def trainNTM(): t = Tasks() x_train, y_train = t.sequence_type_1(2000) ntm = NTM(10, 20) ntm.train(x_train, y_train, 1, maxEpoch=25, learning_rate=0.0006)
def trainNTM(): ntm = NTM(10, 14) X, y = [], [] for i in range(10): tempX, tempY = getData("data/observations_"+str(i*500)+".npy", "data/actions_"+str(i*500)+".npy") X.extend(tempX) y.extend(tempY) print(len(X), len(y)) ntm.train(X, y, 1)
def trainNTM(): ntm = NTM(10, 14) X, y = getData() ntm.train(X, y, 1)
bit = 1 else: sampleIdx = sampleIdxGreen bit = 0 start = random.sample(sampleIdx, 1)[0] sampleIdx.remove(start) print(start) imageSequence = np.load("sequences/image/imageSequence_" + str(start) + ".npy") robotGpsSequence = np.load("sequences/robot/robotGpsSequence_" + str(start) + ".npy") actionSequence = np.load("sequences/action/actionSequence_" + str(start) + ".npy") imageSequence = torch.from_numpy(imageSequence).float() robotGpsSequence = torch.from_numpy(robotGpsSequence).float() y = torch.from_numpy(actionSequence).float() loss = ntm.train(imageSequence, y, robotGpsSequence, learning_rate) losses.append(loss.detach().numpy()) print(i, inEpochCtr, loss) ctr += 1 torch.save(ntm.state_dict(), "ntm.pt") np.save("losses/ntm", np.array(losses))