model.py

import torch
import torch.nn as nn

class loan_classify(nn.Module) :
    def __init__(self):
        super(loan_classify, self).__init__()
        self.fc1 = nn.Linear(9, 128)
        self.batch_norm1 = nn.BatchNorm1d(128)
        self.relu1 = nn.ReLU()
        
        self.dropout1 = nn.Dropout(0.5)
        
        self.fc2 = nn.Linear(128, 64)
        self.batch_norm2 = nn.BatchNorm1d(64)
        self.relu2 = nn.ReLU()

        self.dropout2 = nn.Dropout(0.1)
        
        self.fc3 = nn.Linear(64, 32)
        self.batch_norm3 = nn.BatchNorm1d(32)
        self.relu3 = nn.ReLU()
        
        self.fc4 = nn.Linear(32, 1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        out = self.fc1(x)
        out = self.batch_norm1(out)
        out = self.relu1(out)
        out = self.dropout1(out)
        
        out = self.fc2(out)
        out = self.batch_norm2(out)
        out = self.relu2(out)
        out = self.dropout2(out)
        
        out = self.fc3(out)
        out = self.batch_norm3(out)
        out = self.relu3(out)
        
        out = self.fc4(out)
        out = self.sigmoid(out)
        return out