@source https://github.com/HiroshiARAKI/snnlibpy
@contact araki@hirlab.net
@Website https://hirlab.net
"""
from .additional_encoders import FixedFrequencyEncoder, LIFEncoder, LIFEncoder2
import torch
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from bindsnet.network import Network
from bindsnet.network.nodes import (Nodes, Input, LIFNodes, IFNodes, IzhikevichNodes,
SRM0Nodes, DiehlAndCookNodes, AdaptiveLIFNodes)
from bindsnet.network.topology import Connection
from bindsnet.network.monitors import Monitor
from bindsnet.analysis.plotting import plot_spikes
from bindsnet.learning import PostPre, NoOp, WeightDependentPostPre
from bindsnet.encoding import PoissonEncoder, SingleEncoder, RepeatEncoder, RankOrderEncoder, BernoulliEncoder
from bindsnet.datasets import MNIST
from bindsnet.evaluation import assign_labels, all_activity, proportion_weighting
from tqdm import tqdm
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
from time import time
__version__ = '0.2.6'
class Spiking:
"""
The Class to simulate Spiking Neural Networks.
"""
# ======= Constants ======= #
LIF = LIFNodes
IF = IFNodes
IZHIKEVICH = IzhikevichNodes
SRM = SRM0Nodes
DIEHL_COOK = DiehlAndCookNodes
ADAPTIVE_LIF = AdaptiveLIFNodes
POISSON = PoissonEncoder
SINGLE = SingleEncoder
REPEAT = RepeatEncoder
RANK_ORDER = RankOrderEncoder
BERNOULI = BernoulliEncoder
FIXED_FREQUENCY = FixedFrequencyEncoder
LIF_ENCODER = LIFEncoder
LIF_ENCODER_2 = LIFEncoder2
NO_STDP: str = 'No_STDP'
SIMPLE_STDP: str = 'Simple_STDP'
WEIGHT_DEPENDENT_STDP: str = 'Weight_dependent_STDP'
W_NORMAL_DIST: int = 0 # initialize with Normal Distribution
W_RANDOM: int = 1 # initialize with Uniform Distribution [sw_min, sw_max]
W_SIMPLE_RAND: int = 3 # initialize Uniform Distribution[0, scale]
PROJECT_ROOT: str = os.getcwd()
IMAGE_DIR: str = PROJECT_ROOT + '/images/'
DPI: int = 150 # the dpi value of plt.savefig()
rest_voltage = -65 # [mV] resting potential
reset_voltage = -60 # [mV] reset potential
threshold = -52 # [mV] firing threshold
refractory_period = 5 # [ms] refractory period
# if you encode images with poisson process, then [Hz] firing rate of input spikes
# this intensity should be changed by the Encoder you use
intensity: float = 128
seed = 0 # a seed of random
# ======================== #
def __init__(self, input_l: int, obs_time: int = 300, dt: float = 1.0,
input_shape=(1, 28, 28)):
"""
Constructor: Build SNN easily. Initialize many variables in backend.
:param input_l:
:param obs_time:
"""
print('You Called Spiking Neural Networks Library "WBN"!!')
print('=> WrappedBindsNET (This Library) :version. %s' % __version__)
self.network: Network = Network(dt=dt) # Core of SNN
self.layer_index = 0 # index of last fc-layer
self.input_l = input_l # num of input layer neurons
self.monitors = {} # monitors to manege spikes and voltages activities
self.T = obs_time # simulation time (duration)
self.dt = dt # time step
self.input_layer_name = 'in'
self.train_data = None
self.test_data = None
self.train_loader = None
self.test_loader = None
self.train_data_num = None
self.test_data_num = None
self.batch = 1
self.label_num = 0
self.layer_names = []
self.history = {
'test_acc': [], 'train_acc': [],
'test_pro': [], 'train_pro': [],
}
self.gpu = torch.cuda.is_available() # Is GPU available?
