I am training two simple NN one is a simple RNN network other is a LSTM based network, But after computing the gradients and plotting them, i am getting almost zero gradient flow in case of LSTM

almost similar to rnn

These are the simple models that i have used

```
class RNNModel(nn.Module):
def __init__(self, input_dim, hidden_dim, layer_dim, output_dim, num_layers=1):
super(RNNModel, self).__init__()
self.hidden_dim = hidden_dim
self.layer_dim = layer_dim
self.rnn = nn.RNN(input_size=input_dim, hidden_size=hidden_dim, num_layers=num_layers, batch_first=True)
self.linear = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
x, _ = self.rnn(x)
x = self.linear(x)
return x
```

```
class LSTMModel(nn.Module):
def __init__(self, input_dim, hidden_dim, layer_dim, output_dim, num_layers=1):
super(LSTMModel, self).__init__()
self.hidden_dim = hidden_dim
self.layer_dim = layer_dim
self.lstm = nn.LSTM(input_size=input_dim, hidden_size=hidden_dim, num_layers=num_layers, batch_first=True)
self.linear = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
x, _ = self.lstm(x)
x = self.linear(x)
return x
```

Can anyone tell me why the gradient is so bad in case of lstm which i think shold be better than rnn