def test():
obstacle, wall_cw, wall_awc = Obstacle(), WallCW(), WallACW()
obstacle_params, wall_cw_params, wall_acw_params = torch.load(
'program_memory/move.pt'), torch.load(
'program_memory/cw.pt'), torch.load('program_memory/acw.pt')
networks = [obstacle, wall_cw, wall_awc]
params = [obstacle_params, wall_cw_params, wall_acw_params]
hnm = HNM(10, 14, networks, params)
hnm.load_state_dict(torch.load('learned_params/hnm.pt'))
testX, testY = getTestData()
s = torch.from_numpy(np.array(testX[108:109][0])).float().unsqueeze(0)
l = np.array(testY[108:109][0])
print(s.size())
# print(l.size())
(read_weights, write_weights) = hnm._initialise()
plt.matshow(hnm.Memory.detach().numpy())
plt.show()
correct = 0
for i in range(s.size()[1]):
(out, read_weights,
write_weights) = hnm.forward(s[:, i, :], read_weights, write_weights)
values = out.detach().numpy()
if np.argmax(values) == np.argmax(l[i]): correct += 1
plt.matshow(hnm.Memory.detach().numpy())
plt.show()
print(correct)
def compare():
obstacle, wall_cw, wall_awc = Obstacle(), WallCW(), WallACW()
obstacle_params, wall_cw_params, wall_acw_params = torch.load(
'program_memory/move.pt'), torch.load(
'program_memory/cw.pt'), torch.load('program_memory/acw.pt')
networks = [obstacle, wall_cw, wall_awc]
params = [obstacle_params, wall_cw_params, wall_acw_params]
hnm = HNM(10, 14, networks, params)
hnm.load_state_dict(torch.load('learned_params/hnm.pt'))
ntm = NTM(10, 14)
ntm.load_state_dict(torch.load('learned_params/ntm.pt'))
lstm = LSTM(14, 64, 3, 1)
lstm.load_state_dict(torch.load('learned_params/lstm.pt'))
testX, testY = getTestData()
hnm_correct, ntm_correct, lstm_correct = 0, 0, 0
totSamples = 0
for i in range(0, 25):
s = torch.from_numpy(np.array(testX[i:i + 1][0])).float().unsqueeze(0)
s_lstm = s.view(s.size()[0], s.size()[2], -1)
l = np.array(testY[i:i + 1][0])
print(i)
(hnm_read_weights, hnm_write_weights) = hnm._initialise()
(ntm_read_weights, ntm_write_weights) = ntm._initialise()
lstm_h = lstm.h0.expand(s_lstm.size()[0], 64)
lstm_c = lstm.c0.expand(s_lstm.size()[0], 64)
for j in range(s.size()[1]):
(hnm_out, hnm_read_weights,
hnm_write_weights) = hnm.forward(s[:, j, :], hnm_read_weights,
hnm_write_weights)
(ntm_out, ntm_read_weights,
ntm_write_weights) = ntm.forward(s[:, j, :], ntm_read_weights,
ntm_write_weights)
lstm_h, lstm_c, lstm_out = lstm.forward(s_lstm[:, :, j], lstm_h,
lstm_c)
if np.argmax(hnm_out.detach().numpy()) == np.argmax(l[j]):
hnm_correct += 1
if np.argmax(ntm_out.detach().numpy()) == np.argmax(l[j]):
ntm_correct += 1
if np.argmax(lstm_out.detach().numpy()) == np.argmax(l[j]):
lstm_correct += 1
totSamples += 1
print(hnm_correct, ntm_correct, lstm_correct)
print(totSamples)
def trainHNM():
obstacle, wall_cw, wall_awc = Obstacle(), WallCW(), WallACW()
obstacle_params, wall_cw_params, wall_acw_params = torch.load(
'program_memory/move.pt'), torch.load(
'program_memory/cw.pt'), torch.load('program_memory/acw.pt')
networks = [obstacle, wall_cw, wall_awc]
params = [obstacle_params, wall_cw_params, wall_acw_params]
hnm = HNM(10, 14, networks, params)
X, y = getData()
hnm.train(X, y, 1)
def trainHNM():
obstacle, wall_cw, wall_awc = Obstacle(), WallCW(), WallACW()
obstacle_params, wall_cw_params, wall_acw_params = torch.load('program_memory/move.pt'), torch.load('program_memory/cw.pt'), torch.load('program_memory/acw.pt')
networks = [obstacle, wall_cw, wall_awc]
params = [obstacle_params, wall_cw_params, wall_acw_params]
hnm = HNM(10, 14, networks, params)
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))
hnm.train(X, y, 1)