self.workers = self.gpu * 4 * torch.cuda.device_count()
np.random.seed(self.seed)
if self.gpu:
torch.cuda.manual_seed_all(self.seed)
else:
torch.manual_seed(self.seed)
self.assignments = None
self.proportions = None
self.rates = None
input_layer = Input(n=input_l, traces=True, shape=input_shape)
self.network.add_layer(layer=input_layer, name=self.input_layer_name)
self.layer_names.append(self.input_layer_name)
# information of the last added layer
self.pre = {
'layer': input_layer,
'name': self.input_layer_name
}
monitor = Monitor(
obj=input_layer,
state_vars=('s',),
time=self.T
)
self.monitors[self.input_layer_name] = monitor
self.network.add_monitor(monitor=monitor, name=self.input_layer_name)
def add_layer(self, n: int, name='', node: Nodes = LIF,
w=W_NORMAL_DIST, rule=SIMPLE_STDP,
wmax: float = 1, wmin: float = -1, norm: float = 78.4,
**kwargs):
"""
Add a full connection layer that consists LIF neuron.
:param n:
:param name:
:param node:
:param w:
:param rule:
:param wmax:
:param wmin:
:param norm:
:param kwargs: nu (learning rate of STDP), mu, sigma, w_max and w_min are available
:return:
"""
layer = node(n=n,
traces=True,
rest=self.rest_voltage,
restet=self.reset_voltage,
thresh=self.threshold,
refrac=self.refractory_period,
tc_decay=kwargs.get('tc_decay', 100.0),
theta_plus=kwargs.get('theta_plus', 0.05),
tc_theta_decay=kwargs.get('tc_theta_decay', 1e7),
**kwargs
)
if name == '' or name is None:
name = 'fc-' + str(self.layer_index)
self.layer_index += 1
if type(w) is int:
if w is self.W_NORMAL_DIST:
mu = 0.3 if 'mu' not in kwargs else kwargs['mu']
sigma = 0.3 if 'sigma' not in kwargs else kwargs['sigma']
w = self.weight_norm(self.pre['layer'].n, layer.n,
mu=mu, sigma=sigma)
if w is self.W_RANDOM:
w_max = 0.5 if 'sw_max' not in kwargs else kwargs['sw_max']
w_min = -0.5 if 'sw_min' not in kwargs else kwargs['sw_min']
w = self.weight_rand(self.pre['layer'].n, layer.n,
w_max=w_max, w_min=w_min)
if w is self.W_SIMPLE_RAND:
if 'scale' not in kwargs:
scale = 0.3
else:
scale = kwargs['scale']
w = self.weight_simple_rand(self.pre['layer'].n, layer.n, scale)
self.network.add_layer(layer=layer, name=name)
self.layer_names.append(name)
if 'nu' not in kwargs:
nu = (1e-4, 1e-2)
else:
nu = kwargs['nu']
if rule == self.SIMPLE_STDP:
l_rule = PostPre
elif rule == self.WEIGHT_DEPENDENT_STDP:
l_rule = WeightDependentPostPre
elif rule == self.NO_STDP:
l_rule = NoOp
else:
l_rule = NoOp
connection = Connection(
source=self.pre['layer'],
target=layer,
w=w,
wmax=wmax,
wmin=wmin,
update_rule=l_rule,
nu=nu,
norm=norm
)
self.network.add_connection(connection,
source=self.pre['name'],
target=name,)
monitor = Monitor(
obj=layer,
state_vars=('s', 'v'),
time=self.T
)
self.monitors[name] = monitor
self.network.add_monitor(monitor=monitor, name=name)
self.pre['layer'] = layer
self.pre['name'] = name
print('-- Added', name, 'with the Learning rule,', rule)
def add_inhibit_layer(self, n: int = None, name='', node: Nodes = LIF,
exc_w: float = 22.5, inh_w: float = -100):
"""
Add an inhibitory layer behind the last layer.
If you added this layer, you can add layers more behind a last normal layer, not an inhibitory layer.
