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Make fully_connected_mnist.py work with BindsNET 0.3.1 #9

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Make fully_connected_mnist.py work with BindsNET 0.3.1 #9

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32 changes: 24 additions & 8 deletions experiments/conversion/fully_connected_mnist.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,14 +2,17 @@
import numpy as np
import matplotlib.pyplot as plt

import os
from time import time as t

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms

from bindsnet.datasets import MNIST
from bindsnet.conversion import ann_to_snn
from bindsnet.datasets import MNIST
from bindsnet.encoding import PoissonEncoder
from bindsnet.network.monitors import Monitor
from bindsnet.analysis.plotting import plot_spikes

Expand Down Expand Up @@ -38,8 +41,7 @@ def forward(self, x):
return x


def main(seed=0, n_epochs=5, batch_size=100, time=50, update_interval=50, plot=False):

def main(seed=0, n_epochs=5, batch_size=100, time=50, dt=250, intensity=128, update_interval=50, plot=False):
np.random.seed(seed)

if torch.cuda.is_available():
Expand All @@ -56,8 +58,20 @@ def main(seed=0, n_epochs=5, batch_size=100, time=50, update_interval=50, plot=F
ANN = FullyConnectedNetwork()

# Get the MNIST data.
images, labels = MNIST('../../data/MNIST', download=True).get_train()
images /= images.max() # Standardizing to [0, 1].
mnist = MNIST(
PoissonEncoder(time=time, dt=dt),
None,
root=os.path.join("..", "..", "data", "MNIST"),
download=True,
train=True,
transform=transforms.Compose(
[transforms.ToTensor(), transforms.Lambda(lambda x: x * intensity)]
),
)
images = mnist.train_data
labels = mnist.train_labels

images = images / images.max() # Standardizing to [0, 1].
images = images.view(-1, 784)
labels = labels.long()

Expand Down Expand Up @@ -120,14 +134,14 @@ def main(seed=0, n_epochs=5, batch_size=100, time=50, update_interval=50, plot=F
)
start = t()

SNN.run(inpts={'Input': images[i].repeat(time, 1, 1)}, time=time)
SNN.run(inputs={'Input': images[i].repeat(time, 1, 1)}, time=time)

spikes = {layer: SNN.monitors[layer].get('s') for layer in SNN.monitors}
voltages = {layer: SNN.monitors[layer].get('v') for layer in SNN.monitors}
prediction = torch.softmax(voltages['5'].sum(1), 0).argmax()
prediction = torch.softmax(voltages['5'].squeeze()[-1], dim=0).argmax()
correct.append((prediction == labels[i]).item())

SNN.reset_()
SNN.reset_state_variables()

if plot:
spikes = {k: spikes[k].cpu() for k in spikes}
Expand All @@ -143,6 +157,8 @@ def main(seed=0, n_epochs=5, batch_size=100, time=50, update_interval=50, plot=F
parser.add_argument('--time', type=int, default=50)
parser.add_argument('--update_interval', type=int, default=50)
parser.add_argument('--plot', dest='plot', action='store_true')
parser.add_argument("--dt", type=int, default=1.0)
parser.add_argument("--intensity", type=float, default=128)
parser.set_defaults(plot=False)
args = vars(parser.parse_args())

Expand Down