Include tanh in RNN network

Im new to RNNs (pretty new to ANN in general and im trying to train a network to predict stock market direction next day as a school project (impossible I know :P)

So far I have a network that trains but only predicts 0’s (1 for upward movement and 0 for down)
I want to add a tanh layer, but I get an error message for shape mismatch. can anyone help?

class SimpleRNN(nn.Module):
def init(self, hidden_size):
super(SimpleRNN, self).init()
self.hidden_size = hidden_size

    self.inp = nn.Linear(21, hidden_size)
    self.rnn = nn.LSTM(hidden_size, hidden_size, 2, dropout=0.05, batch_first=True)
    self.act = nn.Tanh()
    self.out = nn.Linear(hidden_size, 1)
    self.softmax = nn.LogSoftmax(dim=1)

def step(self, input, hidden=None):
    input = self.inp(input.view(1, -1)).unsqueeze(1)
    output, hidden = self.rnn(input, hidden)
    output = self.act(self.out(output[0]))
    output = self.out(output.squeeze(1))
    output = self.softmax(output)
    return output, hidden

def forward(self, inputs, hidden=None, force=True, steps=0):
    if force or steps == 0: steps = len(inputs)
    outputs = Variable(torch.zeros(steps, 1, 1))
    for i in range(steps):
        if force or i == 0:
            input = inputs[i]
            input = output
        output, hidden = self.step(input, hidden)
        outputs[i] = output
    return outputs, hidden

You can try removing the layer, and in the forward method call torch.tanh on the tensor you want.