:param n:
:param name:
:param node:
:param exc_w:
:param inh_w:
:return:
"""
if n is None:
n = self.pre['layer'].n
layer = node(
n=n,
traces=False,
rest=self.rest_voltage,
restet=self.reset_voltage,
thresh=self.threshold,
refrac=self.refractory_period,
)
if name == '' or name is None:
name = 'inh[' + self.pre['name'] + ']'
self.layer_index += 1
self.network.add_layer(layer=layer, name=name)
n_neurons = self.pre['layer'].n
# 最終層 - 即抑制層の接続
w = exc_w * torch.diag(torch.ones(n_neurons))
last_to_inh_conn = Connection(
source=self.pre['layer'],
target=layer,
w=w,
wmin=0,
wmax=exc_w,
)
# 即抑制層 - 最終層の接続
w = inh_w * (torch.ones(n_neurons, n_neurons) - torch.diag(torch.ones(n_neurons)))
inh_to_last_conn = Connection(
source=layer,
target=self.pre['layer'],
w=w,
wmin=inh_w,
wmax=0,
)
self.network.add_connection(last_to_inh_conn,
source=self.pre['name'],
target=name)
self.network.add_connection(inh_to_last_conn,
source=name,
target=self.pre['name'])
print('-- Added', name, 'as an inhibitory layer')
def add_recurrent_layer_exc_inh(self, exc_n: int, inh_n: int,
name: str = ('rec_exc', 'rec_inh'),
node: Nodes = LIF,
init_exc_w: float = 0.3, init_inh_w: float = -0.5,
exc_nu=(1e-4, 1e-2), inh_nu=(1e-2, 1e-4),
rule: bool = SIMPLE_STDP, **kwargs):
"""
Create a Recurrent connection layer
:param exc_n:
:param inh_n:
:param name:
:param node:
:param init_exc_w:
:param init_inh_w:
:param exc_nu:
:param inh_nu:
:param rule:
:param kwargs:
:return:
"""
# exc-neuron layer
exc_l = node(n=exc_n,
traces=True,
rest=kwargs.get('exc_rest', self.rest_voltage),
restet=kwargs.get('exc_reset', self.reset_voltage),
thresh=kwargs.get('exc_th', self.threshold),
refrac=kwargs.get('exc_ref', self.refractory_period),
tc_decay=kwargs.get('exc_tc_decay', 100.0),
theta_plus=kwargs.get('exc_theta_plus', 0.05),
tc_theta_decay=kwargs.get('exc_tc_theta_decay', 1e7),
**kwargs
)
# inh-neuron layer
inh_l = node(n=inh_n,
traces=True,
rest=kwargs.get('inh_rest', self.rest_voltage),
restet=kwargs.get('inh_reset', self.reset_voltage),
thresh=kwargs.get('inh_th', self.threshold),
refrac=kwargs.get('inh_ref', self.refractory_period),
tc_decay=kwargs.get('inh_tc_decay', 100.0),
theta_plus=kwargs.get('inh_theta_plus', 0.05),
tc_theta_decay=kwargs.get('inh_tc_theta_decay', 1e7),
**kwargs
)
self.network.add_layer(layer=exc_l, name=name[0])
self.network.add_layer(layer=inh_l, name=name[1])
if rule == self.SIMPLE_STDP:
l_rule = PostPre
elif rule == self.WEIGHT_DEPENDENT_STDP:
l_rule = WeightDependentPostPre
elif rule == self.NO_STDP:
l_rule = NoOp
else:
l_rule = NoOp
""" Normal Connection (input to exc & inh)"""
input_exc_connection = Connection(
source=self.pre['layer'], # input
target=exc_l,
w=self.weight_simple_rand(n=self.pre['layer'].n, m=exc_n, scale=kwargs.get('scale', 0.3)),
wmax=kwargs.get('wmax', 1.0),
wmin=kwargs.get('wmin', -1.0),
update_rule=l_rule,
nu=kwargs.get('nu', (1e-4, 1e-2)),
norm=kwargs.get('norm', 78.4)
)
input_inh_connection = Connection(
source=self.pre['layer'], # input
target=inh_l,
w=self.weight_simple_rand(n=self.pre['layer'].n, m=inh_n, scale=kwargs.get('scale', 0.3)),
wmax=kwargs.get('wmax', 1.0),
wmin=kwargs.get('wmin', -1.0),
update_rule=l_rule,
nu=kwargs.get('nu', (1e-4, 1e-2)),
norm=kwargs.get('norm', 78.4)
)
self.network.add_connection(input_exc_connection,
source=self.pre['name'],
target=name[0], )
self.network.add_connection(input_inh_connection,
source=self.pre['name'],
target=name[1], )
""" Reccurent Connecion (exc and inh)"""
