Building a RNN/LSTM/GRU I am trying to overfit it on random data and I can’t get it to learn (accuracy won’t increase past 0.2). It keeps getting stuck at local optima. Idea of using random data comes fromUNDERSTANDING DEEP LEARNING REQUIRES RETHINKING GENERALIZATION . Any advice on what to change.
Input is (100 * 4 * 60) output is (100  * 10 ).
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
#         self.lstm1 = nn.LSTM(input_size = 60, hidden_size = 32, num_layers = 1, bidirectional= True)
#        self.lstm1 = nn.RNN(input_size = 60, hidden_size = 32, num_layers = 1, bidirectional= True)
        self.lstm1 = nn.GRU(input_size = 60, hidden_size = 32, bidirectional= True)
        self.fcn1 = nn.Linear(4 * 64, 512)
        self.fcn2 = nn.Linear(512, 512)
        self.fcn3 = nn.Linear(512, 10)
        
        self.softmax = nn.Softmax(dim=1)

    def forward(self, x):
        x, _ = self.lstm1(x)
#         print(x.shape)
        x = x.view(100, 4 * 64)
#         print(x.shape)
        x = self.fcn1(x)
        x = F.dropout(x, p = 0.5)
        x = self.fcn2(x)
        x = F.dropout(x, p = 0.5)
        x = self.fcn3(x)
        x = F.dropout(x, p = 0.9)

        x =x.squeeze(1)
  
        
        x = self.softmax(x)
        
        return x

criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.0001)


I’ve tried different architectures, layers , drop-out, learning rates, learning rules, etc. Perhaps I should change the Loss function?
Training Code
for epoch in range(5000):
    
    optimizer.zero_grad()
    
    running_loss = 0.0
    outputs = net(X)
    loss = criterion(outputs, y)
    loss.backward()
    optimizer.step()
    running_loss += loss.item()
    
  
    
    if epoch % 100 == 0: 
        acc = accuracy_score(np.array(outputs.detach()).argmax(1), y)
#         print(np.array(outputs.detach()).argmax(1))
        print("Epoch: ", epoch, "Loss: ",running_loss, "Accuracy:", acc)