If I remove 2 lines h0=torch.zeros.. c0=torch.zeros and batch_first=True my network stops learning.
I thought that a zero initial hidden state is by default in nn.LSTM if you don’t pass in a hidden state .
class ModelLSTMFSM(nn.Module):
    def __init__(self, input_size=MAX_STRING_SIZE, hidden_size=256, num_layers=2, states_size=MAX_STATES_SIZE):
        super(ModelLSTMFSM, self).__init__()
        self.states_size = states_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, self.states_size * self.states_size * 2)
    
    def forward(self, x):
        x = x.reshape(-1, INPUT_SIZE, MAX_STRING_SIZE)
        
        # Set initial hidden and cell states 
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device) 
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        
        # Forward propagate LSTM
        out, _ = self.lstm(x, (h0, c0))  # out: tensor of shape (batch_size, seq_length, hidden_size)
        
        # Decode the hidden state of the last time step
        out = self.fc(out[:, -1, :])
        return out.reshape(-1, self.states_size, self.states_size, 2)