exc_inh_connection = Connection(
source=exc_l,
target=inh_l,
w=self.weight_simple_rand(exc_n, inh_n, init_exc_w),
wmax=kwargs.get('exc_wmax', 1.0),
wmin=kwargs.get('exc_wmin', 0),
update_rule=l_rule,
nu=exc_nu,
norm=kwargs.get('exc_norm', exc_n / 10)
)
inh_exc_connection = Connection(
source=inh_l,
target=exc_l,
w=self.weight_simple_rand(inh_n, exc_n, init_inh_w),
wmax=kwargs.get('inh_wmax', 0),
wmin=kwargs.get('inh_wmin', -1.0),
update_rule=l_rule,
nu=inh_nu,
norm=kwargs.get('inh_norm', inh_n / 10)
)
self.network.add_connection(
exc_inh_connection,
source=name[0],
target=name[1]
)
self.network.add_connection(
inh_exc_connection,
source=name[1],
target=name[0]
)
""" Setting monitors """
exc_monitor = Monitor(
obj=exc_l,
state_vars=('s', 'v'),
time=self.T
)
inh_monitor = Monitor(
obj=inh_l,
state_vars=('s', 'v'),
time=self.T
)
self.monitors[name[0]] = exc_monitor
self.monitors[name[1]] = inh_monitor
self.network.add_monitor(monitor=exc_monitor, name=name[0])
self.network.add_monitor(monitor=inh_monitor, name=name[1])
self.pre['layer'] = exc_l
self.pre['name'] = name[0]
print(' -- Added Reccurent layer (exc: {}, inh: {})'.format(exc_n, inh_n))
def load_MNIST(self, batch: int = 1,
encoder=PoissonEncoder,
intensity=intensity,
**kwargs):
"""
Load MNIST dataset from pyTorch.
:param batch:
:param encoder:
:param intensity:
:return:
"""
self.batch = batch
self.train_data_num = 60000
self.test_data_num = 10000
self.label_num = 10
self.train_data = MNIST(encoder(time=self.T, dt=self.dt),
None,
root=self.PROJECT_ROOT+'/data/mnist',
train=True,
download=True,
transform=transforms.Compose(
[transforms.ToTensor(),
transforms.Lambda(lambda x: x*intensity + kwargs.get('min_lim', 0))]
))
self.test_data = MNIST(encoder(time=self.T, dt=self.dt),
None,
root=self.PROJECT_ROOT+'/data/mnist',
train=False,
download=True,
transform=transforms.Compose(
[transforms.ToTensor(),
transforms.Lambda(lambda x: x*intensity + kwargs.get('min_lim', 0))]
))
self.train_loader = DataLoader(self.train_data,
batch_size=batch,
shuffle=True,
pin_memory=self.gpu,
num_workers=self.workers)
self.test_loader = DataLoader(self.test_data,
batch_size=batch,
shuffle=False,
pin_memory=self.gpu,
num_workers=self.workers)
def run(self,
tr_size=None,
unsupervised: bool = True,
alpha: float = 1.0,
interval: int = 250,
debug: bool = False,
**kwargs):
"""
Let the Network run simply.
:param tr_size:
:param unsupervised:
:param alpha:
:param interval:
:param debug:
:return:
"""
if unsupervised:
print('<Running with Unsupervised learning of label assignment>')
if tr_size is None:
tr_size = int(self.train_data_num / self.batch)
else:
tr_size = int(tr_size / self.batch)
n_out_neurons = self.pre['layer'].n
assignments = -torch.ones(n_out_neurons)
spikes = torch.zeros(interval, self.T, n_out_neurons)
labels = []
proportions = torch.zeros(n_out_neurons, self.label_num)
rates = None
progress = tqdm(enumerate(self.train_loader))
start = time()
for i, data in progress:
progress.set_description_str('\rProgress: %d / %d (%.4f seconds)' % (i, tr_size, time() - start))
inputs_img = {'in': data['encoded_image'].view(int(self.T/self.dt), self.batch, 1, 28, 28)}
# assign labels
if unsupervised and i % interval == 0 and i > 0:
t_labels = torch.tensor(labels) # to tensor
# Get the assignments of output neurons
assignments, proportions, rates = assign_labels(
spikes=spikes,
labels=t_labels,
n_labels=self.label_num,
rates=rates,
alpha=alpha
)
labels = []
if self.gpu:
