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change some code for the new version of bindsnet. #8

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change some code for the new version of bindsnet. #8

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24 changes: 14 additions & 10 deletions experiments/mnist/increasing_inhibition.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,14 +4,15 @@
import numpy as np
import matplotlib.pyplot as plt

import torchvision.datasets.mnist as vision_mnist
from time import time as t
from sklearn.metrics import confusion_matrix
from scipy.spatial.distance import euclidean

from bindsnet.datasets import MNIST
from bindsnet.network import Network
from bindsnet.encoding import poisson
from bindsnet.network import load_network
from bindsnet.network import load
from bindsnet.learning import PostPre, NoOp
from bindsnet.network.monitors import Monitor
from bindsnet.network.topology import Connection
Expand Down Expand Up @@ -96,23 +97,25 @@ def main(seed=0, n_neurons=100, n_train=60000, n_test=10000, c_low=1, c_high=25,
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 = 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(data_path, download=True)
dataset = vision_mnist.MNIST(root=data_path, download=True)

if train:
images, labels = dataset.get_train()
images = dataset.train_data
labels = dataset.train_labels
else:
images, labels = dataset.get_test()
images = dataset.test_data
labels = dataset.test_labels

images = images.view(-1, 784)
images *= intensity
images = images * intensity

# Record spikes during the simulation.
spike_record = torch.zeros(update_interval, int(time / dt), n_neurons)
Expand Down Expand Up @@ -232,7 +235,7 @@ def main(seed=0, n_neurons=100, n_train=60000, n_test=10000, c_low=1, c_high=25,
inpts = {'X' : sample}

# Run the network on the input.
network.run(inpts=inpts, time=time)
network.run(inputs=inpts, time=time)

retries = 0
while spikes['Y'].get('s').sum() < 5 and retries < 3:
Expand All @@ -243,7 +246,7 @@ def main(seed=0, n_neurons=100, n_train=60000, n_test=10000, c_low=1, c_high=25,
network.run(inpts=inpts, time=time)

# Add to spikes recording.
spike_record[i % update_interval] = spikes['Y'].get('s').t()
spike_record[i % update_interval] = spikes['Y'].get('s').squeeze(1)

# Optionally plot various simulation information.
if plot:
Expand All @@ -261,7 +264,7 @@ def main(seed=0, n_neurons=100, n_train=60000, n_test=10000, c_low=1, c_high=25,

plt.pause(1e-8)

network.reset_() # Reset state variables.
network.reset_state_variables() # Reset state variables.

print(f'Progress: {n_examples} / {n_examples} ({t() - start:.4f} seconds)')

Expand Down Expand Up @@ -354,7 +357,7 @@ def main(seed=0, n_neurons=100, n_train=60000, n_test=10000, c_low=1, c_high=25,
# Parameters.
parser = argparse.ArgumentParser()
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--n_neurons', type=int, default=100)
parser.add_argument('--n_neurons', type=int, default=625)
parser.add_argument('--n_train', type=int, default=60000)
parser.add_argument('--n_test', type=int, default=10000)
parser.add_argument('--c_low', type=float, default=1)
Expand Down Expand Up @@ -389,6 +392,7 @@ def main(seed=0, n_neurons=100, n_train=60000, n_test=10000, c_low=1, c_high=25,
progress_interval = args.progress_interval
update_interval = args.update_interval
plot = args.plot
plot = True
train = args.train
gpu = args.gpu

Expand Down