models/model.py

import torch
import torch.nn as nn
class TxnAnomalyGRU(nn.Module):
    def __init__(self, input_dim=32, hidden_dim=128, num_layers=2, dropout=0.3):
        super().__init__()
        self.gru = nn.GRU(input_dim, hidden_dim, num_layers=num_layers, bidirectional=True, batch_first=True, dropout=dropout)
        self.batchnorm = nn.BatchNorm1d(hidden_dim * 2)
        self.dropout = nn.Dropout(dropout)
        self.fc1 = nn.Linear(hidden_dim * 2, 64)
        self.relu = nn.ReLU()
        self.out = nn.Linear(64, 1)
        self.sigmoid = nn.Sigmoid()
    def forward(self, x):
        h0 = torch.zeros(self.gru.num_layers * 2, x.size(0), self.gru.hidden_size).to(x.device)
        out, _ = self.gru(x, h0)
        out = out[:, -1, :]
        out = self.batchnorm(out)
        out = self.dropout(out)
        out = self.relu(self.fc1(out))
        out = self.sigmoid(self.out(out))
        return out