inputs_img = {key: img.cuda() for key, img in inputs_img.items()}
# run!
self.network.run(inputs_img, time=self.T, input_time_dim=1)
if unsupervised:
# labels used by assigning
labels.append(data['label'])
# output spike trains
spikes[i % interval] = self.monitors[self.pre['name']].get('s').squeeze()
self.network.reset_state_variables()
if i >= tr_size: # if reach training size you specified, break for loop
break
print('Progress: %d / %d (%.4f seconds)' % (tr_size, tr_size, time() - start))
# compute train. and test accuracies
if unsupervised:
print('Computing accuracies...')
if debug:
print('\n[Neurons assignments]')
print(assignments)
self.stop_learning()
train_acc, train_pro = self.calc_train_accuracy(assignments=assignments,
proportions=proportions,
tr_size=tr_size)
test_acc, test_pro = self.calc_test_accuracy(assignments=assignments,
proportions=proportions,
ts_size=kwargs.get('ts_size', None))
self.history['train_acc'].append(train_acc)
self.history['train_pro'].append(train_pro)
self.history['test_acc'].append(test_acc)
self.history['test_pro'].append(test_pro)
print('\n*** Train accuracy is %4f ***' % self.history['train_acc'][-1])
print('*** Train accuracy with proportions is %4f ***' % self.history['train_pro'][-1])
print('*** Test accuracy is %4f ***' % self.history['test_acc'][-1])
print('*** Test accuracy with proportions is %4f ***\n' % self.history['test_pro'][-1])
self.start_learning()
print('===< Have finished running the network >===\n')
def calc_test_accuracy(self,
assignments: torch.Tensor,
proportions: torch.Tensor = None,
ts_size: int = None) -> (float, float):
"""
Calculate test accuracy with the assignment.
:param assignments:
:param proportions:
:param ts_size:
:return:
"""
if ts_size is None:
ts_size = self.test_data_num
n_neurons = self.pre['layer'].n
if proportions is None:
proportions = torch.ones(n_neurons, self.label_num).float()
interval = 250
spike_record = torch.zeros(interval, self.T, n_neurons)
labels = []
count_correct = {'acc': 0, 'pro': 0}
progress = tqdm(enumerate(self.test_loader))
print('\n===< Calculate Test accuracy >===')
for i, data in progress:
progress.set_description_str('\rCalculate Test accuracy ... %d / %d ' % (i, ts_size))
inputs_img = {'in': data['encoded_image'].view(int(self.T/self.dt), self.batch, 1, 28, 28)}
if self.gpu:
inputs_img = {key: img.cuda() for key, img in inputs_img.items()}
if i % interval == 0 and i > 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=self.label_num
)
proportion_pred = proportion_weighting(
spikes=spike_record,
assignments=assignments,
proportions=proportions,
n_labels=self.label_num,
)
count_correct['acc'] += torch.sum(label_tensor.long() == all_activity_pred).item()
count_correct['pro'] += torch.sum(label_tensor.long() == proportion_pred).item()
labels = []
# get labels
labels.append(data["label"])
# run!
self.network.run(inputs=inputs_img, time=self.T)
# get output spike trains
spike_record[i % interval] = self.monitors[self.pre['name']].get("s").squeeze()
self.network.reset_state_variables()
if i >= ts_size:
break
print('\r ... done!')
return (float(count_correct['acc']) / float(ts_size), # accuracy
float(count_correct['pro']) / float(ts_size)) # accuracy with proportions
def calc_train_accuracy(self,
assignments: torch.Tensor,
proportions: torch.Tensor = None,
tr_size: int = None) -> (float, float):
"""
Calculate train accuracy with the assignment.
