I have made the changes by passing the final layer with/without relu:
class CNNNet(nn.Module):
    #def __init__(self, input_size, hidden_size, num_layers, d_out):
    def __init__(self, voc_size, emb_dim, d_out):
        super(CNNNet,self).__init__()
        
        self.voc_size = voc_size
        self.emb_dim = emb_dim
        self.d_out = d_out
        
        self.embedding = nn.Embedding(self.voc_size, self.emb_dim)
        self.cnn1 = nn.Conv1d(in_channels=35, out_channels=128, kernel_size=11)
        self.maxpool1 = nn.MaxPool1d(kernel_size=5, stride=1)

        self.fc1 = nn.Linear(4352, 2048)
        self.fc2 = nn.Linear(2048, 1024)
        self.fc3 = nn.Linear(1024, 256)
        self.fc4 = nn.Linear(256, 64)
        self.fc5 = nn.Linear(64, 1)
        self.relu = nn.LeakyReLU()
        self.sigmoid = nn.Sigmoid()
        self.dropout = nn.Dropout(self.d_out)

    def forward(self, x):
        embeds = self.embedding(x)
        embeds = embeds.view(embeds.shape[0], embeds.shape[2], embeds.shape[1])     
        cnn_layer1 = self.relu(self.cnn1(embeds))
        mpool1 = self.maxpool1(cnn_layer1)
        x = mpool1.view(mpool1.size(0), -1)
        out = self.relu(self.dropout(self.fc1(x)))
        out = self.relu(self.dropout(self.fc2(out)))
        out = self.relu(self.dropout(self.fc3(out)))
        out = self.relu(self.dropout(self.fc4(out)))
        out = self.relu(self.dropout(self.fc5(out)))
        return (out)

I replaced nn.BCELoss woth nn.BCEWithLogitsLoss but I still keep getting the same training accuracy and validation accuracy. The loss for both training and validation is increasing. Don’t know if there is something wrong with my final layer.
Epoch:  0  /Loss is:  22.967  /Acc:  0.5  /Val_Loss:  0.305 / Val Acc:  0.5
 Epoch:  1  /Loss is:  26935.934  /Acc:  0.5  /Val_Loss:  55.215 / Val Acc:  0.499
 Epoch:  2  /Loss is:  1912778.449  /Acc:  0.501  /Val_Loss:  6714.289 / Val Acc:  0.499
 Epoch:  3  /Loss is:  63861942.892  /Acc:  0.5  /Val_Loss:  160974.908 / Val Acc:  0.497
 Epoch:  4  /Loss is:  36171148.704  /Acc:  0.5  /Val_Loss:  29366.505 / Val Acc:  0.5
 Epoch:  5  /Loss is:  8249850.308  /Acc:  0.501  /Val_Loss:  67750.454 / Val Acc:  0.501
 Epoch:  6  /Loss is:  54260504.278  /Acc:  0.5  /Val_Loss:  169734.745 / Val Acc:  0.501
 Epoch:  7  /Loss is:  30756635.652  /Acc:  0.5  /Val_Loss:  31760.747 / Val Acc:  0.502
 Epoch:  8  /Loss is:  36212964.045  /Acc:  0.5  /Val_Loss:  383223.904 / Val Acc:  0.502
 Epoch:  9  /Loss is:  102597196.442  /Acc:  0.5  /Val_Loss:  710555.625 / Val Acc:  0.5
 Epoch:  10  /Loss is:  312780892.063  /Acc:  0.5  /Val_Loss:  1806529.0 / Val Acc:  0.499
 Epoch:  11  /Loss is:  194196309.733  /Acc:  0.501  /Val_Loss:  371710.664 / Val Acc:  0.5
 Epoch:  12  /Loss is:  79394308.874  /Acc:  0.5  /Val_Loss:  306873.801 / Val Acc:  0.5