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import torch
from torch import nn
class ECGClassifier(nn.Module):
"""
Simple 1D CNN for ECG classification.
"""
def __init__(self, num_classes: int = 2):
super().__init__()
self.features = nn.Sequential(
nn.Conv1d(1, 16, kernel_size=5, padding=2),
nn.BatchNorm1d(16),
nn.ReLU(inplace=True),
nn.MaxPool1d(kernel_size=2),
nn.Conv1d(16, 32, kernel_size=3, padding=1),
nn.BatchNorm1d(32),
nn.ReLU(inplace=True),
nn.AdaptiveAvgPool1d(1),
)
self.classifier = nn.Sequential(
nn.Flatten(),
nn.Linear(32, num_classes),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
# x shape: (batch, channels=1, length)
feats = self.features(x)
logits = self.classifier(feats)
return logits
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