:param assignments:
:param proportions:
:param tr_size:
:return:
"""
if tr_size is None:
tr_size = self.train_data_num
n_neurons = self.pre['layer'].n
if proportions is None:
proportions = torch.ones(n_neurons, self.label_num).float()
interval = 250
spike_record = torch.zeros(interval, self.T, n_neurons)
labels = []
count_correct = {'acc': 0, 'pro': 0}
progress = tqdm(enumerate(self.train_loader))
print('\n===< Calculate Training accuracy >===')
for i, data in progress:
progress.set_description_str('\rCalculate Training accuracy ... %d / %d ' % (i, tr_size))
inputs_img = {'in': data['encoded_image'].view(int(self.T/self.dt), self.batch, 1, 28, 28)}
if self.gpu:
inputs_img = {key: img.cuda() for key, img in inputs_img.items()}
if i % interval == 0 and i > 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=self.label_num
)
proportion_pred = proportion_weighting(
spikes=spike_record,
assignments=assignments,
proportions=proportions,
n_labels=self.label_num,
)
count_correct['acc'] += torch.sum(label_tensor.long() == all_activity_pred).item()
count_correct['pro'] += torch.sum(label_tensor.long() == proportion_pred).item()
labels = []
# get labels
labels.append(data["label"])
# run!
self.network.run(inputs=inputs_img, time=self.T)
# get output spike trains
spike_record[i % interval] = self.monitors[self.pre['name']].get('s').squeeze()
self.network.reset_state_variables()
if i >= tr_size:
break
print('\r ... done!')
return (float(count_correct['acc']) / float(tr_size), # accuracy
float(count_correct['pro']) / float(tr_size)) # accuracy with proportions
def test(self, data_num: int):
"""
Calculate test accuracy with the label assignment used training data.
:param data_num:
:return accuracy:
"""
# Stop learning
for layer in self.network.layers:
self.network.layers[layer].train(False)
# the assignments of output neurons
assignment = torch.zeros(self.label_num, self.pre['layer'].n)
print('===< Calculate train spikes and assign labels >===')
progress = tqdm(enumerate(self.train_loader))
for i, data in progress:
progress.set_description_str('\rAssign labels... %d / %d ' % (i, data_num))
inputs_img = {'in': data['encoded_image'].view(int(self.T/self.dt), self.batch, 1, 28, 28)}
if self.gpu:
inputs_img = {key: img.cuda() for key, img in inputs_img.items()}
# run!
self.network.run(inputs=inputs_img, time=self.T)
# output spike trains
spikes: torch.Tensor = self.monitors[self.pre['name']].get('s')
# sum of the number of spikes
sum_spikes = spikes.sum(0)
max_n_fire = sum_spikes.argmax(1)
labels = data['label']
for j, l in enumerate(labels):
assignment[l][max_n_fire[j]] += 1
self.network.reset_state_variables()
if i >= data_num:
break
# this result is assignment of output neurons
assignment = assignment.argmax(0)
# Calculate accuracy
labels_rate = torch.zeros(self.label_num).float() # each firing rate of labels
count_correct = 0
progress = tqdm(enumerate(self.test_loader))
print('\n===< Calculate Test accuracy >===')
for i, data in progress:
progress.set_description_str('\rCalculate Test accuracy ... %d / %d ' % (i, self.test_data_num))
inputs_img = {'in': data['encoded_image'].view(int(self.T/self.dt), self.batch, 1, 28, 28)}
if self.gpu:
inputs_img = {key: img.cuda() for key, img in inputs_img.items()}
# run!
self.network.run(inputs=inputs_img, time=self.T)
# output spike trains
spikes: torch.Tensor = self.monitors[self.pre['name']].get('s')
# sum of the number of spikes
sum_spikes = spikes.sum(0)
self.network.reset_state_variables()
for b in range(self.batch):
for l in range(self.label_num):
if l in assignment:
indices = torch.tensor([i for i, a in enumerate(assignment) if a == l])
count_assign = torch.sum(assignment == l)
labels_rate[l] += torch.sum(sum_spikes[b][indices]).float() / count_assign.float()
# if actual prediction equals desired label, increment the count.
if labels_rate.argmax() == data['label']:
count_correct += 1
# initialize zeros
labels_rate[:] = 0
acc = float(count_correct) / float(self.test_data_num)
self.history['test_acc'].append(acc)
print('\n*** Test accuracy is %4f ***\n' % acc)
# make learning rates be back
for layer in self.network.layers:
self.network.layers[layer].train(True)
return acc
def plot_out_voltage(self, index: int, save: bool = False,
file_name: str = 'out_voltage.png', dpi: int = DPI):
"""
Plot a membrane potential of 'index'th neuron in the final layer.
