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Merged
Hananel-Hazan merged 2 commits into from
Apr 29, 2021
Merged

fixes accuracies #482

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46f1f4f
fixes accuracies
Apr 29, 2021
a168592
black formatting
Hananel-Hazan Apr 29, 2021
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2 changes: 1 addition & 1 deletion bindsnet/network/topology.py
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Original file line number Diff line number Diff line change
Expand Up @@ -191,7 +191,7 @@ def compute(self, s: torch.Tensor) -> torch.Tensor:

def compute_window(self, s: torch.Tensor) -> torch.Tensor:
# language=rst
""""""
""" """

if self.s_w == None:
# Construct a matrix of shape batch size * window size * dimension of layer
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2 changes: 1 addition & 1 deletion examples/mnist/SOM_LM-SNNs.py
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Expand Up @@ -341,7 +341,7 @@

pbar = tqdm(total=n_test)
for step, batch in enumerate(test_dataset):
if step > n_test:
if step >= n_test:
break
# Get next input sample.
inputs = {"X": batch["encoded_image"].view(int(time / dt), 1, 1, 28, 28)}
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14 changes: 4 additions & 10 deletions examples/mnist/eth_mnist.py
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Expand Up @@ -14,11 +14,7 @@
from bindsnet.models import DiehlAndCook2015
from bindsnet.network.monitors import Monitor
from bindsnet.utils import get_square_weights, get_square_assignments
from bindsnet.evaluation import (
all_activity,
proportion_weighting,
assign_labels,
)
from bindsnet.evaluation import all_activity, proportion_weighting, assign_labels
from bindsnet.analysis.plotting import (
plot_input,
plot_spikes,
Expand Down Expand Up @@ -168,8 +164,8 @@
# Train the network.
print("\nBegin training.\n")
start = t()
labels = []
for epoch in range(n_epochs):
labels = []

if epoch % progress_interval == 0:
print("Progress: %d / %d (%.4f seconds)" % (epoch, n_epochs, t() - start))
Expand All @@ -194,9 +190,7 @@

# Get network predictions.
all_activity_pred = all_activity(
spikes=spike_record,
assignments=assignments,
n_labels=n_classes,
spikes=spike_record, assignments=assignments, n_labels=n_classes
)
proportion_pred = proportion_weighting(
spikes=spike_record,
Expand Down Expand Up @@ -312,7 +306,7 @@

pbar = tqdm(total=n_test)
for step, batch in enumerate(test_dataset):
if step > n_test:
if step >= n_test:
break
# Get next input sample.
inputs = {"X": batch["encoded_image"].view(int(time / dt), 1, 1, 28, 28)}
Expand Down