readout layer trained

verified with time-series forecasting task and plotted

1 files changed, 0 insertions, 107 deletions

diff --git a/neurobench_testing/examples/gsc/train_SNN.py b/neurobench_testing/examples/gsc/train_SNN.py
deleted file mode 100644
index 4c98cbe..0000000
--- a/neurobench_testing/examples/gsc/train_SNN.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import torch
-import numpy as np
-import snntorch.functional as func
-import snntorch.surrogate as surrogate
-import snntorch.utils as utils
-
-from tqdm import tqdm
-from torch.utils.data import DataLoader
-
-from neurobench.datasets import SpeechCommands
-from neurobench.preprocessing import S2SPreProcessor
-from neurobench.postprocessing import choose_max_count
-
-from SNN import net
-
-BATCH_SIZE = 5
-NUM_WORKERS = 8
-EPOCHS = 100
-
-# Check if GPU is available
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(device)
-
-# Load the dataset
-data_dir = "../../../data/speech_commands/"
-train_set = SpeechCommands(path=data_dir, subset="training")
-val_set = SpeechCommands(path=data_dir, subset="validation")
-test_set = SpeechCommands(path=data_dir, subset="testing")
-
-s2s = S2SPreProcessor()
-
-# Create the dataloaders
-train_loader = DataLoader(train_set, batch_size=BATCH_SIZE, shuffle=True)
-val_loader = DataLoader(val_set, batch_size=BATCH_SIZE, shuffle=True)
-test_loader = DataLoader(test_set, batch_size=BATCH_SIZE, shuffle=True)
-
-# Different parameters to Speech2Spikes make different shapes of data
-tmp_dat = torch.stack([train_set[0][0], train_set[1][0]])
-tmp_label = torch.stack([train_set[0][1], train_set[1][1]])
-tmp_out = s2s((tmp_dat, tmp_label))
-num_steps = tmp_out[0].shape[1]
-num_feat = tmp_out[0].shape[2]
-
-# The SNNTorch forward pass
-def forward_pass(net, data, num_steps):
-    spk_rec = []
-    utils.reset(net)
-    for step in range(num_steps):
-        spk_out, _ = net(data[:, step, ...])
-        spk_rec.append(spk_out)
-    return torch.stack(spk_rec)
-
-# Send network to device
-net.to(device)
-optimizer = torch.optim.Adam(net.parameters(), lr=0.001, betas=(0.9, 0.999))
-loss_fn = func.mse_count_loss(correct_rate=0.25, incorrect_rate=0.025)
-
-# Training Start
-best_acc = 0
-for epoch in range(EPOCHS):
-    print(f"Epoch {epoch}:")
-    train_loss = []
-    train_acc = []
-    net.train()
-    for batch in tqdm(iter(train_loader)):
-        events, targets = s2s(batch)
-        events = events.to(device)
-        targets = targets.to(device)
-
-        spk_rec = forward_pass(net, events, num_steps)
-        loss_val = loss_fn(spk_rec, targets)
-
-        train_loss.append(loss_val.item())
-        train_acc.append(func.accuracy_rate(spk_rec, targets))
-
-        optimizer.zero_grad()
-        loss_val.backward()
-        optimizer.step()
-
-    print(f"Train Loss: {np.mean(train_loss):.3f}")
-    print(f"Train Accuracy: {np.mean(train_acc) * 100:.2f}%")
-
-    val_loss = []
-    val_acc = []
-    net.eval()
-    for batch in tqdm(iter(val_loader)):
-        events, targets = s2s(batch)
-        events = events.to(device)
-        targets = targets.to(device)
-
-        spk_rec = forward_pass(net, events, num_steps)
-
-        val_loss.append(loss_fn(spk_rec, targets).item())
-        val_acc.append(func.accuracy_rate(spk_rec, targets))
-
-    print(f"Validation Loss: {np.mean(val_loss):.3f}")
-    print(f"Validation Accuracy: {np.mean(val_acc) * 100:.2f}%")
-
-    if np.mean(val_acc) > best_acc:
-        print("New Best Validation Accuracy. Saving...")
-        best_acc = np.mean(val_acc)
-        torch.save(net.state_dict(), "model_data/s2s_gsc_snntorch")
-
-    print(f"---------------------\n")
-
-# Load the weights into the network for inference
-net.load_state_dict(torch.load("model_data/s2s_gsc_snntorch"))