readout layer trained

verified with time-series forecasting task and plotted

1 files changed, 0 insertions, 90 deletions

diff --git a/neurobench_testing/neurobench_testing.py b/neurobench_testing/neurobench_testing.py
deleted file mode 100644
index 338562d..0000000
--- a/neurobench_testing/neurobench_testing.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import torch
-from torch.utils.data import DataLoader
-
-from neurobench.datasets import SpeechCommands
-from neurobench.processors.preprocessors import S2SPreProcessor
-from neurobench.processors.postprocessors import ChooseMaxCount
-from neurobench.models import NeuroBenchModel
-
-from neurobench.models import SNNTorchModel
-
-from neurobench.metrics.workload import (
-        ActivationSparsity,
-        SynapticOperations,
-        ClassificationAccuracy,
-)
-
-from neurobench.metrics.static import (
-        Footprint,
-        ConnectionSparsity,
-)
-
-from neurobench.benchmarks import Benchmark
-
-from torch import nn
-import snntorch as snn 
-from snntorch import surrogate 
-
-beta = 0.5 #this does nothing with the current model
-device = torch.device("cpu")
-spike_grad = surrogate.fast_sigmoid()
-net = nn.Sequential(
-    nn.Flatten(),
-    nn.Linear(20, 256),
-    snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True),
-    nn.Linear(256, 256),
-    snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True),
-    nn.Linear(256, 256),
-    snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True),
-    nn.Linear(256, 35),
-    snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True, output=True),
-)
-
-test_set = SpeechCommands(path="data/SpeechCommands/", subset="testing")
-test_set_loader = DataLoader(test_set, batch_size=500, shuffle=True)
-
-net.load_state_dict(torch.load("examples/gsc/model_data/s2s_gsc_snntorch", map_location=device))
-print("weight before noise: ", net[1].weight[0][0].item())
-
-#adds noise for 'simulating' memristors
-# not perfect ik but just testing it out
-noise = 0.05
-with torch.no_grad():
-    for param in net.parameters():
-        param.add_(torch.randn_like(param) * noise)
-
-print("weight after noise: ", net[1].weight[0][0].item())
-
-model = SNNTorchModel(net)
-
-preprocessors = [S2SPreProcessor(device=device)]
-postprocessors = [ChooseMaxCount()]
-
-static_metrics = [Footprint, ConnectionSparsity]
-workload_metrics = [ClassificationAccuracy, ActivationSparsity, SynapticOperations]
-
-print("Layer 1 Beta:", net[2].beta)
-print("Layer 2 Beta:", net[4].beta)
-
-benchmark = Benchmark(
-    model, 
-    test_set_loader, 
-    preprocessors, 
-    postprocessors, 
-    [static_metrics, workload_metrics]
-)
-results = benchmark.run()
-print(results)
-
-# Energy calulations
-ENERGY_PER_MAC = 0.9e-12
-ENERGY_PER_AC = 0.1e-12
-
-macs = results['SynapticOperations']['Effective_MACs']
-acs = results['SynapticOperations']['Effective_ACs']
-
-total_energy = (macs * ENERGY_PER_MAC) + (acs * ENERGY_PER_AC)
-
-print("Energy Report:")
-print(f"Total Operations: {macs} MACS, {acs} acs ")
-print(f"Calculated Energy cost: {total_energy} joules per batch")