:param index:
:param save:
:param file_name:
:param dpi:
:return:
"""
os.makedirs(self.IMAGE_DIR, exist_ok=True)
voltage = self.monitors[self.pre['name']].get('v').numpy().reshape(self.T, self.pre['layer'].n).T[index]
plt.title('Membrane Voltage at neuron[{0}] in final layer'.format(index))
plt.plot(voltage)
plt.xlabel('time [ms]')
plt.ylabel('voltage [mV]')
plt.ylim(self.reset_voltage-5, self.threshold+5)
if not save:
plt.show()
else:
plt.savefig(self.IMAGE_DIR+file_name, dpi=dpi)
plt.close()
@staticmethod
def plot_spikes_scatter(spikes, save=False, dpi=DPI, file_name='spike.png'):
plt.ioff()
plot_spikes(spikes)
if not save:
plt.show()
else:
plt.savefig(file_name, dpi=dpi)
def plot_spikes(self, save: bool = False, index: int = 0,
file_name: str = 'spikes.png', dpi: int = DPI):
"""
Plot spike trains of all neurons as a scatter plot.
:param save:
:param index:
:param file_name:
:param dpi:
:return:
"""
self.make_image_dir()
self.stop_learning()
data = self.train_data[index]
label = data['label']
inputs_img = {'in': data['encoded_image'].view(int(self.T/self.dt), self.batch, 1, 28, 28)}
if self.gpu:
inputs_img = {key: img.cuda() for key, img in inputs_img.items()}
self.network.run(inputs=inputs_img, time=self.T)
spikes = {}
for m_name in self.monitors:
spikes[m_name] = self.monitors[m_name].get('s')
plt.ioff()
plot_spikes(spikes)
if not save:
plt.show()
else:
plt.savefig(self.IMAGE_DIR+'label_'+str(label)+file_name, dpi=dpi)
plt.close()
self.start_learning()
def plot_poisson_img(self, image: torch.Tensor, save: bool = False,
file_name='poisson_img.png', dpi: int = DPI):
"""
Plot a poisson image.
:param image:
:param save:
:param file_name:
:param dpi:
:return:
"""
self.make_image_dir()
result_img = np.zeros((28, 28))
for dt_spike_img in image:
result_img += dt_spike_img.numpy().reshape((28, 28))
plt.imshow(result_img, cmap='winter')
plt.colorbar().set_label('# of spikes')
if not save:
plt.show()
else:
plt.savefig(self.IMAGE_DIR + file_name, dpi=dpi)
plt.close()
def plot_output_weight_map(self, index: int, save: bool = False,
file_name: str = 'weight_map.png', dpi: int = DPI,
c_max: float = 1.0, c_min: float = -1.0):
"""
Plot an afferent weight map of the last layer's [index]th neuron.
:param index:
:param save:
:param file_name:
:param dpi:
:param c_max: max of colormap
:param c_min: min of colormap
:return:
"""
self.make_image_dir()
names = self.layer_names
last = len(names) - 1
# Get the connection information of the last layer
weight: torch.Tensor = self.network.connections[(names[last-1], names[last])].w
# to ndarray and trans.
weight = weight.numpy().T if not self.gpu else weight.cpu().numpy().T
# to shape as (n, n)
weight = weight[index].reshape(28, 28)
# Set the center of a colormap zero.
wmax = weight.max() if weight.max() > c_max else c_max
wmin = weight.min() if weight.min() < c_min else c_min
abs_max = abs(wmax) if abs(wmax) > abs(wmin) else abs(wmin)
plt.imshow(weight, cmap='coolwarm', vmax=abs_max, vmin=(-abs_max))
plt.colorbar()
if not save:
plt.show()
else:
plt.savefig(self.IMAGE_DIR + file_name, dpi=dpi)
plt.close()
def plot_weight_maps(self,
f_shape: tuple = (3, 3),
layer: int = None,
file_name: str = 'weight_maps.png',
dpi: int = DPI,
c_max: float = 1.0,
c_min: float = -1.0,
save: bool = True,
**kwargs):
"""
Plot weight maps of output connection with the shape of [f_shape].
:param f_shape:
:param layer: post layer index counted from 0 (default is last layer)
:param file_name:
:param dpi:
:param c_max:
:param c_min:
:param save:
:return:
"""
self.make_image_dir()