Hello ,
I can’t figure out this error , it seems to be linked to input dimension to my RNNclassifier.
Thanks.
class RNNClassifier(nn.Module):
def __init__(self, input_size, hidden_size, output_size, n_layers=1):
super(RNNClassifier, self).__init__()
self.hidden_size = hidden_size
self.n_layers = n_layers
self.embedding = nn.Embedding(input_size, hidden_size)
self.gru = nn.GRU(hidden_size, hidden_size, n_layers)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, input):
batch_size = input.size(0)
print(" input", input.size())
embedded = self.embedding(input)
print(" embedding", embedded.size())
hidden = self._init_hidden(batch_size)
output, hidden = self.gru(embedded, hidden)
print(" gru hidden output", hidden.size())
fc_output = self.fc(hidden)
print(" fc output", fc_output.size())
return fc_output
def _init_hidden(self, batch_size):
hidden = torch.zeros(self.n_layers, batch_size, self.hidden_size)
return Variable(hidden)
X_train, Y_train = Data('s2-gap-12dates.csv', train = True)
X=X_train[0,:,:]
X=torch.from_numpy(X)
X=X.view(-1,12,20)
inp = Variable(X)
out = classifier(inp)
