TypeError : __init__() takes 1 positional argument but 2 were given

nlp
#1

I apologise if this is silly. But I’m unable to understand this error message :

<ipython-input-164-b29eb2d49cef> in <module>()
      2 
      3 for i in range(num_iters):
----> 4     l = train()
      5     print(l)

<ipython-input-163-818cc46545d8> in train()
      10     for u in range(num_unrollings):
      11         one_hot_input = Variable(torch.from_numpy(batches[u].T).type(dtype), requires_grad=False)
 ---> 12         cell, hidden, output_prior = lstm(one_hot_input, cell, hidden)
      13         loss_function = nn.CrossEntropyLoss()
      14         logits = output_prior.view(output_prior.shape[1], -1).data

/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
      323         for hook in self._forward_pre_hooks.values():
      324             hook(self, input)
  --> 325         result = self.forward(*input, **kwargs)
      326         for hook in self._forward_hooks.values():
      327             hook_result = hook(self, input, result)

<ipython-input-162-7f7723fe4c71> in forward(self, one_hot_input, cell, hidden)
      29 
      30     def forward(self, one_hot_input, cell, hidden):
 ---> 31         input_gate = nn.Sigmoid(torch.mm(self.Wi,one_hot_input) + torch.mm(self.Ui,hidden) + self.bi)
      32         forget_gate = nn.Sigmoid(torch.mm(self.Wf,one_hot_input) + torch.mm(self.Uf,hidden) + self.bf)
      33         output_gate = nn.Sigmoid(torch.mm(self.Wo,one_hot_input) + torch.mm(self.Uo,hidden) + self.bo)

TypeError: __init__() takes 1 positional argument but 2 were given

It’s from a simple character based LSTM and I’m simply trying to print the loss at each step for now

dtype = torch.FloatTensor
hidden_size = 64

class LSTMn(nn.Module):

    def __init__(self, hidden_size, vocab_size):
        super(LSTMn, self).__init__()
        self.Wi = Parameter(torch.rand(hidden_size, vocab_size).type(dtype), requires_grad=True)
        self.Ui = Parameter(torch.rand(hidden_size, hidden_size).type(dtype), requires_grad=True)
        self.bi = Parameter(torch.rand(hidden_size, 1).type(dtype), requires_grad=True)
    
        self.Wf = Parameter(torch.rand(hidden_size, vocab_size).type(dtype), requires_grad=True)
        self.Uf = Parameter(torch.rand(hidden_size, hidden_size).type(dtype), requires_grad=True)
        self.bf = Parameter(torch.rand(hidden_size, 1).type(dtype), requires_grad=True)
    
        self.Wo = Parameter(torch.rand(hidden_size, vocab_size).type(dtype), requires_grad=True)
        self.Uo = Parameter(torch.rand(hidden_size, hidden_size).type(dtype), requires_grad=True)
        self.bo = Parameter(torch.rand(hidden_size, 1).type(dtype), requires_grad=True)
    
        self.Wc = Parameter(torch.rand(hidden_size, vocab_size).type(dtype), requires_grad=True)
        self.Uc = Parameter(torch.rand(hidden_size, hidden_size).type(dtype), requires_grad=True)
        self.bc = Parameter(torch.rand(hidden_size, 1).type(dtype), requires_grad=True)
    
        self.W_soft = Parameter(torch.rand(vocab_size, hidden_size).type(dtype), requires_grad=True)
        self.b_soft = Parameter(torch.rand(vocab_size, 1).type(dtype), requires_grad=True)
    
        self.hidden_state = Variable(torch.zeros(hidden_size, batch_size).type(dtype), requires_grad=False)
        self.cell_state = Variable(torch.zeros(hidden_size, batch_size).type(dtype), requires_grad=False)
    
    def forward(self, one_hot_input, cell, hidden):
        input_gate = nn.Sigmoid(torch.mm(self.Wi,one_hot_input) + torch.mm(self.Ui,hidden) + self.bi)
        forget_gate = nn.Sigmoid(torch.mm(self.Wf,one_hot_input) + torch.mm(self.Uf,hidden) + self.bf)
        output_gate = nn.Sigmoid(torch.mm(self.Wo,one_hot_input) + torch.mm(self.Uo,hidden) + self.bo)
        update = nn.Tanh(torch.mm(self.Wc,one_hot_input) + torch.mm(self.Uc,hidden) + self.bc)
        cell = (forget_gate * cell) + (input_gate * update)
        hidden = output_gate * nn.Tanh(cell)
        output_prior = torch.mm(self.W_soft,hidden) + self.b_soft
        return cell, hidden, output_prior

    def save_state(self, hidden_state, cell_state):
        self.hidden_state = hidden_state
        self.cell_state = cell_state
    
    def load_state(self):
        return self.hidden_state, self.cell_state

lstm = LSTMn(hidden_size, vocab_size)

learning_rate = 0.0001
train_batches = BatchGenerator(train_text, batch_size, num_unrollings)
optimizer = torch.optim.SGD(lstm.parameters(), lr=learning_rate)

def train():
    hidden, cell = lstm.load_state()
    batches = train_batches._next()
    optimizer.zero_grad()
    loss = 0
    for u in range(num_unrollings):
        one_hot_input = Variable(torch.from_numpy(batches[u].T).type(dtype), requires_grad=False)
        cell, hidden, output_prior = lstm(one_hot_input, cell, hidden)
        loss_function = nn.CrossEntropyLoss()
        logits = output_prior.view(output_prior.shape[1], -1).data
        labels = torch.from_numpy(np.argmax(batches[u+1], axis=1))
        loss += loss_function(logits, labels)
    lstm.save_state(hidden, cell)
    loss.backward()
    optimizer.step()
    return loss

num_iters = 1000

for i in range(num_iters):
    l = train()
    print(l)

What init() could the error be referring to? The init() for the class seems alright to me.

#2

Turns out it was silly. The functions are to be implemented like this :

m = nn.Sigmoid()
input = autograd.Variable(torch.randn(2))
print(input)
print(m(input))
Sorry if anyone wasted time on this

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